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

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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By keeping inference closer to users and securing every stage of the AI pipeline, Gcore helps businesses protect data, optimize performance, and meet industry regulations.Explore Gcore AI Inference

Mobile World Congress 2025: the year of AI

As Mobile World Congress wrapped up for another year, it was apparent that only one topic was on everyone’s minds: artificial intelligence.Major players—such as Google, Ericsson, and Deutsche Telekom—showcased the various ways in which they’re piloting AI applications—from operations to infrastructure management and customer interactions. It’s clear there is a great desire to see AI move from the research lab into the real world, where it can make a real difference to people’s everyday lives. The days of more theoretical projects and gimmicky robots seem to be behind us: this year, it was all about real-world applications.MWC has long been an event for telecommunications companies to launch their latest innovations, and this year was no different. Telco companies demonstrated how AI is now essential in managing network performance, reducing operational downtime, and driving significant cost savings. The industry consensus is that AI is no longer experimental but a critical component of modern telecommunications. While many of the applications showcased were early-stage pilots and stakeholders are still figuring out what wide-scale, real-time AI means in practice, the ambition to innovate and move forward on adoption is clear.Here are three of the most exciting AI developments that caught our eye in Barcelona:Conversational AIChatbots were probably the key telco application showcased across MWC, with applications ranging from contact centers, in-field repairs, personal assistants transcribing calls, booking taxis and making restaurant reservations, to emergency responders using intelligent assistants to manage critical incidents. The easy-to-use, conversational nature of chatbots makes them an attractive means to deploy AI across functions, as it doesn’t require users to have any prior hands-on machine learning expertise.AI for first respondersEmergency responders often rely on telco partners to access novel, technology-enabled solutions to address their challenges. One such example is the collaboration between telcos and large language model (LLM) companies to deliver emergency-response chatbots. These tailored chatbots integrate various decision-making models, enabling them to quickly parse vast data streams and suggest actionable steps for human operators in real time.This collaboration not only speeds up response times during critical situations but also enhances the overall effectiveness of emergency services, ensuring that support reaches those in need faster.Another interesting example in this field was the Deutsche Telekom drone with an integrated LTE base station, which can be deployed in emergencies to deliver temporary coverage to an affected area or extend the service footprint during sports events and festivals, for example.Enhancing Radio Access Networks (RAN)Telecommunication companies are increasingly turning to advanced applications to manage the growing complexity of their networks and provide high-quality, uninterrupted service for their customers.By leveraging artificial intelligence, these applications can proactively monitor network performance, detect anomalies in real time, and automatically implement corrective measures. This not only enhances network reliability but reduces operational costs and minimizes downtime, paving the way for more efficient, agile, and customer-focused network management.One notable example was the Deutsche Telekom and Google Cloud collaboration: RAN Guardian. Built using Gemini 2.0, this agent analyzes network behavior, identifies performance issues, and takes corrective measures to boost reliability, lower operational costs, and improve customer experience.As telecom networks become more complex, conventional rule-based automation struggles to handle real-time challenges. In contrast, agentic AI employs large language models (LLMs) and sophisticated reasoning frameworks to create intelligent systems capable of independent thought, action, and learning.What’s next in the world of AI?The innovation on show at MWC 2025 confirms that AI is rapidly transitioning from a research topic to a fundamental component of telecom and enterprise operations.  Wide-scale AI adoption is, however, a balancing act between cost, benefit, and risk management.Telcos are global by design, operating in multiple regions with varying business needs and local regulations. Ensuring service continuity and a good return on investment from AI-driven applications while carefully navigating regional laws around data privacy and security is no mean feat.If you want to learn more about incorporating AI into your business operations, we can help.Gcore Everywhere Inference significantly simplifies large-scale AI deployments by providing a simple-to-use serverless inference tool that abstracts the complexity of AI hardware and allows users to deploy and manage AI inference globally with just a few clicks. It enables fully automated, auto-scalable deployment of inference workloads across multiple geographic locations, making it easier to handle fluctuating requirements, thus simplifying deployment and maintenance.Learn more about Gcore Everywhere Inference

Everywhere Inference updates: new AI models and enhanced product documentation

This month, we’re rolling out new features and updates to enhance AI model accessibility, performance, and cost-efficiency for Everywhere Inference. From new model options to updated product documentation, here’s what’s new in February.Expanding the model libraryWe’ve added several powerful models to Gcore Everywhere Inference, providing more options for AI inference and fine-tuning. This includes three DeepSeek R1 options, state-of-the-art open-weight models optimized for various NLP tasks.DeepSeek’s recent rise represents a major shift in AI accessibility and enterprise adoption. Learn more about DeepSeek’s rise and what it means for businesses in our dedicated blog. Or, explore what DeepSeek’s popularity means for Europe.The following new models are available now in our model library:QVQ-72B-Preview: A large-scale language model designed for advanced reasoning and language understanding.DeepSeek-R1-Distill-Qwen-14B: A distilled version of DeepSeek R1, providing a balance between efficiency and performance for language processing tasks.DeepSeek-R1-Distill-Qwen-32B: A more robust distilled model designed for enterprise-scale AI applications requiring high accuracy and inference speed.DeepSeek-R1-Distill-Llama-70B: A distilled version of Llama 70B, offering significant improvements in efficiency while maintaining strong performance in complex NLP tasks.Phi-3.5-MoE-instruct: A high-quality, reasoning-focused model supporting multilingual capabilities with a 128K context length.Phi-4: A 14-billion-parameter language model excelling in mathematics and advanced language processing.Mistral-Small-24B-Instruct-2501: A 24-billion-parameter model optimized for low-latency AI tasks, performing competitively with larger models.These additions give developers more flexibility in selecting the right models for their use cases, whether they require large-scale reasoning, multimodal capabilities, or optimized inference efficiency. The Gcore model library offers numerous popular models available at the click of a button, but you can also bring your own custom model just as easily.Everywhere Inference product documentationTo help you get the most out of Gcore Everywhere Inference, we’ve expanded our product documentation. Whether you’re deploying AI models, fine-tuning performance, or scaling inference workloads, our docs provide in-depth guidance, API references, and best practices for seamless AI deployment.Choose Gcore for intuitive, powerful AI deploymentWith these updates, Gcore Everywhere Inference continues to provide the latest and best in AI inference. If you need speed, efficiency, and flexibility, get in touch. We’d love to explore how we can support and enhance your AI workloads.Get a complimentary AI consultation

How to optimize ROI with intelligent AI deployment

As generative AI evolves, the cost of running AI workloads has become a pressing concern. A significant portion of these costs will come from inference—the process of applying trained AI models to real-world data to generate responses, predictions, or decisions. Unlike training, which occurs periodically, inference happens continuously, handling vast amounts of user queries and data in real-time. This persistent demand makes managing inference costs a critical challenge, as inefficiencies can gradually drive up expenses.Cost considerations for AI inferenceOptimizing AI inference isn’t just about improving performance—it’s also about controlling costs. Several factors influence the total expense of running AI models at scale, from the choice of hardware to deployment strategies. As businesses expand their AI capabilities, they must navigate the financial trade-offs between speed, accuracy, and infrastructure efficiency.Several factors contribute to inference costs:Compute costs: AI inference relies heavily on GPUs and specialized hardware. These resources are expensive, and as demand grows, so do the associated costs of maintaining and scaling them.Latency vs. cost trade-off: Real-time applications like recommendation systems or conversational AI require ultra-fast processing. Achieving low latency often demands premium resources, creating a challenging trade-off between performance and cost.Operational overheads: Managing inference at scale can lead to rising expenses, particularly as query volumes increase. While cloud-based inference platforms offer flexibility and scalability, it’s important to implement cost-control measures to avoid unnecessary overhead. Optimizing workload distribution and leveraging adaptive scaling can help mitigate these costs.Balancing performance, cost, and efficiency in AI deploymentThe AI marketplace is teeming with different options and configurations. This can make critical decisions about inference optimization, like model selection, infrastructure, and operational management, feel overwhelming and easy to get wrong. We recommend these key considerations when navigating the choices available:Selecting the right model sizeAI models range from massive foundational models to smaller, task-specific in-house solutions. While large models excel in complex reasoning and general-purpose tasks, smaller models can deliver cost-efficient, accurate results for specific applications. Finding the right balance often involves:Experimenting during the proof-of-concept (POC) phase to test different model sizes and accuracy levels.Prioritizing smaller models where possible without compromising task performance.Matching compute with task requirementsNot every workload requires the same level of computational power. By matching hardware resources to model and task requirements, businesses can significantly reduce costs while maintaining performance.Optimizing infrastructure for cost-effective inferenceInfrastructure plays a pivotal role in determining inference efficiency. Here are three emerging trends:Leveraging edge inference: Moving inference closer to the data source can minimize latency and reduce reliance on more expensive centralized cloud solutions. This approach can optimize costs and improve regulatory compliance for data-sensitive industries.Repatriating compute: Many businesses are moving away from hyperscalers—large cloud providers like AWS, Google Cloud, and Microsoft Azure—to local, in-country cloud providers for simplified compliance and often lower costs. This shift enables tighter cost control and can mitigate the unpredictable expenses often associated with cloud platforms.Dynamic inference management tools: Advanced monitoring tools help track real-time performance and spending, enabling proactive adjustments to optimize ROI.Building a sustainable AI futureGcore’s solutions are designed to help you achieve the ideal balance between cost, performance, and scalability. Here’s how:Smart workload routing: Gcore’s intelligent routing technology ensures workloads are processed at the most suitable edge location. While proximity to the user is prioritized for lower latency and compliance, this approach can also save cost by keeping inference closer to data sources.Per-minute billing and cost tracking: Gcore’s platform offers unparalleled budget control with granular per-minute billing. This transparency allows businesses to monitor and optimize their spending closely.Adaptive scaling: Gcore’s adaptive scaling capabilities allocate just the right amount of compute power needed for each workload, reducing resource waste without compromising performance.How Gcore enhances AI inference efficiencyAs AI adoption grows, optimizing inference efficiency becomes critical for sustainable deployment. Carefully balancing model size, infrastructure, and operational strategies can significantly enhance your ROI.Gcore’s Everywhere Inference solution provides a reliable framework to achieve this balance, delivering cost-effective, high-performance AI deployment at scale.Explore Everywhere Inference

The future of AI workloads: scalable inference at the edge

Although artificial intelligence (AI) is rapidly transforming various industries, its value ultimately hinges on inference—the process of running trained models to generate predictions and insights on data it has never seen before. Historically, AI training has been centralized, meaning that models have been developed and trained in large, remote data centers with vast computational resources. However, we’re witnessing a significant shift toward edge-based decentralized inference, where models can operate closer to end users or data. Low-latency processing, cost-effectiveness, and data privacy compliance are the driving forces for this evolution. For most AI-driven projects, efficient inference scaling is essential, though some low-traffic or batch-processing tasks may require less of it.The evolution of AI workloadsThe way businesses handle AI workloads is changing as AI adoption increases. In contrast to early AI efforts, which focused primarily on training complex models, today’s focus lies in optimizing inference or applying these trained models in real-time. The increasing demand for scalability, cost-effectiveness, and real-time processing drives this shift, guaranteeing that AI can generate valuable insights quickly and at a large scale.Training vs. inferenceTraining and inference are the two key processes involved in developing and operating AI workloads. Building AI models through training is a resource-intensive process that requires massive data sets and computational capacity. Inference is how these trained models are used in real-time to process incoming data and generate predictions. For example, an AI model trained on historical banking transactions to detect fraud can then infer fraudulent activity in real-time by analyzing new transactions and flagging suspicious patterns. While training defines an AI model’s potential, inference determines its real-world usability.The growing focus on InferenceBusinesses are increasingly prioritizing inference as part of the natural evolution of AI projects. Once a model has been trained or a suitable pretrained model has been identified and procured, it transitions into the inference phase, where the model interacts with real-world data. A few factors drive the increase in inference activity we’re seeing in the marketplace:Businesses have now gained experience experimenting with AI and are ready to deploy models in the real world.Many projects that invested significant time in training have reached a stage where the models meet desired performance levels and are ready for production.The availability of high-performing, pretrained models like ChatGPT has simplified the inference process, reducing the need to train models from scratch.This evolution underscores the growing role of inference in AI workloads as organizations leverage advancements and experience to move models from experimentation to real-world application.The rise of dynamic inference cloudsDue to increasing requirements for AI models to scale, the need for flexible and cost-effective inference environments has also grown. Traditional, static infrastructure struggles to keep up with fluctuating AI workloads, often leading to inefficiencies in performance and cost. This challenge has given rise to dynamic inference clouds—platforms that enable businesses to adjust their compute resources based on workload complexity, latency requirements, and budget constraints.Centralized vs. edge-based inferenceAs AI applications scale, the drawbacks of centralized cloud-based inference become more apparent. Businesses need faster, more efficient ways to process AI workloads while reducing costs and guaranteeing data privacy. Edge-based inference overcomes these issues by bringing AI processing closer to users or data, reducing latency, lowering operating costs, and improving compliance.The challenges of AI inferenceCentralized cloud-based inference is still used in many AI applications; however, this approach presents multiple drawbacks:High latency: Data must travel back and forth between distant locations to centralized servers, resulting in higher latency. This issue is especially relevant for real-time applications like fraud detection and driverless cars.Operational costs: Running inference in centralized environments often involves higher expenses due to cross-region traffic and compute resource requirements. By keeping traffic localized within the country or region of the workload, businesses can significantly reduce these costs and improve efficiency.Data privacy and compliance risks: Multiple industries, including healthcare and financial services, are subject to strict data privacy laws. It is more challenging to guarantee compliance with regional requirements with centralized inference than keeping workloads in their originating region.On the other hand, edge-based inference comes with its own set of challenges. Deploying and managing distributed infrastructure can be complex, requiring careful allocation of resources across multiple locations. Additionally, edge devices often have limited computational power, making it crucial to optimize models for efficiency. Guaranteeing consistent performance and reliability across diverse environments also adds an extra layer or operational complexity.The benefits of edge-based inferenceAs the demand for real-time AI applications grows, centralized inference often falls short of meeting performance, cost, and compliance requirements. Let’s have a look at how edge-based inference addresses these challenges:Low latency: By running inference closer to end users or data, delays are minimized, enabling real-time applications.Cost optimization: Traffic is optimized within the country, optimizing operational costs.Compliance-friendly processing: By keeping sensitive data local, edge-based inference simplifies compliance with regional regulations.While centralized inference offers high computational power and simplicity, it can introduce latency and rising costs at scale. Edge-based inference reduces these issues by processing data closer to the source, enhancing both speed and compliance. The right approach depends on workload demands, budget constraints, and infrastructure capabilities. In practice, combining centralized and edge-based inference often strikes the optimal balance, enabling businesses to achieve both performance and cost-efficiency while maintaining flexibility.Scale AI inference seamlessly with GcoreScalable, dynamic inference is essential for deploying AI efficiently. As your AI applications grow, you need a solution that optimizes performance, reduces latency, and keeps data compliant with privacy regulations. Gcore Everywhere Inference lets you deploy AI workloads dynamically, bringing inference closer to users and data sources. With a global edge infrastructure, smart routing, and autoscaling capabilities, Gcore guarantees your AI runs efficiently, cost-effectively, and adapts to real-world demands.Ready to scale your AI workloads with edge inference?Explore Everywhere Inference

What do the Stargate and DeepSeek AI announcements mean for Europe?

Within the last week, we’ve seen the announcement of two major AI developments: Last week, President Trump unveiled The Stargate Project, a $500bn venture to build up AI infrastructure in the US, while Chinese start-up DeepSeek blindsided the technology and finance worlds with the surprise launch of its new high-quality and cost-efficient AI models. Seemingly in a rushed response to this news, fellow Chinese tech company Alibaba yesterday announced a new version of its own AI model—which it claims outperforms the latest DeepSeek iteration.President Trump immediately declared DeepSeek a wake-up call for the US, while Meta was said to be “scrambling war rooms of engineers” seeking ways to compete with DeepSeek in terms of low costs and computing power. But if the normally bullish American government and tech giants are rattled by DeepSeek, where does that leave the more highly regulated and divided Europe in terms of keeping up with these AI titans?Multiple sources have already expressed concerns about Europe’s role in the AI age, including the CEO of German software developer SAP, who blamed the silos that come with individual countries having different domestic priorities. European venture capitalists had a more mixed view, with some lamenting the slower speed of European innovation but some also citing DeepSeek’s seeming cost-effectiveness as an inspiration for more low-cost AI development across the continent.With an apparent AI arms race developing between the US and China, is Europe really being left behind, or is that a misperception? Does it matter? And how should the continent respond to these global leaps in AI advancement?Why does it seem like the US and China are outpacing Europe?China and the US are racing ahead in AI due to massive investments in research, talent, and infrastructure. China’s government plays a significant role by backing AI as a national priority, with strategic plans, large data sets (due to its population size), and a more flexible regulatory environment than Europe.Similarly, the US benefits from its robust tech industry with major players like Google, OpenAI, Meta, and Microsoft, as well as a long-standing culture of innovation and risk-taking in the private sector. The US is also the home of some of the world’s leading academic institutions, which are driving AI breakthroughs. Europe, by contrast, lacks some of these major drivers, and the hurdles that AI innovators face in Europe include the following:Fragmented markets and regulationUnlike China and the US, Europe is made up of individual countries, each with their own regulatory frameworks. This can create delays and complexities for scaling AI initiatives. While Europe is leading the way on data privacy with laws like GDPR, these regulations can also slow innovation. Forward-thinking EU initiatives such as the AI Act and Horizon Europe are also in progress, albeit in the early stages.Compare this to China and the US, where regulations are minimalist with the goal of driving innovation. For instance, collecting large datasets, essential for training AI models, is much easier in the US and China due to looser privacy concerns. This creates an innovation lag, especially in consumer-facing AI.The US used to have national-level regulation, but that was revoked in January 2025 with Trump’s Executive Order, and some states have little to no regulation, leaving businesses free to innovate without barriers. China has relatively strict AI laws, but they’re all applied consistently across the vast country, making their application simple compared to Europe’s piecemeal approach. All of this has the potential to incentivize AI innovators to set up shop outside of Europe for the sake of speed and simplicity—although plenty remain in Europe!Talent drainThe US and China can attract the best AI talent due to financial incentives, fewer regulatory barriers, and more concentrated hubs (Silicon Valley, Beijing). While many AI experts trained in Europe, they often move abroad or work with multinational corporations that are based elsewhere. Europe has excellent academic institutions, but the private sector can struggle to keep talent within the region.Funding gapsStartups in Europe face more challenges in terms of funding and scaling compared to those in the US or China. Venture capital is more abundant and aggressive in the US, and the Chinese government heavily invests in AI companies with a clear, state-backed direction. In contrast, European investors are often more risk-averse, and many AI startups struggle to get the same level of backing.How should Europe respond to global AI innovations?While Europe may not be able to compete with the wealth, unification, and autonomy of either China or the US, there are plenty of important areas in which it excels, even leading these other players. Besides that, caution and stricter adherence to ethical regulations may be beneficial in the long run. Last year, the previous US administration commissioned a report warning of the dangers of AI evolving too quickly. Europe’s more “slow and steady” approach is more likely to mitigate these risks.At the same time, Europe should aim to foster innovation as well as take advantage of AI developments in other markets. Here are some more ways in which European companies can take advantage of their regional positioning to get ahead in the global AI market:Innovation in niche areas: Europe may not be able to lead in general-purpose AI like the US or China, but it can carve out spaces in areas like ethical AI, AI governance, and privacy-focused AI. European companies could also specialize in areas like AI for healthcare, environmental sustainability, or manufacturing, where the continent has existing strengths.Collaboration over competition: European nations might need to focus on collaborative efforts. By pooling resources, sharing expertise, and aligning on common goals, Europe can build a unified approach to AI that is both innovative and cohesive. This collaborative model could help Europe create AI frameworks that are sustainable, inclusive, and ethically responsible, all while fostering a spirit of teamwork rather than rivalry.AI sovereignty: AI sovereignty means aiming to ensure that Europe isn’t overly dependent on American or Chinese tech giants and keeps European data in Europe. This involves building localized infrastructure, developing homegrown AI solutions, and protecting European data—while ensuring European AI remains competitive globally. European sovereignty and the region’s tight regulations are likely to catch the eye of the international AI market in light of the already-emerging concerns regarding DeepSeek and censorship, which may be offputting for markets outside of China.So, while the US and China are making the headlines right now, Europe is more quietly paving its own areas of AI specialization, characterized by concern for data privacy and ethics. We’re curious to see whether the global AI market will turn its attention to the benefits Europe offers during 2025. Whether or not European AI companies become top news stories, there’s no doubt that we’re already seeing incredible quality AI models coming out of the continent, and exciting projects in the works that build on key industries and expertise in the region.Talk to us about your AI needsNo matter where in the world your business operates, it’s essential to keep up with changes in the fast-paced AI world. These constant shifts in the market and rapid innovation cycles can create both opportunities and challenges for businesses. While it may be tempting to jump on the latest bandwagon, businesses should carefully examine the pros and cons for their specific use case, and keep in mind their regulatory responsibilities.Whether you’re operating in Europe or globally, our innovative solutions can help you navigate the fast-moving world of AI. Get in touch to learn more about how Gcore Everywhere Inference can support your AI innovation journey.Get a personalized AI consultation

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