Since 2018, Cloudflare Stream has offered a managed video pipeline that empowers customers to serve rich video experiences at global scale easily, in multiple formats and quality levels. Sometimes, the greatest friction to getting started isn't even about video, but rather the thought of migrating all those files. Customers want a simpler solution that retains their current storage strategy to deliver small, optimized MP4 files. Now you can do that with Media Transformations.

For customers with a huge volume of short video, such as generative AI output, e-commerce product videos, social media clips, or short marketing content, uploading those assets to Stream is not always practical. Furthermore, Stream’s key features like adaptive bitrate encoding and HLS packaging offer diminishing returns on short content or small files.

Instead, content like this should be fetched from our customers' existing storage like R2 or S3 directly, optimized by Cloudflare quickly, and delivered efficiently as small MP4 files. Cloudflare Images customers reading this will note that this sounds just like their existing Image Transformation workflows. Starting today, the same workflow can be applied to your short-form videos.

The distinction between video and images online can sometimes be blurry --- consider an animated GIF: is that an image or a video? (They're usually smaller as MP4s anyway!) As a practical example, consider a selection of product images for a new jacket on an e-commerce site. You want a consumer to know how it looks, but also how it flows. So perhaps the first "image" in that carousel is actually a video of a model simply putting the jacket on. Media Transformations empowers customers to optimize the product video and images with similar tools and identical infrastructure.

Any website that is already enabled for Image Transformations is now enabled for Media Transformations. To enable a new zone, navigate to “Transformations” under Stream (or Images), locate your zone in the list, and click Enable. Enabling and disabling a zone for transformations affects both Images and Media transformations.

After enabling Media Transformations on a website, it is simple to construct a URL that transforms a video. The pattern is similar to Image Transformations, but uses the media endpoint instead of the image endpoint:

https://example.com/cdn-cgi/media/<OPTIONS>/<SOURCE-VIDEO>

The <OPTIONS> portion of the URL is a comma-separated list of flags written as key=value. A few noteworthy flags:

• mode can be video (the default) to output a video, frame to pull a still image of a single frame, or even spritesheet to generate an image with multiple frames, which is useful for seek previews or storyboarding.

• time specifies the exact start time from the input video to extract a frame or start making a clip

• duration specifies the length of an output video to make a clip shorter than the original

• fit, together with height and width allow resizing and cropping the output video or frame.

• Setting audio to false removes the sound in the output video.

The <SOURCE-VIDEO> is a full URL to a source file or a root-relative path if the origin is on the same zone as the transformation request.

A full list of supported options, examples, and troubleshooting information is available in DevDocs.

I used my phone to take this video of the randomness mobile in Cloudflare’s Austin Office and put it in an R2 bucket. Of course, it is possible to embed the original video file from R2 directly:

That video file is almost 30 MB. Let’s optimize it together — a more efficient choice would be to resize the video to the width of this blog post template. Let’s apply a width adjustment in the options portion of the URL:

https://example.com/cdn-cgi/media/width=760/https://pub-d9fcbc1abcd244c1821f38b99017347f.r2.dev/aus-mobile.mp4

That will deliver the same video, resized and optimized:

Not only is this video the right size for its container, now it’s less than 4 MB. That’s a big bandwidth savings for visitors.

As I recorded the video, the lobby was pretty quiet, but there was someone talking in the distance. If we wanted to use this video as a background, we should remove the audio, shorten it, and perhaps crop it vertically. All of these options can be combined, comma-separated, in the options portion of the URL:

https://example.com/cdn-cgi/media/mode=video,duration=10s,width=480,height=720,fit=cover,audio=false/https://pub-d9fcbc1abcd244c1821f38b99017347f.r2.dev/aus-mobile.mp4

The result:

If this were a product video, we might want a small thumbnail to add to the carousel of images so shoppers can click to zoom in and see it move. Use the “frame” mode and a “time” to generate a static image from a single point in the video. The same size and fit options apply:

https://example.com/cdn-cgi/media/mode=frame,time=3s,width=120,height=120,fit=cover/https://pub-d9fcbc1abcd244c1821f38b99017347f.r2.dev/aus-mobile.mp4

Which generates this optimized image:

Try it out yourself using our video or one of your own: 

• Enable transformations on your website/zone and use the endpoint: https://[your-site]/cdn-cgi/media/

• Mobile video: https://pub-d9fcbc1abcd244c1821f38b99017347f.r2.dev/aus-mobile.mp4 

• Check out the Media Transformation URL Generator from Kristian Freeman on our Developer Relations team, which he built using the Streamlit Python framework on Workers.

We are eager to start supporting real customer content, and we will right-size our input limitations with our early adopters. To start:

• Video files must be smaller than 40 megabytes.

• Files must be MP4s and should be h.264 encoded.

• Videos and images generated with Media Transformations will be cached. However, in our initial beta, the original content will not be cached which means regenerating a variant will result in a request to the origin.

Unlike Stream, Media Transformations receives requests on a customer’s own website. Internally, however, these requests are passed to the same On-the-Fly Encoder (“OTFE”) platform that Stream Live uses. To achieve this, the Stream team built modules that run on our servers to act as entry points for these requests.

These entry points perform some initial validation on the URL formatting and flags before building a request to Stream’s own Delivery Worker, which in turn calls OTFE’s set of transformation handlers. The original asset is fetched from the customer’s origin, validated for size and type, and passed to the same OTFE methods responsible for manipulating and optimizing video or still frame thumbnails for videos uploaded to Stream. These tools do a final inspection of the media type and encoding for compatibility, then generate the requested variant. If any errors were raised along the way, an HTTP error response will be generated using similar error codes to Image Transformations. When successful, the result is cached for future use and delivered to the requestor as a single file. Even for new or uncached requests, all of this operates much faster than the video’s play time.

Media Transformations will be free for all customers while in beta. We expect the beta period to extend into Q3 2025, and after that, Media Transformations will use the same subscriptions and billing mechanics as Image Transformations — including a free allocation for all websites/zones. Generating a still frame (single image) from a video counts as 1 transformation. Generating an optimized video is billed as 1 transformation per second of the output video. Each unique transformation is only billed once per month. All Media and Image Transformations cost $0.50 per 1,000 monthly unique transformation operations, with a free monthly allocation of 5,000.

Using this post as an example, recall the two transformed videos and one transformed image above — the big original doesn’t count because it wasn’t transformed. The first video (showing blog post width) was 15 seconds of output. The second video (silent vertical clip) was 10 seconds of output. The preview square is a still frame. These three operations would count as 26 transformations — and they would only bill once per month, regardless of how many visitors this page receives.

Our short-term focus will be on right-sizing input limits based on real customer usage as well as adding a caching layer for origin fetches to reduce any egress fees our customers may be facing from other storage providers. Looking further, we intend to streamline Images and Media Transformations to further simplify the developer experience, unify the features, and streamline enablement: Cloudflare’s Media Transformations will optimize your images and video, quickly and easily, wherever you need them.

Try it for yourself today using our sample asset above, or get started by enabling Transformations on a zone in your account and uploading a short file to R2, both of which offer a free tier to get you going.

With one click, customers can now generate video captions effortlessly using Stream’s newest feature: AI-generated captions for on-demand videos and recordings of live streams. As part of Cloudflare’s mission to help build a better Internet, this feature is available to all Stream customers at no additional cost.

This solution is designed for simplicity, eliminating the need for third-party transcription services and complex workflows. For videos lacking accessibility features like captions, manual transcription can be time-consuming and impractical, especially for large video libraries. Traditionally, it has involved specialized services, sometimes even dedicated teams, to transcribe audio and deliver the text along with video, so it can be displayed during playback. As captions become more widely expected for a variety of reasons, including ethical obligation, legal compliance, and changing audience preferences, we wanted to relieve this burden.

With Stream’s integrated solution, the caption generation process is seamlessly integrated into your existing video management workflow, saving time and resources. Regardless of when you uploaded a video, you can easily add automatic captions to enhance accessibility. Captions can now be generated within the Cloudflare Dashboard or via an API request, all within the familiar and unified Stream platform.

This feature is designed with utmost consideration for privacy and data protection. Unlike other third-party transcription services that may share content with external entities, your data remains securely within Cloudflare's ecosystem throughout the caption generation process. Cloudflare does not utilize your content for model training purposes. For more information about data protection, review Your Data and Workers AI.

Starting June 20th, 2024, this beta is available for all Stream customers as well as subscribers of the Professional and Business plans, which include 100 minutes of video storage.

To get started, upload a video to Stream (from the Cloudflare Dashboard or via API).

Next, navigate to the "Captions" tab on the video, click “Add Captions,” then select the language and “Generate captions with AI.” Finally, click save and within a few minutes, the new captions will be visible in the captions manager and automatically available in the player, too. Captions can also be generated via the API.

Captions are usually generated in a few minutes. When captions are ready, the Stream player will automatically be updated to offer them to users. The HLS and DASH manifests are also updated so third party players that support text tracks can display them as well.

On-demand videos and recordings of live streams, regardless of when they were created, are supported. While in beta, only English captions can be generated, and videos must be shorter than 2 hours. The quality of the transcription is best on videos with clear speech and minimal background noise.

We've been pleased with how well the AI model transcribes different types of content during our tests. That said, there are times when the results aren't perfect, and another method might work better for some use cases. It's important to check if the accuracy of the generated captions are right for your needs.

The Stream engineering team built this new feature using Workers AI, allowing us to access the Whisper model – an open source Automatic Speech Recognition model – with a single API call. Using Workers AI radically simplified the AI model deployment, integration, and scaling with an out-of-the-box solution. We eliminated the need for our team to handle infrastructure complexities, enabling us to focus solely on building the automated captions feature.

Writing software that utilizes an AI model can involve several challenges. First, there’s the difficulty of configuring the appropriate hardware infrastructure. AI models require substantial computational resources to run efficiently and require specialized hardware, like GPUs, which can be expensive and complex to manage. There’s also the daunting task of deploying AI models at scale, which involve the complexities of balancing workload distribution, minimizing latency, optimizing throughput, and maintaining high availability. Not only does Workers AI solve the pain of managing underlying infrastructure, it also automatically scales as needed.

Using Workers AI transformed a daunting task into a Worker that transcribes audio files with less than 30 lines of code.

import { Ai } from '@cloudflare/ai'


export interface Env {
 AI: any
}


export type AiVTTOutput = {
 vtt?: string
}


export default {
 async fetch(request: Request, env: Env) {
   const blob = await request.arrayBuffer()


   const ai = new Ai(env.AI)
   const input = {
     audio: [...new Uint8Array(blob)],
   }


   try {
     const response: AiVTTOutput = (await ai.run(
       '@cf/openai/whisper-tiny-en',
       input
     )) as any
     return Response.json({ vtt: response.vtt })
   } catch (e) {
     const errMsg =
       e instanceof Error
         ? `${e.name}\n${e.message}\n${e.stack}`
         : 'unknown error type'
     return new Response(`${errMsg}`, {
       status: 500,
       statusText: 'Internal error',
     })
   }
 },
}

The Stream team wanted to ensure this feature is fast and performant at scale,   which required engineering work to process a high volume of videos regardless of duration.

First, our team needed to pre-process the audio prior to running AI inference to ensure the input is compatible with Whisper’s input format and requirements.

There is a wide spectrum of variability in video content, from a short grainy video filmed on a phone to a multi-hour high-quality Hollywood-produced movie. Videos may be silent or contain an action-driven cacophony. Also, Stream’s on-demand videos include recordings of live streams which are packaged differently from videos uploaded as whole files. With this variability, the audio inputs are stored in an array of different container formats, with different durations, and different file sizes. We ensured our audio files were properly formatted to be compatible with Whisper’s requirements.

One aspect for pre-processing is ensuring files are a sensible duration for optimized inference.  Whisper has an “sweet spot” of 30 seconds for the duration of audio files for transcription. As they note in this Github discussion: “Too short, and you’d lack surrounding context. You’d cut sentences more often. A lot of sentences would cease to make sense. Too long, and you’ll need larger and larger models to contain the complexity of the meaning you want the model to keep track of.” Fortunately, Stream already splits videos into smaller segments to ensure fast delivery during playback on the web. We wrote functionality to concatenate those small segments into 30-second batches prior to sending to Workers AI.

To optimize processing speed, our team parallelized as many operations as possible. By concurrently creating the 30-second audio batches and sending requests to Workers AI, we take full advantage of the scalability of the Workers AI platform. Doing this greatly reduces the time it takes to generate captions, but adds some additional complexity. Because we are sending requests to Workers AI in parallel, transcription responses may arrive out-of-order. For example, if a video is one minute in duration, the request to generate captions for the second 30 seconds of a video may complete before the request for the first 30 seconds of the video. The captions need to be sequential to align with the video, so our team had to maintain an understanding of the audio batch order to ensure our final combined WebVTT caption file is properly synced with the video. We sort the incoming Workers AI responses and re-order timestamps for a final accurate transcript.

The end result is the ability to generate captions for longer videos quickly and efficiently at scale.

We are excited to bring this feature to open beta for all of our subscribers as well as Pro and Business plan customers today! Get started by uploading a video to Stream. Review our documentation for tutorials and current beta limitations. Up next, we will be focused on adding more languages and supporting longer videos.

Today, we’re excited to announce that Stream Live is out of beta, available to everyone, and ready for production traffic at scale. Stream Live is a feature of Cloudflare Stream that allows developers to build live video features in websites and native apps.

Since its beta launch, developers have used Stream to broadcast live concerts from some of the world’s most popular artists directly to fans, build brand-new video creator platforms, operate a global 24/7 live OTT service, and more. While in beta, Stream has ingested millions of minutes of live video and delivered to viewers all over the world.

Bring your big live events, ambitious new video subscription service, or the next mobile video app with millions of users — we’re ready for it.

Live video uses a massive amount of bandwidth. For example, a one-hour live stream at 1080p at 8Mbps is 3.6GB. At typical cloud provider egress prices, even a little egress can break the bank.

Live video must be encoded on-the-fly, in real-time. People expect to be able to watch live video on their phone, while connected to mobile networks with less bandwidth, higher latency and frequently interrupted connections. To support this, live video must be re-encoded in real-time into multiple resolutions, allowing someone’s phone to drop to a lower resolution and continue playback. This can be complex (Which bitrates? Which codecs? How many?) and costly: running a fleet of virtual machines doesn’t come cheap.

Ingest location matters — Live streaming protocols like RTMPS send video over TCP. If a single packet is dropped or lost, the entire connection is brought to a halt while the packet is found and re-transmitted. This is known as “head of line blocking”. The further away the broadcaster is from the ingest server, the more network hops, and the more likely packets will be dropped or lost, ultimately resulting in latency and buffering for viewers.

Delivery location matters — Live video must be cached and served from points of presence as close to viewers as possible. The longer the network round trips, the more likely videos will buffer or drop to a lower quality.

Broadcasting protocols are in flux — The most widely used protocol for streaming live video, RTMPS, has been abandoned since 2012, and dates back to the era of Flash video in the early 2000s. A new emerging standard, SRT, is not yet supported everywhere. And WebRTC has only recently evolved into an option for high definition one-to-many broadcasting at scale.

The old way to solve this has been to stitch together separate cloud services from different vendors. One vendor provides excellent content delivery, but no encoding. Another provides APIs or hardware to encode, but leaves you to fend for yourself and build your own storage layer. As a developer, you have to learn, manage and write a layer of glue code around the esoteric details of video streaming protocols, codecs, encoding settings and delivery pipelines.

We built Stream Live to make streaming live video easy, like adding an tag to a website. Live video is now a fundamental building block of Internet content, and we think any developer should have the tools to add it to their website or native app.

With Stream, you or your users stream live video directly to Cloudflare, and Cloudflare delivers video directly to your viewers. You never have to manage internal encoding, storage, or delivery systems — it’s just live video in and live video out.

We’re not the only ones building APIs for live video — but we are the only ones with our own global network and hardware that we control and optimize for video. That lets us do things that others can’t, like sub-second glass-to-glass latency using RTMPS and SRT playback at scale.

Newer video codecs require specialized hardware encoders, and while others are beholden to the hardware limitations of public cloud providers, we’re already hard at work installing the latest encoding hardware in our own racks, so that you can deliver high resolution video with even less bandwidth. Our goal is to make what is otherwise only available to video giants available directly to you — stay tuned for some exciting updates on this next week.

Most providers limit how many destinations you can restream or simulcast to. Because we operate our own network, we’ve never even worried about this, and let you restream to as many destinations as you need.

Operating our own network lets us price Stream based on minutes of video delivered — unlike others, we don’t pay someone else for bandwidth and then pass along their costs to you at a markup. The status quo of charging for bandwidth or per-GB storage penalizes you for delivering or storing high resolution content. If you ask why a few times, most of the time you’ll discover that others are pushing their own cost structures on to you.

Encoding video is compute-intensive, delivering video is bandwidth intensive, and location matters when ingesting live video. When you use Stream, you don't need to worry about optimizing performance, finding a CDN, and/or tweaking configuration endlessly. Stream takes care of this for you.

Nearly every business uses live video, whether to engage with customers, broadcast events or monetize live content. But few have the specialized engineering resources to deliver live video at scale on their own, and wire together multiple low level cloud services. To date, many of the largest content creators have been forced to depend on a shortlist of social media apps and streaming services to deliver live content at scale.

Unlike the status quo, who force you to put your live video in their apps and services and fit their business models, Stream gives you full control of your live video, on your website or app, on any device, at scale, without pushing your users to someone else’s service.

Other platforms charge for ingestion and encoding. Many even force you to consider where you’re streaming to and from, the bitrate and frames per second of your video, and even which of their datacenters you’re using.

With Stream, encoding and ingestion are free. Other platforms charge for delivery based on bandwidth, penalizing you for delivering high quality video to your viewers. If you stream at a high resolution, you pay more.

With Stream, you don’t pay a penalty for delivering high resolution video. Stream’s pricing is simple — minutes of video delivered and stored. Because you pay per minute, not per gigabyte, you can stream at the ideal resolution for your viewers without worrying about bandwidth costs.

Other platforms charge separately for analytics, requiring you to buy another product to get metrics about your live streams.

With Stream, analytics are free. Stream provides an API and Dashboard for both server-side and client-side analytics, that can be queried on a per-video, per-creator, per-country basis, and more. You can use analytics to identify which creators in your app have the most viewed live streams, inform how much to bill your customers for their own usage, identify where content is going viral, and more.

Other platforms tack on live recordings or DVR mode as a separate add-on feature, and recordings only become available minutes or even hours after a live stream ends.

With Stream, live recordings are a built-in feature, made available instantly after a live stream ends. Once a live stream is available, it works just like any other video uploaded to Stream, letting you seamlessly use the same APIs for managing both pre-recorded and live content.

Cloudflare Stream enables you or your users to go live using the same protocols and tools that broadcasters big and small use to go live to YouTube or Twitch, but gives you full control over access and presentation of live streams.

Create a new live input from the Stream Dashboard or use use the Stream API:

Request

curl -X POST \
-H "Authorization: Bearer <YOUR_API_TOKEN>" \
-d "{"recording": { "mode": "automatic" } }" \
https://api.cloudflare.com/client/v4/accounts/<YOUR_CLOUDFLARE_ACCOUNT_ID>/stream/live_inputs

Response

{
"result": {
"uid": "<UID_OF_YOUR_LIVE_INPUT>",
"rtmps": {
"url": "rtmps://live.cloudflare.com:443/live/",
"streamKey": "<PRIVATE_RTMPS_STREAM_KEY>"
},
...
}
}

Copy the RTMPS URL and key, and use them with your live streaming application. We recommend using Open Broadcaster Software (OBS) to get started, but any RTMPS or SRT compatible software should be able to interoperate with Stream Live.

Enter the Stream RTMPS URL and the Stream Key from Step 1:

In the Stream Dashboard, within seconds of going live, you will see a preview of what your viewers will see, along with the real-time connection status of your live stream.

Stream your video using our Stream Player embed code, or use any video player that supports HLS or DASH — live streams can be played in both websites or native iOS and Android apps.

For example, on iOS, all you need to do is provide AVPlayer with the URL to the HLS manifest for your live input, which you can find via the API or in the Stream Dashboard.

import SwiftUI
import AVKit

struct MyView: View {
    // Change the url to the Cloudflare Stream HLS manifest URL
    private let player = AVPlayer(url: URL(string: "https://customer-9cbb9x7nxdw5hb57.cloudflarestream.com/8f92fe7d2c1c0983767649e065e691fc/manifest/video.m3u8")!)

    var body: some View {
        VideoPlayer(player: player)
            .onAppear() {
                player.play()
            }
    }
}

struct MyView_Previews: PreviewProvider {
    static var previews: some View {
        MyView()
    }
}

To run a complete example app in XCode, follow this guide in the Stream Developer docs.

Developers want control, but most don’t have time to become video experts. And even video experts building innovative new platforms don’t want to manage live streaming infrastructure.

Switcher Studio's whole business is live video -- their iOS app allows creators and businesses to produce their own branded, multi camera live streams. Switcher uses Stream as an essential part of their live streaming infrastructure. In their own words:

“Since 2014, Switcher has helped creators connect to audiences with livestreams. Now, our users create over 100,000 streams per month. As we grew, we needed a scalable content delivery solution. Cloudflare offers secure, fast delivery, and even allowed us to offer new features, like multistreaming. Trusting Cloudflare Stream lets our team focus on the live production tools that make Switcher unique."

While Stream Live has been in beta, we’ve worked with many customers like Switcher, where live video isn’t just one product feature, it is the core of their product. Even as experts in live video, they choose to use Stream, so that they can focus on the unique value they create for their customers, leaving the infrastructure of ingesting, encoding, recording and delivering live video to Cloudflare.

It takes just a few minutes to sign up and start your first live stream, using the Cloudflare Dashboard, with no code required to get started, but APIs for when you’re ready to start building your own live video features. Give it a try — we’re ready for you, no matter the size of your audience.

Building inclusive technology is core to the Cloudflare mission. Cloudflare Stream has supported captions for on-demand videos for several years. Soon, Stream will auto-detect embedded captions and include it in the live stream delivered to your viewers.

Thousands of Cloudflare customers use the Stream product to build video functionality into their apps. With live caption support, Stream customers can better serve their users with a more comprehensive viewing experience.

Stream Live scans for CEA-608 and CEA-708 captions in incoming live streams ingested via SRT and RTMPS.  Assuming the live streams you are pushing to Cloudflare Stream contain captions, you don’t have to do anything further: the captions will simply get included in the manifest file.

If you are using the Stream Player, these captions will be rendered by the Stream Player. If you are using your own player, you simply have to configure your player to display captions.  

Currently, Stream Live supports captions for a single language during the live event. While the support for captions is limited to one language during the live stream, you can upload captions for multiple languages once the event completes and the live event becomes an on-demand video.

When captions were first introduced in 1973, they were open captions. This means the captions were literally overlaid on top of the picture in the video and therefore, could not be turned off. In 1982, we saw the introduction of closed captions during live television. Captions were no longer imprinted on the video and were instead passed via a separate feed and rendered on the video by the television set.

CEA-608 (also known as Line 21) and CEA-708 are well-established standards used to transmit captions. CEA-708 is a modern iteration of CEA-608, offering support for nearly every language and text positioning–something not supported with CEA-608.

Live caption support will be available in closed beta next month. To request access, sign up for the closed beta.

Including captions in any video stream is critical to making your content more accessible. For example, the majority of live events are watched on mute and thereby, increasing the value of captions. While Stream Live does not generate live captions yet, we plan to build support for automatic live captions in the future.