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Best AI tools to step up content writing in 2023 | Comparison of 5 tools

  • By Gcore
  • April 30, 2023
  • 17 min read
Best AI tools to step up content writing in 2023 | Comparison of 5 tools

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Recently, the rise of artificial intelligence (AI) has brought about many technological advancements, including the development of AI content writing tools. These tools use machine learning algorithms to generate written content, such as articles, product descriptions, and even entire books, reducing the time and effort required for content creation. As you scroll through various articles, it’s possible if not probable that one was written or assisted by an AI program.

However, users may still be searching for which AI content writing tool is the best fit and should be used. To help them out, we tested and compared five popular tools: ChatGPT, QuillBot, Rytr, Copy.ai, and Writesonic. Read this article to see their pros and cons and evaluate which ones best meet the needs for your business or personal use.

Comparison of AI content writing tools

Below, you will find a comprehensive comparison of the top-performing tools and their key features, allowing you to assess which one is best suited to your specific writing needs. So, without further ado, let’s explore the best AI writing tools available today.

1. ChatGPT

ChatGPT is a language model and can be used as a content writing tool for generating text on various topics. It can help with brainstorming ideas, researching, and gathering information, and even writing content itself.

For instance, if you’re tasked with writing an article on a particular subject, such as meal plans for weight loss, you can request ChatGPT to provide you with relevant information and insights. Additionally, you may ask it to create an outline or structure for your article, or even generate a draft that you can review and refine.

Spoiler: Here is what you can expect from ChatGPT.

Key featuresProsConsPricing
• Prompt-based writing and language support 
• Paraphrasing and proofreading 
• Outlining 
• Idea generator 
• Chat history
• Free plan 
• Saves time when creating content 
• Suggests creative ideas 
• Useful for brainstorming ideas for various types of content
• Lacks personal touch 
• May occasionally produce biased content 
• Limited knowledge of world events after 2021 
• May experience downtime when there is high demand
• The free plan includes key features 
• ChatGPT Plus ($20/month) provides faster response speed, priority access to new features, and access during times of high demand

Key features of ChatGPT for content writers

Prompt-based writing and language support. ChatGPT can produce text that sounds like it was written by a human in different languages, such as English, French, German, Spanish, and more. By giving specific prompts, you can control how your text will be written. To give you an idea, here’s a brief example:

Paraphrasing and proofreading. It can paraphrase and proofread existing text, making the content more unique content and engaging. Here’s a quick example:

Outlining. One of the capabilities of ChatGPT is producing outlines and structures for various types of content, which can simplify the process of arranging ideas and producing coherent works. Here’s a quick example where we prompted ChatGPT to create an outline for a blog article.

Idea generator. As a content creator, coming up with a catchy title for your article can be a challenging task. However, by using a prompt to ChatGPT, you can generate ideas to create both catchy and SEO-friendly titles for your content. You can be creative with your prompts to have more interesting results.

Chat history. Please note that these sample prompts are not limited, and you can engage in a conversation with ChatGPT to get more specific ideas. It remembers your previous chats and you can continue to formulate ideas based on its answers. For example, we asked ChatGPT to create a content plan for social media posts on Instagram for selling sneakers.

Next, we asked if it could provide us with a table on how we could schedule Instagram postings.

As a result, it provides us with a schedule for the whole week, including the type of content, captions to use, photos to post, and when to post them. As a content writer and business owner, this could open up opportunities to step up your content creation and gain more followers.

Pros of ChatGPT for content writers

Saves time. Using ChatGPT can quickly generate ideas, titles, and outlines for various types of content, which can help writers or content creators save time during the creation process.

Supports your creativity. Creative prompts allow you to interact with ChatGPT’s creative capacity. ChatGPT can provide you with unexpected ideas and angles, which can then increase productivity and help you come up with unique content.

A free plan is available. One of our favorite pros of this platform is that you don’t need to subscribe to try one of the key features we mentioned. However, there is also a premium subscription called ChatGPT Plus, which costs around $20 per month.

Cons of ChatGPT for content writers

Lack of personal touch. While ChatGPT is a useful tool for generating content quickly, it cannot replace the personal touch and unique voice of a human writer. For instance, when asked to provide a short description of an oven toaster, the resulting text was informative but lacked the nuances and creativity that a human writer could bring to the task.

When writing about a specific topic like an oven toaster, incorporating personal experiences can add depth and authenticity to the content, which is something that may be lacking in AI-generated text. For example, you could write about how people use it or share your favorite pastry recipe that can be baked in the toaster.

Knowledge limitation. It is limited to what it has been trained on, which means that it may not have a deep understanding of specific topics or industries. For example, say we wanted to write a fully in-depth technical article about cloud services. As one of our subject experts said, it can only provide basic information about “the cloud,” and it can’t be more technically accurate once it tries to generate more content.

Let’s take a look at the example below.

We asked ChatGPT to write a brief, one-paragraph answer to the question, “What is cloud storage?”

At first glance, ChatGPT may seem impressive with its answer, but upon closer examination, its response only provides generic information without specifics. According to one of our cloud experts, while it does offer a general idea of cloud storage, it typically describes an internet service used by end-users to store files such as photos and music; it falls short in giving an accurate example.

Now, we was curious and asked ChatGPT about this question to see what the result would be.

We then asked ChatGPT to clarify the accuracy of its responses.

Testing the accuracy of ChatGPT’s responses is crucial because it does not provide sources. When dealing with factual information such as dates, history, science, technology, and medicine, it is advisable to conduct further research and fact-checking before sharing any information.

It may experience downtime when there is high demand. In our experience, if you use ChatGPT during peak working hours (7:00 AM to 1:00 PM), there is a likelihood that you will receive an error message instead of a response to your inquiry.

Pricing

While ChatGPT is a free service, there is a premium version called ChatGPT Plus, which costs $20 per month. This version provides several benefits, including uninterrupted availability even during periods of high demand, quicker response times, and exclusive access to new features.

2. QuillBot

QuillBot is a popular AI tool that specializes in paraphrasing sentences and paragraphs. This tool helps writers and content creators produce better text that aligns with their preferences. QuillBot offers different modes, such as Standard paraphrase, which rephrases sentences by selecting words that can be improved in terms of vocabulary. This feature would be useful for writers who want variations of their text and to improve the message by paraphrasing it.

Spoiler: Here is what you can expect from QuillBot.

Key featuresProsConsPricing
• Paraphrasing 
• Grammar check 
• Summarizing
• Free plan 
• No need to sign up 
• Easy to use 
• The grammar checker tool is free
• Paraphrased text may contain grammatical errors• Key features are available on the free plan 
• The premium plan costs $19.95 per month

Key features

Paraphraser. The free version includes Standard and Fluency modes, while the Premium version offers Formal, Simple, Creative, Expand, and Shorten modes.

Grammar checker. This feature allows you to fix all errors in your text with a single click of a button, and it also allows you to paraphrase and edit the text in their rich text editor.

Summarizer. The Summarizer condenses articles, papers, and other documents into a bulleted Key Sentences list or into a new paragraph.

Pros

Free plan. It already includes 125 words in the Paraphraser. It has Standard and Fluency modes, 3 synonym options, 1 freeze word or phrase and 1200 words in the summarizer.

No need to sign up. You have the option to use their paraphrase tool immediately without having to sign in or create an account.

It’s easy to use. The user interface is straightforward, and anyone can use it, even those who are not tech-savvy. All the features can be easily navigated.

Grammar checker tool is free. You can correct any grammar, spelling, and punctuation errors with a single click of a button by clicking “Fix All Errors.” (Which may be useful given the possible limitations of other features—please see below.)

Cons

Paraphrased text may contain grammatical errors. Here is a sample of text that was paraphrased in QuillBot.

After copying the paraphrased paragraph and checking it in Grammarly, a grammar error was found.

Interestingly, after reviewing it in QuillBot’s own grammar checker, it also detected grammar and punctuation errors.

So be aware of this limitation by using the paraphraser feature in QuillBot.

Pricing

The pricing may vary depending on your location, so best to visit their website for accurate pricing information.

3. Rytr

Rytr is a writing tool driven by AI that enables users to produce high-quality content quickly. It uses sophisticated algorithms and natural language processing to produce content that is accurate and engaging. Let’s take a look at its features.

Spoiler: Here is what you can expect from Rytr.

Key featuresProsConsPricing
• Composing blog ideas and outlines 
• Writing blog sections 
• Generating email content 
• Writing product descriptions 
• Social media updates 
• Chat feature
• Has a library of use cases 
• Generates high-quality text
• Limited word count 
• Not recommended for full-length posts
• A free plan is available 
• Paid plans range from $9 to $29 per month

Key features

Composing blog ideas and outlines. Rytr allows you to generate ideas, structure, and content for articles. You can specify the language type, tone, use case, primary keyword, number of variants, and creativity level. Here’s how this process looks:

Blog section writing. This allows users to write articles based on section topics and headlines. Here’s how we turned the section topic “Weight loss benefits of regular walking” from the previous feature into a fully crafted blog section. First, we identified relevant keywords and added them to the “Section keywords” field (see image below.) Those keywords are inserted seamlessly into each sentence, thus ensuring their natural placement in the content. This will elevate your blog’s SEO articles.

Email. What we like about this feature is that you only need to type a few “Key points” into the corresponding field, and Rytr creates a ready-to-send email. Of course, for best results, you need to provide a maximum input in the “Key points” field. In this example, we asked Rytr to generate an email anticipating a follow-up call about a company’s products and services, including a customer’s phone number.

Product description. This feature allows you to write a brief description for your product or feature. You only need to supply the product name and some basic information in the “About product” field. We tried this out for a Bluetooth speaker product called “XYZ speaker.”

We offered a product description input with limited information, and Rytr was still able to generate satisfactory content. Although it wasn’t an outstanding output text, Rytr elevated our single sentence “About product” field. This feature can serve as a useful starting point for writing product descriptions.

Social media updates. Rytr can take your social media topic ideas and turn them into posts and captions. You can customize the creativity level to your preference.

Chat feature. Rytr’s chat feature also allows you to request content using a conversational interaction. You could request ad copy, generate video ideas, write a blog post, or create a how-to explanation. This feature is incredibly useful and one of our favorites. We asked Rytr to generate a 100-word text about the benefits of walking every day. The resulting text was surprisingly good, and Rytr responded quickly.

You can keep requesting modifications to the content, and the Rytr chat will provide you with the appropriate response. Below, we requested that a numbered list be generated based on its previous response. Once again, we appreciated the prompt response and how it organized the text into a numbered list.

Next, we requested that Rytr chat suggest some SEO-friendly titles based on that topic. The results were excellent, as you can see below.

Our research indicates that Rytr uses GPT-3 AI technology, which functions similarly to ChatGPT. While templates may appear to be the easier option, once you’ve become skilled in creating effective prompts, you’ll achieve the desired outcomes just as efficiently with the chat feature.

Pros

Use cases. Compared to other writing tools, Rytr’s AI writer can generate a wider variety of content with over 30+ use cases available. The tool allows you to customize the output’s tone, creativity level, and language to suit your needs.

Perfect for generating high-quality text. Rytr is highly effective in creating brief posts, especially in producing quality blog sections and outlines. Here is an example below where we choose the use case: Blog Section Writing. We also indicate the section topic, section keywords, number of variants, and creativity level.

Cons

Limited word count. Its free plan has a limited monthly quota, and you need to wait for 30 days before it can be restored. As you can see below, we reached the 10,000-word count limit.

Pricing

The pricing tiers start with a free plan; as you scale, you can upgrade to plans ranging from $9 to $29 per month. The upgraded plans include a dedicated account manager and priority email and chat support from the platform’s team.

4. Copy.ai

Copy.ai is an impressive writing tool that utilizes the power of AI to assist content creators in crafting copy for various purposes, including social media, blogs, and advertising. It also utilizes natural language processing to generate text that closely resembles human writing and provides a broad selection of writing templates. The platform’s user-friendly interface is easy to navigate, and its ability to generate high-quality content quickly has made it a favored tool for both busy professionals and companies. Let’s dive into its features.

Spoiler: Here is what you can expect from Copy.ai.

Key featuresProsConsPricing
• Creates website copy 
• Writes social media updates 
• Blog tools 
• Generates email content 
• Chat feature
• Decent text editor 
• Multiple templates available
• Not suitable for long post content• A free plan is available 
• The pro version is available for $49 per month

Key features

Website copy. This feature offers two modes. The first enables you to design a sales landing page that can help drive traffic to your website and generate leads. The second option lets you utilize a pre-designed template to create an “About Us” section, which will share your brand’s story. Here’s how this looks when we filled in a template for a sales landing page.

Then we filled in the necessary information to generate content.

Once finished, we simply click the “Create Content” button. Copy.ai then works its magic and produces content; simply scroll down the page and copy the generated text that you want to use. In our experience, Copy.ai provided four versions of the text. Moreover, at the bottom of the page, there is an option to click “Make more.”

Social media updates. This feature offers a huge variety of templates, including for discounts or special promotions, social media bios, and “tips” sections. We tested out a few, including one for creating social media posts about discounts.

The results were pretty good.

By being creative with prompts and including additional details, you can achieve improved outcomes. But even with minimal input, the Copy.ai generates good content for social media posts.

Blog tools. Copy.ai offers four different use cases for their blog tools: write a blog introduction, create a blog outline, draft individual blog sections, and a post wizard that is exclusively available in the paid version. The post wizard feature enables you to create an entire first draft of your blog post in just five minutes. We created a blog intro about AI copywriting using Copy.ai.

To create the blog post, we entered the required details such as the title, and topic, and selected a suitable tone from the given options, which typically include friendly, professional, persuasive, and more. You also have the freedom to customize the tone if the available options do not suit your needs. The tool even suggested “Elon Musk” as a tone option, so we decided to experiment with it and observe the results.

Based on our test, the system produced six different versions of blog introductions. If you want to generate more content, you can simply click “Make More.” However, we weren’t convinced that the tone was quite right for Elon Musk. We’ll let you be the judge.

Emails. This feature enables you to generate email content for various purposes. Copy.ai offers ten different use cases, including “Welcome/Confirmation Email,” “Coupons/Discounts,” “Recurring Email Newsletters,” and “Event Promotions.”

We tested the “Welcome/Confirmation Email” feature and used a mechanical keyboard as an example product.

The results were great, with four variations of content generated. Here’s the first result:

Copy.ai creates effective and straightforward welcome email content.

Chat feature. Copy.ai’s chat feature includes a real-time search, the ability to generate long-form content, and facilitates brainstorming. We found the prompt library of prompts (under “Browse Prompts”) was particularly helpful for getting started.

Copy.ai’s prompt library is impressively well organized. In this particular instance, we clicked on the “Content/SEO category,” then selected the “Article Generator” prompt. We were impressed with how easy it was to use the sample prompt—simply insert the topic and tone of the content and the content will be generated.

We utilized the provided prompt and entered the topic “Best gaming laptops” while selecting a friendly tone. We observed an “Improve” button located next to the prompt, so before submitting, we clicked it and the prompt was enhanced to be more specific and detailed.

The prompt was converted into an outline format after selecting the Improve button. Next, we copied and pasted the outline to the text editor in order to display the entire prompt up to the “Sources” section.

Unfortunately, the generated result fell short of our expectations as it didn’t offer any particular laptop models. Some parts of the article failed to persuade us as readers, particularly in the last section on “Diverse Perspectives on Gaming Laptops.” Under that section, the copy doesn’t provide any details about the experts mentioned, and unfortunately is misleading to the reader. We do acknowledge that as an AI language model, Copy.ai may not have access to the most up-to-date information or the capacity to make assessments on these products. Overall, our positive experience creating the prompt wasn’t matched by Copy.ai’s subpar output.

We tested out the chat with different topics, but found that the responses were slow. Nonetheless, we found the sample prompts provided to be useful and could be applied to other AI chat tools like ChatGPT or Rytr.

Pros

It has a decent text editor. While Copy.ai generates content on your behalf, you can easily review and edit the copied content straight to Copy.ai’s text editor. No need to switch windows to edit your content. In addition to that, it is simple to adjust the tone of the content to your liking, whether you prefer a professional tone or a custom-made one.

It has multiple templates available. The platform provides various templates for creating different content types, such as marketing text, social media posts, captions for Instagram, long-form content, and more.

Cons

Not suitable for long post content. Like Rytr, mentioned above, Copy.ai is not suitable for creating lengthy content, as it tends to include redundant information that may not make sense to readers. Alternatively, it may repeat the same content using rephrased wording. Here’s an example of me telling the tool we wanted to write an article about using AI for business content:

When we click on “Create content,” we are only provided with concise descriptions that we can easily copy and paste into my text editor. However, clicking on “more like this” only shows us the same information as the initial description, with no further options available.

Pricing

A free plan, then you can upgrade to Pro, which is available for $49 per month.

5. Writesonic

Writesonic allows writers to create content that is both SEO-friendly and free from plagiarism for various purposes, such as blogs, advertisements, emails, and websites, in a faster and more efficient manner.

Spoiler: Here is what you can expect from Writesonic.

Key featuresProsConsPricing
• Creating long-form blogs and articles 
• Paraphrasing 
• Extending or shortening text 
• Converting passive voice to active voice 
• Chatsonic
• Cost effective 
• Lots of exciting feature 
• Good help resources
• Generated content can sound robotic 
• Limited word count
• Free trial with a 10,000-word limit 
• Long term plan offered at $12.69 per month

Key features

Create long-form blogs and articles. One of our favorite features of this tool is its ability to quickly generate full-length articles with just a few clicks. Within the “Article and Blogs” tab of the control panel, users have access to 14 different use cases. Examples include AI-generated blog titles, paragraph writing, and the AI article writer 4.0, which can produce SEO-friendly articles of up to 3000 words. Additional helpful tools are offered, such as content rephrasing.

We clicked on the AI Article Writer 4.0 option, which allows you to input your topic and search for relevant keywords. For this instance, we used “Benefits of CDN in websites” as our topic and clicked on the “Search keywords” button. The tool then generated a list of keywords to incorporate into the article.

Moving on, we created a title for our article. We used the previously generated keywords and opted for an engaging tone of voice, a first-person point of view, and a premium quality type.

After choosing the title, the next step is to craft an introduction for the blog. We simply clicked on “Generate ideas,” but instead of the default three variants, we adjusted it to two to conserve word count since the tool has a limit of 10,000 words. We chose the second variant.

After we chose this intro, it was automatically copied and pasted into the “Article Intro” field. We were then able to customize the tone of voice, point of view, and even add a CTA (call to action.) From the two outlines generated, we chose the first one.

Writesonic then generated the article from the outline we selected. It produced a 1435-word article and even included a cover image, but the problem is that the image doesn’t match the topic of CDNs.

We copied the entire text and tested it in the Grammarly plagiarism checker to identify any similarities. The checker detected at least 21%, but most of the matches were individual sentences rather than entire paragraphs. Here is a snapshot of the results.

The sentence structure in general is good, particularly for simple explanations. However, this tool is only appropriate for straightforward content. When it comes to writing a highly technical article, it’s important to ensure accuracy by fact-checking it with a professional. Targeted keywords do put generated content at risk of similarity to other online sources. It is important to keep in mind that when using this tool, you should check punctuation, grammar, and run the content through a plagiarism checker. If you plan to publish the blog articles online, you may need to rephrase some of the sentences. In addition, please keep in mind these tips apply to all AI content tools; we just happened to check them with Writesonic since it provided the closest output to a finished blog post.

General writing. We appreciated the extra tools offered by Writesonic, which include the ability to paraphrase, extend or shorten text, convert passive voice to active voice, and provide pros and cons. Here’s an example of a content that was generated earlier for a blog section. We selected the paraphraser to make it more engaging.

As you can see from the result, the tool rephrase the content and gave it a more personalized and engaging tone.

Chatsonic. Chatsonic presents itself as an alternative to ChatGPT that can overcome ChatGPT’s limitations. One of the advantages of Chatsonic is its ability to connect directly to Google. We put this claim to the test by asking for a list of the “Best Gaming Laptops in 2023.” However, we discovered that using Google data comes at an additional cost per word, but can be disabled if desired. We gave the Google data option a try.

As seen in the above image, Chatsonic cited its Google references. Next, we tested turning Google data off. Here are the results:

As seen above, the results changed when we turned off the Google data, and it displayed the 2022 information. Now, let’s ask ChatGPT the same prompt.

So, as we expected, since ChatGPT isn’t directly connected to Google data it can’t provide the latest information. Going back to Chatsonic, we left Google data turned off for the upcoming prompts since we didn’t require the most recent data. We then asked it to generate a caption for a Facebook post promoting a giveaway.

The generated content is great, and it included some emojis and hashtags that are essential for effective social media posts. In another test, we requested Chatsonic to create a 100-word introduction for a YouTube channel. The platform offers an option to choose the type of personality to be used. We selected General AI and Stand-up Comedian, but we didn’t notice any significant change in the text or tone of the content.

In general, we didn’t find Chatsonic to be a great alternative to ChatGPT because of Chatsonic’s pricing. The only advantage of Chatsonic is its ability to connect directly to Google data—an interesting offering, but not enough to make it a serious contender to ChatGPT.

Pros

Cost-effective and has a lot of exciting features. This tool not only has a free plan available, it also offers a range of writing templates and features that utilize AI technology. Here’s how it looks like with their user interface showing different features, from articles and blogs to e-commerce and social media.

Good resources for help provided. If you need assistance on how to use it, their tutorials and documentation guides are well done and you can easily follow each instruction without any issues.

Cons

Generated content can sound robotic. Although the content generated by Writesonic can be of high quality, there are occasions where it may lack a natural and organic feel. As an example, we used it to generate a text on the topic “AI is the future of copywriting,” and upon reading it, we noticed that it had a generic and monotonous tone. The predictable sentence structure consistently started each sentence with the main headline, followed by examples, which may be noticeable to the reader.

Limited word count. Once you hit the free plan limit of 10,000 words, you’ll need to wait 30 days for a reset. We were not fond of the fact that Chatsonic’s chat feature counts towards your free account’s word count limit. In contrast, other platforms do not have this limitation, and their word count limit is only applicable to their use case templates.

Pricing

Starts with a free trial of a 10,000-word count. The long-form plan starts at $12.69/month.

Results of the AI Tool Comparison

After reviewing the top 5 content writing tools, let’s take a look at the table below for a quick comparison.

ToolBest forFree trialPricing starts atSupported languages
ChatGPTCreating long posts, generating ideas, editing and paraphrasing textYes, no word count limitThe basic plan is free. ChatGPT Plus costs $20/month95+ languages
QuillBotParaphraser, summarizer, and grammar checker of any form of contentYes, it can paraphrase 150 words at once and summarize 1200 words at onceThe basic plan is free. $19.95 billed monthlyTranslates over 30+ languages But only paraphrased English language
Copy.aiBlog posts, product descriptions, ad copy, social media postsYes (2,000 words per month)$36/month 
Billed $432/year
29+ languages
RytrShort-form content, ad copy, social media postsYes (5,000 words per month)$9/month 
$90/year
30+ languages
WritesonicBlog posts, ad copy, social media postsYes (up to 10,000 words per month)Starts at $12.69/month24 languages

Please be advised that prices are subject to change. For the most current information, always refer to the company website.

Conclusion

AI-powered content writing tools offer a range of benefits, including faster and more efficient content creation. However, it’s crucial to choose a tool that suits your specific business needs.

We hope that our list has provided you with valuable insights into some of the top AI content writing tools available. Also, it’s important to exercise caution when using these tools, as they can be prone to errors and may produce redundant or even incorrect information. Nevertheless, these limitations should not deter us from using them. Instead, we should use AI writing tools thoughtfully and collaboratively to leverage the power of AI and enhance our content creation efforts to achieve our goals.

As AI technology continues to evolve, we can expect to see even more advancements in this area. This offers new opportunities to optimize digital marketing efforts and create high-quality content faster and more efficiently.

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Inference is becoming Europe’s core AI workload. Telcos are moving fast on low-latency infrastructure. Data sovereignty is shaping every deployment decision.At GTC Europe, these trends were impossible to miss. The conversation has moved beyond experimentation to execution, with exciting, distinctly European priorities shaping conversations.Gcore’s own Seva Vayner, Product Director of Edge Cloud and AI, shared his take on this year’s event during GTC. He sees a clear shift in what European enterprises are asking for and what the ecosystem is ready to deliver.Scroll on to watch the interview and see where AI in Europe is heading.“It’s really a pleasure to see GTC in Europe”After years of global AI strategy being shaped primarily by the US and China, Europe is carving its own path. Seva notes that this year’s GTC Europe wasn’t just a regional spin-off. it marked the emergence of a distinctly European voice in AI development.“First of all, it's really a pleasure to see that GTC in Europe happened, and that a lot of European companies came together to have the conversation and build the ecosystem.”As Seva notes, the real excitement came from watching European players collaborate. The focus was less on following global trends and more on co-creating the region’s own AI trajectory.“Inference workloads will grow significantly in Europe”Inference was a throughline across nearly every session. As Seva points out, Europe is still at the early stages of adopting inference at scale, but the shift is happening fast.“Europe is only just starting its journey into inference, but we already see the trend. Over the next 5 to 10 years, inference workloads will grow significantly. That’s why GTC Europe is becoming a permanent, yearly event.”This growth won’t just be driven by startups. Enterprises, governments, and infrastructure providers are all waking up to the importance of real-time, regional inference capabilities.“There’s real traction. Companies are more and more interested in how to deliver low-latency inference. In a few years, this will be one of the most crucial workloads for any GPU cloud in Europe.”“Telcos are getting serious about AI”One of the clearest signs of maturity at GTC Europe was that telcos and CSPs are actively looking to deploy AI. And they’re asking the hard questions about how to integrate it into their infrastructure at a vast scale.“One of the most interesting things is how telcos are thinking about adopting AI workloads on their infrastructure to deliver low latency. Sovereignty is crucial, especially for customers looking to serve training or inference workloads inside their region. And also user experience: how can I get GPU capacity in clusters, or deliver inference in just a few clicks?”This theme—fast, sovereign, self-service AI—popped up again and again. Telcos and service providers want frictionless deployment and local control.“Companies are struggling most with data”While model deployment and infrastructure strategy took center stage, Seva reminds us that data processing and storage remains the bottleneck. Enterprises know they need to adopt AI, but they’re still navigating where and how to store and process the data that fuels it.“One of the biggest struggles for end customers is the data: where it’s processed, where it’s stored, and what kind of capabilities are available. From a European perspective, we already see more and more companies looking for sovereign data privacy and simple, mature solutions for end users.”That’s a familiar challenge for enterprises operating under GDPR, NIS2, and other compliance frameworks. The new wave of AI infrastructure has to be built for performance and for trust.AI in Europe: responsible, scalable, and localSeva’s key takeaway is that AI in Europe is no longer about catching up, it’s about doing it differently. The questions have changed from “Should we do AI?” to “How do we scale it responsibly, reliably, and locally?”From sovereign deployment to edge-first infrastructure, GTC Europe 2025 showed that inference is the foundation of how European businesses plan to run AI. “The ecosystem is coming together,” explains Seva. “And the next five years will be crucial for defining how AI will work: not just in the cloud, but everywhere.”If you’re looking to reduce latency, cut costs, and stay compliant while deploying AI in production, we invite you to download our free ebook, The inference optimization playbook.Download our free inference optimization playbook

Gcore and Orange Business launch innovation program piloting joint solution to deliver sovereign inference as a service

Gcore and Orange Business have kicked off a strategic co-innovation program with the mission to deliver a scalable, production-grade AI inference service that is sovereign by design. By combining Orange Business’ secure, trusted cloud infrastructure and Gcore’s AI inference private deployment service, the collaboration empowers European enterprises and public sector organizations to run inference workloads at scale, without compromising on latency, control, or compliance.Gcore’s AI inference private deployment service is already live on Orange Business’ Cloud Avenue infrastructure. Selected enterprises across industries are actively testing it in real-world scenarios. These pilot customers are exploring how fast, secure, and compliant inference can accelerate their AI projects, cut deployment times, and reduce infrastructure overhead.The prototype will be demonstrated at NVIDIA GTC Paris, at the Taiga Cloud booth G26. Stop by any time to see it in action.The inference supercycle is underwayBy 2030, inference will comprise 70% of enterprise AI workloads. Telcos are well positioned to lead this shift due to their dense edge presence, licensed national data infrastructure, and long-standing trust relationships.Gcore’s inference solution provides a sovereign, edge-native inference layer. It enables users to serve real-time, GPU-intensive applications like agentic AI, trusted LLMs, computer vision, and predictive analytics, all while staying compliant with Europe’s evolving data and AI governance frameworks.From complexity to three clicksEnterprise AI doesn’t need to be hard. Deploying inference workloads at scale used to demand Kubernetes fluency, large MLOps teams, and costly trial-and-error.Now? It’s just three clicks:Pick a model: Choose from NVIDIA NIMs, open source, or proprietary libraries.Choose a region: Select one of Orange Business’ accredited EU data centers.Deploy: See your workloads go live in under 10 seconds.Enterprises can launch inference projects faster, test ideas more quickly, and deliver production-ready AI services without spending months on ML plumbing.Explore our blog to watch a demo showing how enterprises can deploy inference workloads in just three clicks and ten seconds.Sovereign by designAll model data, logs, and inference results are stored exclusively within Orange Business’ own data centers in France, Germany, Norway, and Sweden. Cross-border data transfer is opt-in only, helping ensure alignment with GDPR, sector-specific regulations, and the forthcoming EU AI Act.This platform is built for trust, transparency, and sovereignty by default. Customers maintain full control over their data, with governance baked into every layer of the deployment.Performance without trade-offsGcore’s AI inference solution avoids the latency spikes, cold starts, and resource waste common in traditional cloud AI setups. Key design features include:Smart GPU routing: Directs each request to the nearest in-region GPU, delivering real-time performance with sub-50ms latency.Pre-loaded models: Reduces cold start delays and improves response times.Secure multi-tenancy: Isolates customer data while maximizing infrastructure efficiency.The result is a production-ready inference platform optimized for both performance and compliance.Powering the future of AI infrastructureThis partnership marks a step forward for Europe’s sovereign AI capabilities. It highlights how telcos can serve as the backbone of next-generation AI infrastructure, hosting, scaling, and securing workloads at the edge.With hundreds of edge POPs, trusted national networks, and deep ties across vertical industries, Orange Business is uniquely positioned to support a broad range of use cases, including real-time customer service AI, fraud detection, healthcare diagnostics, logistics automation, and public sector digital services.What’s next: validating real-world performanceThis phase of the Gcore and Orange Business program is focused on validating the solution through live customer deployments and performance benchmarks. Orange Business will gather feedback from early access customers to shape its future sovereign inference service offering. These insights will drive refinements and shape the roadmap ahead of a full commercial launch planned for later this year.Gcore and Orange Business are committed to delivering a sovereign inference service that meets Europe’s highest standards for speed, simplicity, and trust. This co-innovation program lays the foundation for that future.Ready to discover how Gcore and Orange Business can deliver sovereign inference as a service for your business?Request a preview

Why on-premises AI is making a comeback

In recent years, cloud AI infrastructure has soared in popularity. With its scalability and ease of deployment, it’s no surprise that organizations rushed to transfer their data to the cloud in a bid to become “cloud-first.”But now, the tide is turning.As AI workloads grow more complex and regulatory pressures increase, many companies are reconsidering their reliance on cloud and turning back toward on-premises AI infrastructure.Rather than doubling down on the cloud, organizations are diversifying—adopting multi-cloud models, sovereign cloud environments, and even hybrid or fully on-prem setups. The era of a single cloud provider handling everything is coming to an end. Why? Control, security, and performance are hard to find in the public cloud.Here’s why more businesses are bringing AI back in-house.#1 Enhanced data security and controlData security remains one of the most urgent concerns driving the return to on-prem infrastructure.For sensitive or high-priority workloads—common in sectors like finance, healthcare, and government—keeping data off the cloud is often non-negotiable. Cloud computing inherently increases risk by exposing data to shared environments, wider attack surfaces, and complex supply chains.Choosing a trusted cloud provider can mitigate some of those risks. But it can’t replace the peace of mind that comes from keeping sensitive data in-house.With on-premises AI, organizations gain fine-grained access control. Encryption keys remain internal and breach exposure shrinks dramatically. It’s also much easier to stay compliant with privacy laws when data never leaves your own secure perimeter.For industries where trust and confidentiality are everything, on-prem solutions offer full visibility into where and how data is stored and processed.#2 Performance enhancement and latency reductionLatency matters—especially in AI.On-premises AI systems excel in environments that require real-time performance and heavy compute loads. Processing data locally avoids the physical delays caused by transferring it across the internet to a cloud data center.By eliminating long-haul network hops, companies get near-instant access to computing resources. They also get to fine-tune their internal networks—using private fiber, low-hop switching, and other low-latency optimizations that cloud customers can’t control.Unlike multi-tenant cloud platforms, on-prem resources aren’t shared. That means consistently low, predictable latency.This is vital for use cases where milliseconds—or even microseconds—make a difference: autonomous vehicles, real-time analytics, robotic control systems, and high-speed trading. Fast feedback loops and localized processing enable better outcomes, tighter control, and faster decision-making at the edge.#3 Regulatory compliance and data sovereigntyAround the world, data privacy regulations are tightening. For most organizations, compliance isn’t optional.On-premises infrastructure helps keep data safely inside the organization’s network. This supports data sovereignty, ensuring that sensitive information remains subject only to local laws—not the policies of another country’s cloud provider.It's also a powerful hedge against geopolitical instability.While hyperscalers operate globally, they’re always headquartered somewhere. That makes their infrastructure vulnerable to political shifts, sanctions, or changes in international data law. Governments may require them to restrict access, share data, or cut off services entirely—especially to organizations in sanctioned or adversarial jurisdictions.Businesses relying on these providers risk disruption when regulations change. On-premises infrastructure, by contrast, offers reliable continuity and greater control—especially in uncertain times.#4 Cost control and operational benefitsCloud pricing may look flexible, but costs can escalate quickly.Data transfers, storage, and compute spikes all add up—fast. In contrast, on-premises infrastructure provides a predictable Total Cost of Ownership (TCO). Although upfront CapEx is higher, OpEx remains more stable over time.Organizations can invest in high-performance hardware tailored to their specific needs and amortize those costs across years. That means no surprise bills, no sudden price hikes, and no dependence on vendor pricing models.Of course, running on-prem infrastructure comes with its own challenges. It demands specialized teams for deployment, maintenance, and support. These experts are costly to recruit and retain—but they’re critical to ensure uptime, security, and performance.Still, for companies with relatively stable compute and storage needs, the long-term savings often outweigh the initial setup effort. On-prem also integrates more smoothly into existing IT workflows, without the need for internet access or additional network setup—another operational bonus.#5 Proactive threat detection and automated responsesOn-premises AI sometimes enables smarter, more customized security.Advanced platforms can continuously analyze live data streams using machine learning to detect anomalies and predict threats. When something suspicious is flagged, the system can respond instantly by quarantining data, blocking traffic, and alerting security teams.That kind of automation is essential for minimizing damage and downtime.With full infrastructure control, organizations can deploy bespoke monitoring systems that align with their threat models. Deep packet inspection, real-time anomaly detection, and behavioral analytics can be easier to configure and maintain on-prem than in shared cloud environments.These systems can also work seamlessly with WAAP and DDoS tools to detect and neutralize threats before they spread. The key is flexibility: whether on-prem or cloud-based, AI-driven security should adapt to your architecture and threat landscape, not the other way around.End-to-end visibility can give security teams a clearer picture and faster response options than generic, one-size-fits-all public cloud security tools.How to combine eon-premises control with cloud scalabilityLet’s be clear: on-premises AI isn’t perfect. It demands upfront investment. It requires skilled personnel to deploy and manage systems. And integrating AI into legacy environments takes thoughtful planning.But today’s tools are helping bridge those gaps. Modern platforms reduce the need for constant manual intervention. They support real-time updates to threat models and detection logic. As a result, security teams can spend more time on strategy and less on maintenance.Meanwhile, the cloud still plays an important role. It offers faster access to new tools, software updates, and next-gen GPU hardware.That’s why many organizations are opting for a hybrid model.Our recommendation: Keep your sensitive, high-priority workloads on-prem. Use the cloud for elastic scale and innovation. Together, they deliver the best of both worlds: performance, control, compliance, and flexibility.Secure your digital infrastructure with Gcore on-premises AI inferenceWhether you’re protecting sensitive data or running high-demand workloads, on-premises AI gives you the control and confidence you need. Securing sensitive data and managing high-demand workloads requires a level of control, performance, and predictability that only on-premises AI infrastructure delivers.Gcore Everywhere Inference Private Deployment makes it easier than ever to bring powerful serverless AI inference capabilities directly into your physical environment. Designed for scalable global performance, Everywhere Inference enables robust and secure multi-tenant AI inference deployments across on-prem and cloud environments, helping you meet data sovereignty requirements, reduce latency, and streamline deployment.Talk to us about your on-prem AI plans

3 clicks, 10 seconds: what real serverless AI inference should look like

Deploying a trained AI model could be the easiest part of the AI lifecycle. After the heavy lifting of data collection, training, and optimization, pushing a model into production is where “the rubber hits the road”, meaning the business expects to see the benefits of invested time and resources. In reality, many AI projects fail in production because of poor performance stemming from suboptimal infrastructure conditions.There are, broadly speaking, two paths developers can take when deploying inference: DIY, which is time and resource-consuming and requires domain expertise from several teams within the business, or the ever-so-popular “serverless inference” solution. The latter is supposed to simplify the task at hand and deliver productivity, cutting down effort to seconds, not hours. Yet most platforms offering “serverless” AI inference still feel anything but effortless. They require containers, configs, and custom scripts. They bury users in infrastructure decisions. And they often assume your data scientists are also DevOps engineers. It’s a far cry from what serverless was meant to be.At Gcore, we believe real serverless inference means this: three clicks and ten seconds to deploy a model. That’s not a tagline—it’s the experience we built. And it’s what infrastructure leaders like Mirantis are now enabling for enterprises through partnerships with Gcore.Why deployment UX matters more than you thinkServerless inference isn’t just a backend architecture choice. It’s a business enabler, a go-to-market accelerator, an ROI optimizer, a technology democratizer—or, if poorly executed, a blocker.The reality is that inference workloads are a key point of interface between your AI product or service and the customer. If deployment is clunky, you’re struggling to keep up with demand. If provisioning takes too long, latency spikes, performance is inconsistent, and ultimately your service doesn’t scale. And if the user experience is unclear or inconsistent, customers end up frustrated—or worse, they churn.Developers and data scientists don’t want to manage infrastructure. They want to bring a model and get results without becoming cloud operators in the process.Dom Wilde, SVP Marketing, MirantisThat’s why deployment UX is no longer a nice-to-have. It’s the core of your product.The benchmark: 3 clicks, 10 secondsWe built Gcore Everywhere Inference to remove every unnecessary step between uploading a model and running it in production. That includes GPU provisioning, routing, scaling, isolation, and endpoint generation, all handled behind the scenes.The result is what we believe should be the default:Upload a modelConfirm deployment parametersClick deployAnd within ten seconds, you’re serving live inference.For platform teams supporting AI workloads, this isn’t just a better workflow. It’s a transformation.With Gcore, our customers can deliver not just self-service infrastructure but also inference as a product. End users can deploy models in seconds, and customers don’t have to micromanage the backend to support that.Dom Wilde, MirantisSimple frontend, powerful backendIt’s worth saying: simplifying the frontend doesn’t mean weakening the backend. Gcore’s platform is built for scale and performance, offering the following:Multi-tenant GPU isolationSmart routing based on location and loadAuto-scaling based on demandA unified API and UI for both automation and accessibilityWhat makes this meaningful isn’t just the tech, it’s the way it vanishes behind the scenes. With Gcore, Mirantis customers can deliver low-latency inference, maximize GPU efficiency, and meet data privacy requirements without touching low-level infrastructure.Many enterprises and cloud customers worry about underutilized GPUs. Now, every cycle is optimized. The platform handles the complexity so our customers can focus on building value.Dom Wilde, MirantisIf it’s not 3 clicks and 10 seconds, it’s not really serverlessThere’s a growing gap between what serverless inference promises and what most platforms deliver. Many cloud providers are focused on raw compute or orchestration, but overlook the deployment layer. That’s a mistake. Because when it comes to customer experience, ease of deployment is the product.Mirantis saw that early on and partnered with Gcore to bring inference-as-a-service to CSP and enterprise customers, fast. Now, customers can launch new offerings more quickly, reduce operational overhead, and improve the user experience with a simple, elegant deployment path.Redefine serverless AI with GcoreIf it takes a config file, a container, and a support ticket to deploy a model, it’s not serverless—it’s server-less-ish. With Gcore Everywhere Inference, we’ve set a new benchmark: three clicks and ten seconds to deploy AI. And, our model catalog offers a variety of popular models so you can get started right away.Whether you’re frustrated with slow, inefficient model deployments or looking for the most effective way to start using AI for your company, you need Gcore Everywhere Inference. Give our experts a call to discover how we can simplify your AI so you can focus on scaling and business logic.Let’s talk about your AI project

Run AI inference faster, smarter, and at scale

Training your AI models is only the beginning. The real challenge lies in running them efficiently, securely, and at scale. AI and reality meet in inference—the continuous process of generating predictions in real time. It is the driving force behind virtual assistants, fraud detection, product recommendations, and everything in between. Unlike training, inference doesn’t happen once; it runs continuously. This means that inference is your operational engine rather than just technical infrastructure. And if you don’t manage it well, you’re looking at skyrocketing costs, compliance risks, and frustrating performance bottlenecks. That’s why it’s critical to rethink where and how inference runs in your infrastructure.The hidden cost of AI inferenceWhile training large models often dominates the AI conversation, it’s inference that carries the greatest operational burden. As more models move into production, teams are discovering that traditional, centralized infrastructure isn’t built to support inference at scale.This is particularly evident when:Real-time performance is critical to user experienceRegulatory frameworks require region-specific data processingCompute demand fluctuates unpredictably across time zones and applicationsIf you don’t have a clear plan to manage inference, the performance and impact of your AI initiatives could be undermined. You risk increasing cloud costs, adding latency, and falling out of compliance.The solution: optimize where and how you run inferenceOptimizing AI inference isn’t just about adding more infrastructure—it’s about running models smarter and more strategically. In our new white paper, “How to Optimize AI Inference for Cost, Speed, and Compliance”, we break it down into three key decisions:1. Choose the right stage of the AI lifecycleNot every workload needs a massive training run. Inference is where value is delivered, so focus your resources on where they matter most. Learn when to use pretrained models, when to fine-tune, and when simple inference will do the job.2. Decide where your inference should runFrom the public cloud to on-prem and edge locations, where your model runs, impacts everything from latency to compliance. We show why edge inference is critical for regulated, real-time use cases—and how to deploy it efficiently.3. Match your model and infrastructure to the taskBigger models aren’t always better. We cover how to choose the right model size and infrastructure setup to reduce costs, maintain performance, and meet privacy and security requirements.Who should read itIf you’re responsible for turning AI from proof of concept into production, this guide is for you.Inference is where your choices immediately impact performance, cost, and customer experience, whether you’re managing infrastructure, developing models, or building AI-powered solutions. This white paper will help you cut through complexity and focus on what matters most: running smarter, faster, and more scalable inference.It’s especially relevant if you’re:A machine learning engineer or AI architect deploying models across environmentsA product manager introducing real-time AI featuresA technical leader or decision-maker managing compute, cloud spend, or complianceOr simply trying to scale AI without sacrificing controlIf inference is the next big challenge on your roadmap, this white paper is where to start.Scale AI inference seamlessly with GcoreEfficient, scalable inference is critical to making AI work in production. Whether you’re optimizing for performance, cost, or compliance, you need infrastructure that adapts to real-world demand. Gcore Everywhere Inference brings your models closer to users and data sources—reducing latency, minimizing costs, and supporting region-specific deployments.Our latest white paper, “How to optimize AI inference for cost, speed, and compliance”, breaks down the strategies and technologies that make this possible. From smart model selection to edge deployment and dynamic scaling, you’ll learn how to build an inference pipeline that delivers at scale.Ready to make AI inference faster, smarter, and easier to manage?Download the white paper

Securing vibe coding: balancing speed with cybersecurity

Vibe coding has emerged as a cultural phenomenon in 2025 software development. It’s a style defined by coding on instinct and moving fast, often with the help of AI, rather than following rigid plans. It lets developers skip exhaustive design phases and dive straight into building, writing code (or prompting an AI to write it) in a rapid, conversational loop. It has caught on fast and boasts a dedicated following of developers hosting vibe coding game jams.So why all the buzz? For one, vibe coding delivers speed and spontaneity. Enthusiasts say it frees them to prototype at the speed of thought, without overthinking architecture. A working feature can be blinked into existence after a few AI-assisted prompts, which is intoxicating for startups chasing product-market fit. But as with any trend that favors speed over process, there’s a flip side.This article explores the benefits of vibe coding and the cybersecurity risks it introduces, examines real incidents where "just ship it" coding backfired, and outlines how security leaders can keep up without slowing innovation.The upside: innovation at breakneck speedVibe coding addresses real development needs and has major benefits:Allows lightning-fast prototyping with AI assistance. Speed is a major advantage, especially for startups, and allows faster validation of ideas and product-market fit.Prioritizes creativity over perfection, rewarding flow and iteration over perfection.Lowers barriers to entry for non-experts. AI tooling lowers the skill floor, letting more people code.Produces real success stories, like a game built via vibe coding hitting $1M ARR in 17 days.Vibe coding aligns well with lean, agile, and continuous delivery environments by removing overhead and empowering rapid iteration.When speed bites backVibe coding isn’t inherently insecure, but the culture of speed it promotes can lead to critical oversights, especially when paired with AI tooling and lax process discipline. The following real-world incidents aren’t all examples of vibe coding per se, but they illustrate the kinds of risks that arise when developers prioritize velocity over security, skip reviews, or lean too heavily on AI without safeguards. These three cases show how fast-moving or under-documented development practices can open serious vulnerabilities.xAI API key leak (2025)A developer at Elon Musk’s AI company, xAI, accidentally committed internal API keys to a public GitHub repo. These keys provided access to proprietary LLMs trained on Tesla and SpaceX data. The leak went undetected for two months, exposing critical intellectual property until a researcher reported it. The error likely stemmed from fast-moving development where secrets were hardcoded for convenience.Malicious NPM packages (2024)In January 2024, attackers uploaded npm packages like warbeast2000 and kodiak2k, which exfiltrated SSH keys from developer machines. These were downloaded over 1,600 times before detection. Developers, trusting AI suggestions or searching hastily for functionality, unknowingly included these malicious libraries.OpenAI API key abuse via Replit (2024)Hackers scraped thousands of OpenAI API keys from public Replit projects, which developers had left in plaintext. These keys were abused to access GPT-4 for free, racking up massive bills for unsuspecting users. This incident shows how projects with weak secret hygiene, which is a risk of vibe coding, become easy targets.Securing the vibe: smart risk mitigationCybersecurity teams can enable innovation without compromising safety by following a few simple cybersecurity best practices. While these don’t offer 100% security, they do mitigate many of the major vulnerabilities of vibe coding.Integrate scanning tools: Use SAST, SCA, and secret scanners in CI/CD. Supplement with AI-based code analyzers to assess LLM-generated code.Shift security left: Embed secure-by-default templates and dev-friendly checklists. Make secure SDKs and CLI wrappers easily available.Use guardrails, not gates: Enable runtime protections like WAF, bot filtering, DDoS defense, and rate limiting. Leverage progressive delivery to limit blast radius.Educate, don’t block: Provide lightweight, modular security learning paths for developers. Encourage experimentation in secure sandboxes with audit trails.Consult security experts: Consider outsourcing your cybersecurity to an expert like Gcore to keep your app or AI safe.Secure innovation sustainably with GcoreVibe coding is here to stay, and for good reason. It unlocks creativity and accelerates delivery. But it also invites mistakes that attackers can exploit. Rather than fight the vibe, cybersecurity leaders must adapt: automating protections, partnering with devs, and building a culture where shipping fast doesn't mean shipping insecure.Want to secure your edge-built AI or fast-moving app infrastructure? Gcore’s Edge Security platform offers robust, low-latency protection with next-gen WAAP and DDoS mitigation to help you innovate confidently, even at speed. As AI and security experts, we understand the risks and rewards of vibe coding, and we’re ideally positioned to help you secure your workloads without slowing down development.Into vibe coding? Talk to us about how to keep it secure.

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