Gaming industry under DDoS attack. Get DDoS protection now. Start onboarding
  1. Home
  2. Blog
  3. How to monetize video content in 2021
Network
Expert insights

How to monetize video content in 2021

  • September 2, 2021
  • 11 min read
How to monetize video content in 2021

Every day we consume tons of content. According to DoubleVerify research, the average amount of time users spend browsing information online doubled in 2020.

The same research has shown that the audience of such corporations as Netflix, Disney+, and YouTube grew by 40–50% during the last year.

Video content manufacturers are having their good times now. And a growing audience provides an opportunity to earn more money.

In this article, we will focus on how to monetize video content in 2021 effectively.

Main content monetization techniques

There are many ways of earning money from your content:

  • Paid access (SVOD, TVOD, EST, PVOD)
  • Advertising (AVOD, BVOD)
  • Paid high quality content (4K/8K, AR, 360°)
  • Native and integrated advertising, sponsorship
  • Creating videos on request

Each of these models has its high and low points as well as some peculiarities. Choosing which technique to use depends largely on the needs of your project.

Let us discuss two most popular methods—paid access and advertising.

Monetization through advertising

According to a Dataxis research, the number of video content platforms using advertising is growing every year.

The main advantage of this technique for the viewers is evident: users don’t have to pay for the opportunity to watch the content. This creates further advantages:

  • Such video content spreads easier. Any of your viewers can share a link to your video and increase your audience.
  • If you select relevant advertising and insert it in your content properly, ads can even be useful to your viewers.

The key disadvantages:

  • Users dislike advertising. If there are too many ads in your videos, or if these ads are irrelevant, this will influence user experience negatively.
  • Ads get blocked by Adblock. According to Backlinko, 42.7% internet users all over the world use ad blocking software.

The key point to consider when opting for the advertising model is that it only fits channels with a large audience. The more people watch your video content, the more attractive your channel is for advertisers.

When you start your project, no ads are needed. Once you’ve attracted enough viewers, you can start using monetization techniques. If you manage to create a large audience and to insert relevant advertising in your content, your initial spending will be justified.

The most important question is: how to integrate relevant advertising in your content to make the model work well? How to make this integration bring profit to both you and your advertisers and to prevent your users from getting annoyed by the ads? Let’s take a deeper look into it.

How to insert ads effectively?

This issue needs to be examined from the viewpoint of marketing as well as from the technical point of view.

From the viewpoint of marketing, the first thing to consider is whether the advertising is organically connected with your content and meets the needs of your target audience.

If you mainly focus on sporting events, advertising women’s cosmetics during your live broadcasts is hardly going to be relevant.

Analyzing the structure of your target audience and choosing the ads that will be interesting to your viewers requires professional marketing experts’ advice.

It’s not only your content topics that matter. Even the minutest details may turn out to be very effective:

  • In which part of the video the ads appear
  • How long the ads last
  • How many ads there are in one video
  • How ads are integrated into the video and whether they interrupt people mid-sentence
  • Whether the client can interact with the video (e.g., skip the ad or visit the advertiser’s website by clicking on the ad)

All these aspects need to be considered and tested. You should check the reaction of your viewers to different approaches and opt for the most effective ones.

This is where technical issues become important. Use the technologies that make it possible to implement different ad insertion techniques and analyze their effectiveness.

Let’s discuss two ad insertion methods: AVOD and BVOD.

AVOD (Advertising VOD) implies inserting ads while the video is playing.

There are four ways of inserting ads into in a video:

  • Pre-roll—ads starting before the video
  • Mid-roll—ads starting at a certain time after the beginning of the video
  • Pause-roll—ads starting when the video is paused
  • Post-roll—ads starting in the end of the video

You can use VAST/VPAID protocols to make ads visible to users.

VAST is a protocol allowing the video player to launch ads at certain times during the video.

VAST makes the video player fulfill certain instructions and decide:

  • Which ad needs to be integrated into the video
  • How the ad will be shown
  • How long an advertising break is supposed to last
  • Whether the user can visit the advertiser’s website by clicking on the ad and where exactly the user will be redirected
  • Whether the user can skip the integrated ad

VPAID is another advertising protocol allowing you to specify the rules for the video player to follow when showing ad blocks. Its main distinction from VAST consists in its ability to show interactive ads and record the audience’s reaction to the ads.

VPAID provides statistical data allowing you to estimate ads effectiveness.

This protocol allows you to insert ad blocks users are able not only view but also interact with in the following ways:

  • Click on different buttons to see more information
  • Fill in a form included in the ad (e.g., leave your contacts)
  • Participate in a poll contained in the ad block
  • Interact with different elements or even play a game

At the same time, this protocol monitors users’ actions allowing you to gather detailed statistical data, test different approaches, and make conclusions concerning the effectiveness of the ads integrated into your videos.

BVOD (Broadcaster VOD) is a variation of AVOD used by TV channels broadcasting TV shows with ads.

It gives you the opportunity to watch TV shows later, rewind the broadcast, and watch extra materials not included in the main content body.

The ad’s position in such videos can be specified using special SCTE-35 marks in the video stream or using the chunk schedule (chunks are short segments that videos are divided into in order to speed up content delivery).

All these technologies make it possible to specify the ad’s position in the video stream, track user interaction with the ad, receive statistical information, and make conclusions concerning the effectiveness of the ad integrated into your video. Yet they fail to solve another pool of tasks that need to be fulfilled in terms of effective ad insertion:

  • Balancing ad quality with video quality
  • Avoiding buffering while the ad block is playing
  • Avoiding delays when ads are inserted in live broadcasts
  • Preventing ads from being blocked by Adblock

There is a technology that is capable of solving all these problems. It is called Server-Side Ad Insertion.

Server-Side Ad Insertion

There are two opposite technologies: Client-Side Ad Insertion and Server-Side Ad Insertion.

Client-Side Ad Insertion implies that when the player decides that it’s time to show advertising, it stops the main video, sends a request to the server where the ad is stored, receives the ad and shows it.

Server-Side Ad Insertion (SSAI) merges the video content and the ad to form an inseparable video stream.

To use SSAI you need an intermediate server that will retrieve ads from the ads server and insert them in the main video. The ad block positions need to be specified in advance, and the server inserts them in the video before the content is delivered to the user.

The ad format in this case is changed to fit the video format. The ad is divided into small segments to speed up its delivery to the user.

SSAI advantages:

  • Ads are delivered to the user together with the content, and there is no need to interrupt the main video in order to request the ad block from the server. This helps avoid buffering.
  • SSAI uses the adaptive bitrate streaming technique: the video quality is changed depending on the user’s device characteristics and on the Internet connection quality available. Buffering can be avoided even in case of poor Internet connection.
  • Both the ad block and the main video are divided into segments, which speeds up content delivery to the user and makes it possible to avoid delays during live broadcasts.
  • The SSAI’s greatest advantage is that it makes Adblock perceive ads as part of the video stream. As a result, ads aren’t blocked.

Yet there is one important detail to consider: this technology requires high computer performance. It is necessary to retrieve the ad from the server, transcode it for the purpose of making it meet the necessary formats, integrate it into the video, divide it into segments, and deliver it to the user. And all this needs to be done fast and without any delays.

This is why using this ad insertion technology requires a high-capacity server.

How to integrate ad insertion technologies into your website

We’ve enlisted quite a number of effective technological solutions. Integrating them into your website on your own and making your website work properly and without delays is a challenging, time- and money-consuming task.

Yet there is a faster and cheaper solution to this problem—using an advanced streaming platform.

A streaming platform is a special service using a set of technical solutions that allow you to deliver your video content to the users.

There are many types of such platforms, and all of them feature different functions. Some streaming platforms present a complex service where the platform provider takes on the whole broadcasting process from video capture to playing the video on the users’ devices. Other streaming platforms allow you only to upload your content and deliver it to your users.

Some platforms can be used as a single service only. Other solutions can be fully integrated into your website.

Gcore Streaming Platform is a complex solution for live broadcasting as well as for videos shown on request. We provide turnkey video streaming services that can be fully integrated into your website.

We offer stable streaming solutions and can integrate broadcasts and video calls into our clients’ services. Our portfolio features even a telemedicine platform developed for the Duchy of Luxembourg. You can view all examples of our work in the Case Studies section of our website.

As for the advertising tools, our platform supports all the technologies mentioned above.

It will allow you to easily upload videos with ads and deliver your content to millions of users with the video quality being up to 8K.

Paid video content access

This model can be implemented in many different ways. You can sell one-time access to certain videos, provide monthly or yearly subscriptions, or publish additional materials with paid access (for example, you can provide a free live broadcast of some event and then sell its recorded copies).

Subscription

SVOD (Subscription Video On Demand) is rapidly growing in popularity now. In the last 10 years the total revenue of subscription business has grown by 437%. This result is significantly greater than that of other sales models.

Subscriptions are offered by many industry giants including Netflix, Disney+, HBO Max, etc. This business brings the companies immense profits.

For example, in the first quarter of 2021 Disney+ brought the Disney company $2 billion, which is 4 times as much as in the first quarter of 2020. By March 2021, the number of their subscribers reached 100 million.

The company’s equally successful competitor Netflix had 193 million subscribers in July 2020. Public reports show that in the recent years the company’s profits have been boosting.

The key advantages of video content subscription are as follows:

  • You regularly receive pretty large sums. The users have to pay monthly or yearly fees in advance. This model helps you keep your audience with you. If the users like your content, they are more likely to prolong subscription again and again.
  • Subscription is convenient and, in many cases, profitable for the users. They don’t need to pay every time when they want to watch a video. And if they watch your content regularly, this scheme appears to be money-saving for them.

Yet this model isn’t deprived of some drawbacks:

  • Not everyone is ready to pay for a month in advance. Some users are eager to watch some of your videos but aren’t planning to do it regularly, which makes subscription useless to them. You’d better provide an opportunity of one-time paid access for such cases.
  • This model is suitable for you only if you generate plenty of content on a regular basis. If you publish a couple of videos every month, your users will have nothing to watch most of the time and, hence, nothing to pay for.

Buying or renting video content

If you don’t generate video content on a regular basis but want to earn from it, opt for one-time paid access.

You can opt for TVOD (Transactional Video On Demand). This technology implies renting videos, with the users getting paid access to your content for a limited period of time and being able to watch it a limited number of times. Alternatively, you can use EST (Electronic Sell-Through), which implies selling videos and giving your users full access to them without any time and viewing limits.

Early access to premium content

Another model is called PVOD (Premium Video On Demand). It implies providing paid access to your content allowing one to watch the videos earlier than everyone else.

This method is usually used together with SVOD, TVOD, or with the advertising model. The Mulan movie recently produced by Disney+ is a typical PVOD example. To watch it on the first days after the release, the users needed not only to buy subscription but also to pay an additional fee making up about $25.

Key points to consider when opting for the paid access model

1. Your video content must be unique, interesting, and useful. It is very important for the users not to be able to find a free analog of your content.

According to a poll conducted by the Digiday online magazine, 43% of video content producers believe that creating a product the audience will be ready to pay for is the most difficult task when opting for the paid model.

News is a typical content type that users are hardly ever ready to pay for. Many mass media opt for the advertising model instead of paid subscription because their audience can easily find other sources of information that are completely free.

Specialized periodicals publishing reports with exclusive expert comments, research, and other similar things are the only exception from this rule. In such cases, the subscription model can be used.

The New York Times is one of such publications. You can read some articles on their website for free but if you need access to the core materials, you’ll have to pay for it.

2. All technical issues need thorough consideration. How are your users going to pay for the access to your content? How are you going to track the payments received?

The payment system must be well-designed and convenient for your clients.

3. Your content needs efficient protection against illegal copying.

If malicious users steal your content and publish it online, you are at risk of losing the greatest part of your profit.

Using reliable protection is a must. In our blog, you can read an article on how to effectively protect your video content.

Gcore Streaming Platform can differentiate video access rights for different viewers and check if their payments have been received. Before showing a video to the user, the system requests your app for the information on whether the payment has been received, and either allows or blocks the video depending on the response.

We can protect your content against unauthorized user access and illegal copying. Our platform meets all modern safety standards:

  • AES 128/256 encryption protects your video while it is being delivered to the users.
  • Single-use signed URLs are generated to provide access to your content. This helps avoid unauthorized user access to live broadcasts and protect your videos against illegal copying.
  • CORS is a technology allowing you to restrict the number of domains having access to your website. Your content will be unavailable for anybody except the domains specified.
  • DRM is a system complex defining content access rights. It ensures one of the highest protection levels for your content.

Complex approach to monetization

We’ve discussed different approaches to content monetization, yet these solutions can be combined in one. You can give your users free access to videos with ads, and if they don’t want to get distracted by advertising, they can opt for a paid subscription and watch your content without it.

This method is currently used by YouTube. This website offers paid Premium subscription allowing its users to watch videos without ads.

This method can be pretty effective. A Deloitte research has shown that 40% of users are ready to pay subscription fees in order to watch videos without ads.

This model is very convenient for content producers. You earn money from the users who are ready to pay subscription fees and show ads to those who aren’t.

This approach is a client-oriented one because you offer your users to make a choice on their own.

Conclusions

  1. There are many ways of monetizing video content, with paid access and ads being two of the most widespread models.
  2. The advertising model is a popular and effective monetization method. Yet to make it work properly you need to insert ads in your videos properly, try different approaches, and analyze their effectiveness.
  3. You can set rules concerning the ads position in the video and the ways your clients can interact with the ads. You can gather statistics using the VAST/VPAID protocols.
  4. Seamless ad insertion is possible if you use the Server-Side Ad Insertion technology. It solves many technical issues connected with the ad insertion process including disabling Adblock.
  5. Paid access to video content is also an efficient monetization technique. Today’s most popular model is subscription. It is used by such industry leaders as Netflix, HBO Max, Disney+, and others. Yet the paid access model only fits those who offer unique content having no free analogs.
  6. Two monetization models can be combined in one: you can show video content for free with ads and offer paid access without ads. This model is also very effective.
  7. To make your monetization model work, you need to protect your content from illegal copying. If you opt for the advertising model, you need to thoroughly consider the technical issues connected with ad insertion.
  8. Gcore Streaming Platform provides both reliable content protection and effective ad insertion solutions. We use all advanced streaming and content monetization technologies. We will deliver your video content to the viewers safely and with the shortest delays possible—not more than 4 seconds.

Get more information about Streaming Platform on our website or schedule a free consultation with one of our managers to choose a plan that suits your goals best.

You can test our platform for free to make sure that it fits your project needs.

Try for free

Try Gcore Network

Gcore all-in-one platform: cloud, AI, CDN, security, and other infrastructure services.

Related articles

Gcore partners with AVEQ to elevate streaming performance monitoring

At Gcore, delivering exceptional streaming experiences to users across our global network is at the heart of what we do. We're excited to share how we're taking our CDN performance monitoring to new heights through our partnership with AVEQ and their innovative Surfmeter solution.Operating a massive global network spanning 210 points of presence across six continents comes with unique challenges. While our globally distributed caching infrastructure already ensures optimal performance for end-users, we recognized the need for deeper insights into the complex interactions between applications and our underlying network. We needed to move beyond traditional server-side monitoring to truly understand what our customers' users experience in the real world.Real-world performance visibilityThat's where AVEQ's Surfmeter comes in. We're now using Surfmeter to gain unprecedented visibility into our network performance through automated, active measurements that simulate actual streaming video quality, exactly as end-users experience it.This isn't about checking boxes or reviewing server logs. It's about measuring what users see on their screens at home. With Surfmeter, our engineering teams can identify and resolve potential bottlenecks or suboptimal configurations, and collaborate more effectively with our customers to continuously improve Quality of Experience (QoE).How we use SurfmeterAVEQ helps us simulate and analyze real-world scenarios where users access different video streams. Their software runs both on network nodes close to our data center CDN caches and at selected end-user locations with genuine ISP connections.What sets Surfmeter apart is its authentic approach: it opens video streams from the same platforms and players that end-users rely on, ensuring measurements truly represent real-world conditions. Unlike monitoring solutions that simply check stream availability, Surfmeter doesn't make assumptions or use third-party playback engines. Instead, it precisely replicates how video players request and decode data served from our CDN.Rapid issue resolutionWhen performance issues arise, Surfmeter provides our engineers with the deep insights needed to quickly identify root causes. Whether the problem lies within our CDN, with peering partners, or on the server side, we can pinpoint it with precision.By monitoring individual video requests, including headers and timings, and combining this data with our internal logging, we gain complete visibility and observability into our entire streaming pipeline. Surfmeter can also perform ping and traceroute tests from the same device, measuring video QoE, allowing our engineers to access all collected data through one API rather than manually connecting to devices for troubleshooting.Competitive benchmarking and future capabilitiesSurfmeter also enables us to benchmark our performance against other services and network providers. By deploying Surfmeter probes at customer-like locations, we can measure streaming from any source via different ISPs.This partnership reflects our commitment to transparency and data-driven service excellence. By leveraging AVEQ's Surfmeter solution, we ensure that our customers receive the best possible streaming performance, backed by objective, end-user-centric insights.Learn more about Gcore CDN

How we engineered a single pipeline for LL-HLS and LL-DASH

Viewers in sports, gaming, and interactive events expect real-time, low-latency streaming experiences. To deliver this, the industry has rallied around two powerful protocols: Low-Latency HLS (LL-HLS) and Low-Latency DASH (LL-DASH).While they share a goal, their methods are fundamentally different. LL-HLS delivers video in a sequence of tiny, discrete files. LL-DASH delivers it as a continuous, chunked download of a larger file. This isn't just a minor difference in syntax; it implies completely different behaviors for the packager, the CDN, and the player.This duality presents a major architectural challenge: How do you build a single, efficient, and cost-effective pipeline that can serve both protocols simultaneously from one source?At Gcore, we took on this unification problem. The result is a robust, single-source pipeline that delivers streams with a glass-to-glass latency of approximately 2.0 seconds for LL-DASH and 3.0 seconds for LL-HLS. This is the story of how we designed it.Understanding the dualityTo build a unified system, we first had to deeply understand the differences in how each protocol operates at the delivery level.LL-DASH: the continuous feedMPEG-DASH has always been flexible, using a single manifest file to define media segments by their timing. Low-Latency DASH builds on this by using Chunked CMAF segments.Imagine a file that is still being written to on the server. Instead of waiting for the whole file to be finished, the player can request it, and the server can send it piece by piece using Chunked Transfer Encoding. The player receives a continuous stream of bytes and can start playback as soon as it has enough data.Single, long-lived files: A segment might be 2–6 seconds long, but it’s delivered as it’s being generated.Timing-based requests: The player knows when a segment should be available and requests it. The server uses chunked transfer to send what it has so far.Player-driven latency: The manifest contains a targetLatency attribute, giving the player a strong hint about how close to the live edge it should play.LL-HLS: the rapid-fire deliveryLL-HLS takes a different approach. It extends the traditional playlist-based HLS by breaking segments into even smaller chunks called Parts.Think of it like getting breaking news updates. The server pre-announces upcoming Parts in the manifest before they are fully available. The player then requests a Part, but the server holds that request open until the Part is ready to be delivered at full speed. This is called a Blocking Playlist Reload.Many tiny files (Parts): A 2-second segment might be broken into four 0.5-second Parts, each requested individually.Manifest-driven updates: The server constantly updates the manifest with new Parts, and uses special tags like #EXT-X-PART-INF and #EXT-X-SERVER-CONTROL to manage delivery.Server-enforced timing: The server controls when the player receives data by holding onto requests, which helps synchronize all viewers.A simplified diagram visually comparing the LL-HLS delivery of many small parts versus the LL-DASH chunked transfer of a single, larger segment over the same time period.These two philosophies demand different things from a CDN. LL-DASH requires the CDN to intelligently cache and serve partially complete files. LL-HLS requires the CDN to handle a massive volume of short, bursty requests and hold connections open for manifest updates. A traditional CDN is optimized for neither.Forging a unified strategyWith two different delivery models, where do you start? You find the one thing they both depend on: the keyframe.Playback can only start from a keyframe (or I-frame). Therefore, the placement of keyframes, which defines the Group of Pictures (GOP), is the foundational layer that both protocols must respect. By enforcing a consistent keyframe interval on the source stream, we could create a predictable media timeline. This timeline can then be described in two different “languages” in the manifests for LL-HLS and LL-DASH.A single timeline with consistent GOPs being packaged for both protocols.This realization led us to a baseline configuration, but each parameter involved a critical engineering trade-off:GOP: 1 second. We chose a frequent, 1-second GOP. The primary benefit is extremely fast stream acquisition; a player never has to wait more than a second for a keyframe to begin playback. The trade-off is a higher bitrate. A 1-second GOP can increase bitrate by 10–15% compared to a more standard 2-second GOP because you're storing more full-frame data. For real-time, interactive use cases, we prioritized startup speed over bitrate savings.Segment Size: 2 seconds. A 2-second segment duration provides a sweet spot. For LL-DASH and modern HLS players, it's short enough to keep manifest sizes manageable. For older, standard HLS clients, it prevents them from falling too far behind the live edge, keeping latency reduced even on legacy players.Part Size: 0.5 seconds. For LL-HLS, this means we deliver four updates per segment. This frequency is aggressive enough to achieve sub-3-second latency while being coarse enough to avoid overwhelming networks with excessive request overhead, which can happen with part durations in the 100–200ms range.Cascading challenges through the pipeline1. Ingest: predictability is paramountTo produce a clean, synchronized output, you need a clean, predictable input. We found that the encoder settings of the source stream are critical. An unstable source with a variable bitrate or erratic keyframe placement will wreck any attempt at low-latency delivery.For our users, we recommend settings that prioritize speed and predictability over compression efficiency:Rate control: Constant Bitrate (CBR)Keyframe interval: A fixed interval (e.g., every 30 frames for 30 FPS, to match our 1s GOP).Encoder tune: zerolatencyAdvanced options: Disable B-frames (bframes=0) and scene-cut detection (scenecut=0) to ensure keyframes are placed exactly where you command them to be.Here is an example ffmpeg command in Bash that encapsulates these principles:ffmpeg -re -i "source.mp4" -c:a aac -c:v libx264 \ -profile:v baseline -tune zerolatency -preset veryfast \ -x264opts "bframes=0:scenecut=0:keyint=30" \ -f flv "rtmp://your-ingest-url"2. Transcoding and packagingOur transcoding and Just-In-Time Packaging (JITP) layer is where the unification truly happens. This component does more than just convert codecs; it has to operate on a stream that is fundamentally incomplete.The primary challenge is that the packager must generate manifests and parts from media files that are still being written by the transcoder. This requires a tightly-coupled architecture where the packager can safely read from the transcoder's buffer.To handle the unpredictable nature of live sources, especially user-generated content via WebRTC, we use a hybrid workflow:GPU Workers (Nvidia/Intel): These handle the heavy lifting of decoding and encoding. Offloading to GPU hardware is crucial for minimizing processing latency and preserving advanced color formats like HDR+.Software Workers and Filters: These provide flexibility. When a live stream from a mobile device suddenly changes resolution or its framerate drops due to a poor connection, a rigid hardware pipeline would crash. Our software layer can handle these context changes gracefully, for instance, by scaling the erratic source and overlaying it on a stable, black-background canvas, meaning the output stream never stops.This makes our JITP a universal packager, creating three synchronized content types from a single, resilient source:LL-DASH (CMAF)LL-HLS (CMAF)Standard HLS (MPEG-TS) for backward compatibility3. CDN delivery: solving two problems at onceThis was the most intensive part of the engineering effort. Our CDN had to be taught how to excel at two completely different, high-performance tasks simultaneously.For LL-DASH, we developed a custom caching module we call chunked-proxy. When the first request for a new .m4s segment arrives, our edge server requests it from the origin. As bytes flow in from the origin, the chunked-proxy immediately forwards them to the client. When a second client requests the same file, our edge server serves all the bytes it has already cached and then appends the new bytes to both clients' streams simultaneously. It’s a multi-client cache for in-flight data.For LL-HLS, the challenges were different:Handling Blocked Requests: Our edge servers needed to be optimized to hold thousands of manifest requests open for hundreds of milliseconds without consuming excessive resources.Intelligent Caching: We needed to correctly handle cache statuses (MISS, EXPIRED) for manifests to ensure only one request goes to the origin per update, preventing a "thundering herd" problem.High Request Volume: LL-HLS generates a storm of requests for tiny part-files. Our infrastructure was scaled and optimized to serve these small files with minimal overhead.The payoff: ultimate flexibility for developersThis engineering effort wasn't just an academic exercise. It provides tangible benefits to developers building with our platform. The primary benefit is simplicity through unification, but the most powerful benefit is the ability to optimize for every platform.Consider the complex landscape of Apple devices. With our unified pipeline, you can create a player logic that does this:On iOS 17.1+: Use LL-DASH with the new Managed Media Source (MMS) API for ~2.0 second latency.On iOS 14.0 - 17.0: Use native LL-HLS for ~3.0 second latency.On older iOS versions: Automatically fall back to standard HLS with a reduced latency of ~9 seconds.This lets you provide the best possible experience on every device, all from a single backend and a single live source, without any extra configuration.Don't fly blind: observability in a low-latency worldA complex system is useless without visibility, and traditional metrics can be misleading for low-latency streaming. Simply looking at response_time from a CDN log is not enough.We had to rethink what to measure. For example:For an LL-HLS manifest, a high response_time (e.g., 500ms) is expected behavior, as it reflects the server correctly holding the request while waiting for the next part. A low response_time could actually indicate a problem. We monitor “Manifest Hold Time” to ensure this blocking mechanism is working as intended.For LL-DASH, a player requesting a chunk that isn't ready yet might receive a 404 Not Found error. While occasional 404s are normal, a spike can indicate origin-to-edge latency issues. This metric, combined with monitoring player liveCatchup behavior, gives a true picture of stream health.Gcore: one pipeline to serve them allThe paths of LL-HLS and LL-DASH may be different, but their destination is the same: real-time interaction with a global audience. By starting with a common foundation—the keyframe—and custom-engineering every component of our pipeline to handle this duality, we successfully solved the unification problem.The result is a single, robust system that gives developers the power of both protocols without the complexity of running two separate infrastructures. It’s how we deliver ±2.0s latency with LL-DASH and ±3.0s with LL-HLS, and it’s the foundation upon which we’ll build to push the boundaries of real-time streaming even further.

Gcore CDN updates: Dedicated IP and BYOIP now available

We’re pleased to announce two new premium features for Gcore CDN: Dedicated IP and Bring Your Own IP (BYOIP). These capabilities give customers more control over their CDN configuration, helping you meet strict security, compliance, and branding requirements.Many organizations, especially in finance and other regulated sectors, require full control over their network identity. With these new features, Gcore enables customers to use unique, dedicated IP addresses to meet compliance or security standards; retain ownership and visibility over IP reputation and routing, and deliver content globally while maintaining trusted, verifiable IP associations.Read on for more information about the benefits of both updates.Dedicated IP: exclusive addresses for your CDN resourcesThe Dedicated IP feature enables customers to assign a private IP address to their CDN configuration, rather than using shared ones. This is ideal for:Businesses that are subject to strict security or legal frameworks.Customers who want isolated IP resources to ensure consistent access and reputation.Teams using WAAP or other advanced security solutions where dedicated IPs simplify policy management.BYOIP: bring your own IP range to Gcore CDNWith Bring Your Own IP (BYOIP), customers can use their own public IP address range while leveraging the performance and global reach of Gcore CDN. This option is especially useful for:Resellers who prefer to keep Gcore infrastructure invisible to end clients.Enterprises maintaining brand consistency and control over IP reputation.How to get startedBoth features are currently available as paid add-ons and are configured manually by the Gcore team. To request activation or learn more, please contact Gcore Support or your account manager.We’re working on making these features easier to manage and automate in future releases. As always, we welcome your feedback on both the feature functionality and the request process—your insights help us improve the Gcore CDN experience for everyone.Get in touch for more information

Smart caching and predictive streaming: the next generation of content delivery

As streaming demand surges worldwide, providers face mounting pressure to deliver high-quality video without buffering, lag, or quality dips, no matter where the viewer is or what device they're using. That pressure is only growing as audiences consume content across mobile, desktop, smart TVs, and edge-connected devices.Traditional content delivery networks (CDNs) were built to handle scale, but not prediction. They reacted to demand, but couldn’t anticipate it. That’s changing.Today, predictive streaming and AI-powered smart caching are enabling a proactive, intelligent approach to content delivery. These technologies go beyond delivering content by forecasting what users will need and making sure it's there before it's even requested. For network engineers, platform teams, and content providers, this marks a major evolution in performance, reliability, and cost control.What are predictive streaming and smart caching?Predictive streaming is a technology that uses AI to anticipate what a viewer will watch next, so the content can be ready before it's requested. That might mean preloading the next episode in a series, caching popular highlights from a live event, or delivering region-specific content based on localized viewing trends.Smart caching supports this by storing that predicted content on servers closer to the viewer, reducing delays and buffering. Together, they make streaming faster and smoother by preparing content in advance based on user behavior.Unlike traditional caching, which relies on static popularity metrics or simple geolocation, predictive streaming is dynamic. It adapts in real time to what’s happening on the platform: user actions, traffic spikes, network conditions, and content trends. This results in:Faster playback with minimal bufferingReduced bandwidth and server loadHigher quality of experience (QoE) scores across user segmentsFor example, during the 2024 UEFA European Championship, several broadcasters used predictive caching to preload high-traffic game segments and highlight reels based on past viewer drop-off points. This allowed for instant replay delivery in multiple languages without overloading central servers.Why predictive streaming matters for viewersGlobally, viewers tend to binge-watch new streaming platform releases. For example, sci-fi-action drama Fallout got 25% of its annual US viewing minutes (2.9 billion minutes) in its first few days of release. The South Korean series Queen of Tears became Netflix's most-watched Korean drama of all time in 2024, amassing over 682.6 million hours viewed globally, with more than half of those watch hours occurring during its six-week broadcast run.A predictive caching system can take advantage of this launch-day momentum by pre-positioning likely-to-be-watched episodes, trailers, or bonus content at the edge, customized by region, device, or time of day.The result is a seamless, high-performance experience that anticipates user behavior and scales intelligently to meet it.Benefits for streaming providersTraditional CDNs often waste resources caching content that may never be viewed. Predictive caching focuses only on content that is likely to be accessed, leading to:Lower egress costsReduced server loadMore efficient cache hit ratiosOne of the core benefits of predictive streaming is latency reduction. By caching content at the edge before it’s requested, platforms avoid the delay caused by round-trips to origin servers. This is especially critical for:Live sports and eventsInteractive or real-time formats (e.g., polls, chats, synchronized streams)Edge environments with unreliable last-mile connectivityFor instance, during the 2024 Copa América, mobile viewers in remote areas of Argentina were able to stream matches without delay thanks to proactive edge caching based on geo-temporal viewing predictions.How it worksAt the core of predictive streaming is smart caching: the process of storing data closer to the end user before it’s explicitly requested. Here’s how it works:Data ingestion: The system gathers data on user behavior, device types, content popularity, and location-based trends.Behavior modeling: AI models identify patterns (e.g., binge-watching behaviors, peak-hour traffic, or regional content spikes).Pre-positioning: Based on predictions, the system caches video segments, trailers, or interactive assets to edge servers closest to where demand is expected.Real-time adaptation: As user behavior changes, the system continuously updates its caching strategy.Use cases across streaming ecosystemsSmart caching and predictive delivery benefit nearly every vertical of streaming.Esports and gaming platforms: Live tournaments generate unpredictable traffic surges, especially when underdog teams advance. Predictive caching helps preload high-interest match content, post-game analysis, and multilingual commentary before traffic spikes hit. This helps provide global availability with minimal delay.Corporate webcasts and investor events: Virtual AGMs or earnings calls need to stream seamlessly to thousands of stakeholders, often under compliance pressure. Predictive systems can cache frequently accessed segments, like executive speeches or financial summaries, at regional nodes.Education platforms: In EdTech environments, predictive delivery ensures that recorded lectures, supplemental materials, and quizzes are ready for users based on their course progression. This reduces lag for remote learners on mobile connections.VOD platforms with regional licensing: Content availability differs across geographies. Predictive caching allows platforms to cache licensed material efficiently and avoid serving geo-blocked content by mistake, while also meeting local performance expectations.Government or emergency broadcasts: During public health updates or crisis communications, predictive streaming can support multi-language delivery, instant replay, and mobile-first optimization without overloading networks during peak alerts.Looking forward: Personalization and platform governanceWe predict that the next wave of predictive streaming will likely include innovations that help platforms scale faster while protecting performance and compliance:Viewer-personalized caching, where individual user profiles guide what’s cached locally (e.g., continuing series, genre preferences)Programmatic cache governance, giving DevOps and marketing teams finer control over how and when content is distributedCross-platform intelligence, allowing syndicated content across services to benefit from shared predictions and joint caching strategiesGcore’s role in the predictive futureAt Gcore, we’re building AI-powered delivery infrastructure that makes the future of streaming a practical reality. Our smart caching, real-time analytics, and global edge network work together to help reduce latency and cost, optimize resource usage, and improve user retention and stream stability.If you’re ready to unlock the next level of content delivery, Gcore’s team is here to help you assess your current setup and plan your predictive evolution.Discover how Gcore streaming technologies helped fan.at boost subscription revenue by 133%

Protecting networks at scale with AI security strategies

Network cyberattacks are no longer isolated incidents. They are a constant, relentless assault on network infrastructure, probing for vulnerabilities in routing, session handling, and authentication flows. With AI at their disposal, threat actors can move faster than ever, shifting tactics mid-attack to bypass static defenses.Legacy systems, designed for simpler threats, cannot keep pace. Modern network security demands a new approach, combining real-time visibility, automated response, AI-driven adaptation, and decentralized protection to secure critical infrastructure without sacrificing speed or availability.At Gcore, we believe security must move as fast as your network does. So, in this article, we explore how L3/L4 network security is evolving to meet new network security challenges and how AI strengthens defenses against today’s most advanced threats.Smarter threat detection across complex network layersModern threats blend into legitimate traffic, using encrypted command-and-control, slow drip API abuse, and DNS tunneling to evade detection. Attackers increasingly embed credential stuffing into regular login activity. Without deep flow analysis, these attempts bypass simple rate limits and avoid triggering alerts until major breaches occur.Effective network defense today means inspection at Layer 3 and Layer 4, looking at:Traffic flow metadata (NetFlow, sFlow)SSL/TLS handshake anomaliesDNS request irregularitiesUnexpected session persistence behaviorsGcore Edge Security applies real-time traffic inspection across multiple layers, correlating flows and behaviors across routers, load balancers, proxies, and cloud edges. Even slight anomalies in NetFlow exports or unexpected east-west traffic inside a VPC can trigger early threat alerts.By combining packet metadata analysis, flow telemetry, and historical modeling, Gcore helps organizations detect stealth attacks long before traditional security controls react.Automated response to contain threats at network speedDetection is only half the battle. Once an anomaly is identified, defenders must act within seconds to prevent damage.Real-world example: DNS amplification attackIf a volumetric DNS amplification attack begins saturating a branch office's upstream link, automated systems can:Apply ACL-based rate limits at the nearest edge routerFilter malicious traffic upstream before WAN degradationAlert teams for manual inspection if thresholds escalateSimilarly, if lateral movement is detected inside a cloud deployment, dynamic firewall policies can isolate affected subnets before attackers pivot deeper.Gcore’s network automation frameworks integrate real-time AI decision-making with response workflows, enabling selective throttling, forced reauthentication, or local isolation—without disrupting legitimate users. Automation means threats are contained quickly, minimizing impact without crippling operations.Hardening DDoS mitigation against evolving attack patternsDDoS attacks have moved beyond basic volumetric floods. Today, attackers combine multiple tactics in coordinated strikes. Common attack vectors in modern DDoS include the following:UDP floods targeting bandwidth exhaustionSSL handshake floods overwhelming load balancersHTTP floods simulating legitimate browser sessionsAdaptive multi-vector shifts changing methods mid-attackReal-world case study: ISP under hybrid DDoS attackIn recent years, ISPs and large enterprises have faced hybrid DDoS attacks blending hundreds of gigabits per second of L3/4 UDP flood traffic with targeted SSL handshake floods. Attackers shift vectors dynamically to bypass static defenses and overwhelm infrastructure at multiple layers simultaneously. Static defenses fail in such cases because attackers change vectors every few minutes.Building resilient networks through self-healing capabilitiesEven the best defenses can be breached. When that happens, resilient networks must recover automatically to maintain uptime.If BGP route flapping is detected on a peering session, self-healing networks can:Suppress unstable prefixesReroute traffic through backup transit providersPrevent packet loss and service degradation without manual interventionSimilarly, if a VPN concentrator faces resource exhaustion from targeted attack traffic, automated scaling can:Spin up additional concentratorsRedistribute tunnel sessions dynamicallyMaintain stable access for remote usersGcore’s infrastructure supports self-healing capabilities by combining telemetry analysis, automated failover, and rapid resource scaling across core and edge networks. This resilience prevents localized incidents from escalating into major outages.Securing the edge against decentralized threatsThe network perimeter is now everywhere. Branches, mobile endpoints, IoT devices, and multi-cloud services all represent potential entry points for attackers.Real-world example: IoT malware infection at the branchMalware-infected IoT devices at a branch office can initiate outbound C2 traffic during low-traffic periods. Without local inspection, this activity can go undetected until aggregated telemetry reaches the central SOC, often too late.Modern edge security platforms deploy the following:Real-time traffic inspection at branch and edge routersBehavioral anomaly detection at local points of presenceAutomated enforcement policies blocking malicious flows immediatelyGcore’s edge nodes analyze flows and detect anomalies in near real time, enabling local containment before threats can propagate deeper into cloud or core systems. Decentralized defense shortens attacker dwell time, minimizes potential damage, and offloads pressure from centralized systems.How Gcore is preparing networks for the next generation of threatsThe threat landscape will only grow more complex. Attackers are investing in automation, AI, and adaptive tactics to stay one step ahead. Defending modern networks demands:Full-stack visibility from core to edgeAdaptive defense that adjusts faster than attackersAutomated recovery from disruption or compromiseDecentralized detection and containment at every entry pointGcore Edge Security delivers these capabilities, combining AI-enhanced traffic analysis, real-time mitigation, resilient failover systems, and edge-to-core defense. In a world where minutes of network downtime can cost millions, you can’t afford static defenses. We enable networks to protect critical infrastructure without sacrificing performance, agility, or resilience.Move faster than attackers. Build AI-powered resilience into your network with Gcore.Check out our docs to see how DDoS Protection protects your network

Introducing Gcore for Startups: created for builders, by builders

Building a startup is tough. Every decision about your infrastructure can make or break your speed to market and burn rate. Your time, team, and budget are stretched thin. That’s why you need a partner that helps you scale without compromise.At Gcore, we get it. We’ve been there ourselves, and we’ve helped thousands of engineering teams scale global applications under pressure.That’s why we created the Gcore Startups Program: to give early-stage founders the infrastructure, support, and pricing they actually need to launch and grow.At Gcore, we launched the Startups Program because we’ve been in their shoes. We know what it means to build under pressure, with limited resources, and big ambitions. We wanted to offer early-stage founders more than just short-term credits and fine print; our goal is to give them robust, long-term infrastructure they can rely on.Dmitry Maslennikov, Head of Gcore for StartupsWhat you get when you joinThe program is open to startups across industries, whether you’re building in fintech, AI, gaming, media, or something entirely new.Here’s what founders receive:Startup-friendly pricing on Gcore’s cloud and edge servicesCloud credits to help you get started without riskWhite-labeled dashboards to track usage across your team or customersPersonalized onboarding and migration supportGo-to-market resources to accelerate your launchYou also get direct access to all Gcore products, including Everywhere Inference, GPU Cloud, Managed Kubernetes, Object Storage, CDN, and security services. They’re available globally via our single, intuitive Gcore Customer Portal, and ready for your production workloads.When startups join the program, they get access to powerful cloud and edge infrastructure at startup-friendly pricing, personal migration support, white-labeled dashboards for tracking usage, and go-to-market resources. Everything we provide is tailored to the specific startup’s unique needs and designed to help them scale faster and smarter.Dmitry MaslennikovWhy startups are choosing GcoreWe understand that performance and flexibility are key for startups. From high-throughput AI inference to real-time media delivery, our infrastructure was designed to support demanding, distributed applications at scale.But what sets us apart is how we work with founders. We don’t force startups into rigid plans or abstract SLAs. We build with you 24/7, because we know your hustle isn’t a 9–5.One recent success story: an AI startup that migrated from a major hyperscaler told us they cut their inference costs by over 40%…and got actual human support for the first time. What truly sets us apart is our flexibility: we’re not a faceless hyperscaler. We tailor offers, support, and infrastructure to each startup’s stage and needs.Dmitry MaslennikovWe’re excited to support startups working on AI, machine learning, video, gaming, and real-time apps. Gcore for Startups is delivering serious value to founders in industries where performance, cost efficiency, and responsiveness make or break product experience.Ready to scale smarter?Apply today and get hands-on support from engineers who’ve been in your shoes. If you’re an early-stage startup with a working product and funding (pre-seed to Series A), we’ll review your application quickly and tailor infrastructure that matches your stage, stack, and goals.To get started, head on over to our Gcore for Startups page and book a demo.Discover Gcore for Startups

Subscribe to our newsletter

Get the latest industry trends, exclusive insights, and Gcore updates delivered straight to your inbox.