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.

The last two years have given rise to hundreds of live streaming platforms. Most live streaming platforms enable their creators to go live by providing them with a server and an RTMP/SRT key that they can configure in their broadcasting app.

Until today, even if your live streaming platform was called live-yoga-classes.com, your users would need to push the RTMPS feed to live.cloudflare.com. Starting today, every Stream account can configure its own domain in the Stream dashboard. And your creators can broadcast to a domain such as push.live-yoga-classes.com.

This feature is available to all Stream accounts, including self-serve customers at no additional cost. Every Cloudflare account with a Stream subscription can add up to five ingest domains.

Cloudflare Stream only supports encrypted video ingestion using RTMPS and SRT protocols. These are secure protocols and, similar to HTTPS, ensure encryption between the broadcaster and Cloudflare servers. Unlike non-secure protocols like RTMP, secure RTMP (or RTMPS) protects your users from monster-in-the-middle attacks.

In an unsecure world, you could simply CNAME a domain to another domain regardless of whether you own the domain you are sending traffic to. Because Stream Live intentionally does not support insecure live streams, you cannot simply CNAME your domain to live.cloudflare.com. So we leveraged other Cloudflare products such as Spectrum to natively support custom-branded domains in the Stream Live product without making the live streams less private for your broadcasters.

To begin configuring your custom domain, add the domain to your Cloudflare account as a regular zone.

Add a zone to your Cloudflare account

Next, CNAME the domain to live.cloudflare.com.

CNAME the zone to live.cloudflare.com

Assuming you have a Stream subscription, visit the Inputs page and click on the Settings icon:

Click on Settings icon on Live Inputs page

Next, add the domain you configured in the previous step as a Live Ingest Domain:

Add Custom Ingest Domain

If your domain is successfully added, you will see a confirmation:

Confirmation of domain being added as an ingest domain

Once you’ve added your ingest domain, test it by changing your existing configuration in your broadcasting software to your ingest domain. You can read the complete docs and limitations in the Stream Live developer docs.

Besides the branding upside —  you don’t have to instruct your users to configure a domain such as live.cloudflare.com — custom domains help you avoid vendor lock-in and seamless migration. For example, if you have an existing live video pipeline that you are considering moving to Stream Live, this makes the migration one step easier because you no longer have to ask your users to change any settings in their broadcasting app.

A natural next step is to support custom keys. Currently, your users must still use keys that are provided by Stream Live. Soon, you will be able to bring your own keys. Custom domains combined with custom keys will help you migrate to Stream Live with zero breaking changes for your end users.

Creator platforms across the world use Cloudflare Stream to rapidly build video experiences into their apps. These platforms serve a diverse range of creators, enabling them to share their passion with their beloved audience. While working with creator platforms, we learned that many Stream customers track video usage on a per-creator basis in order to answer critical questions such as:

• “Who are our fastest growing creators?”

• “How much do we charge or pay creators each month?”

• “What can we do more of in order to serve our creators?”

Creator platforms enable artists, teachers and hobbyists to express themselves through various media, including video. We built Cloudflare Stream for these platforms, enabling them to rapidly build video use cases without needing to build and maintain a video pipeline at scale.

At its heart, every creator platform must manage ownership of user-generated content. When a video is uploaded to Stream, Stream returns a video ID. Platforms using Stream have traditionally had to maintain their own index to track content ownership. For example, when a user with internal user ID 83721759 uploads a video to Stream with video ID 06aadc28eb1897702d41b4841b85f322, the platform must maintain a database table of some sort to keep track of the fact that Stream video ID 06aadc28eb1897702d41b4841b85f322 belongs to internal user 83721759.

With the introduction of the creator property, platforms no longer need to maintain this index. Stream already has a direct creator upload feature to enable users to upload videos directly to Stream using tokenized URLs and without exposing account-wide auth information. You can now set the creator field with your user’s internal user ID at the time of requesting a tokenized upload URL:

curl -X POST "https://api.cloudflare.com/client/v4/accounts/023e105f4ecef8ad9ca31a8372d0c353/stream/direct_upload" \
     -H "X-Auth-Email: user@example.com" \
     -H "X-Auth-Key: c2547eb745079dac9320b638f5e225cf483cc5cfdda41" \
     -H "Content-Type: application/json" \
     --data '{"maxDurationSeconds":300,"expiry":"2021-01-02T02:20:00Z","creator": "<CREATOR_ID>", "thumbnailTimestampPct":0.529241,"allowedOrigins":["example.com"],"requireSignedURLs":true,"watermark":{"uid":"ea95132c15732412d22c1476fa83f27a"}}'

When the user uploads the video, the creator property would be automatically set to your internal user ID and can be leveraged for operations such as pulling analytics data for your creators.

Setting the creator property on your video uploads is just the beginning. You can now filter Stream Analytics via the Dashboard or the GraphQL API using the creator property.

Filter Stream Analytics in the Dashboard using the Creator property

Previously, if you wanted to generate a monthly report of all your creators and the number of minutes of watch time recorded for their videos, you’d likely use a scripting language such as Python to do the following:

1. Call the Stream GraphQL API requesting a list of videos and their watch time

2. Traverse through the list of videos and query your internal index to determine which creator each video belongs to

3. Sum up the video watch time for each creator to get a clean report showing you video consumption grouped by the video creator

The creator property eliminates this three step manual process. You can make a single API call to the GraphQL API to request a list of creators and the consumption of their videos for a given time period. Here is an example GraphQL API query that returns minutes delivered by creator:

query {
  viewer {
    accounts(filter: { accountTag: "<ACCOUNT_ID>" }) {
      streamMinutesViewedAdaptiveGroups(
        limit: 10
        orderBy: [sum_minutesViewed_DESC]
        filter: { date_lt: "2022-04-01", date_gt: "2022-04-31" }
      ) {
        sum {
          minutesViewed
        }
        dimensions {
          creator
        }
      }
    }
  }
}

Stream is focused on helping creator platforms innovate and scale. Matt Ober, CTO of NFT media management platform Piñata, says "By allowing us to upload and then query using creator IDs, large-scale analytics of Cloudflare Stream is about to get a lot easier."

Read the docs to learn more about setting the creator property on new and previously uploaded videos. You can also set the creator property on live inputs, so the recorded videos generated from the live event will already have the creator field populated.

Being able to filter analytics is just the beginning. We can’t wait to enable more creator-level operations, so you can spend more time on what makes your idea unique and less time maintaining table stakes infrastructure.