Cloudflare has announced the release of Streamline, a new developer playground and open-source architecture that demonstrates how developers can build custom, bespoke video processing pipelines directly on Cloudflare’s Developer Platform. While Cloudflare Stream has long operated as a powerful, out-of-the-box broadcasting platform for a wide variety of customers, advanced use cases—such as rendering dynamic annotations on live streams or generating alternate video versions with burned-in subtitles—traditionally required external media infrastructure. Streamline bridges this gap by illustrating how developers can leverage Cloudflare Workers, Containers, and modern media protocols to modify video streams in real-time and instantly publish the resulting output as a new livestream or hosted video.
Bridging the Gap for Custom Media Workflows
Processing video streams efficiently requires a durable, long-running computational environment capable of executing specialized, compiled code with predictable memory and CPU allocations. Because video streams often run continuously for minutes or even hours, the underlying media processes must maintain a lifecycle completely independent of the initial web request that triggered them. Modern web applications require the flexibility to start a processing pipeline, transmit input data, inspect the ongoing process, and eventually shut it down without keeping a single HTTP connection open for the entire duration of the stream.
Cloudflare’s suite of developer primitives provides the foundational blocks necessary to solve this architectural challenge. Containers offer long-lived runtimes ideally suited for intensive media processing tasks, while Durable Objects facilitate precise session orchestration. Furthermore, Cloudflare Workers act as the central nervous system for control signaling and monitoring. By combining these tools, developers can build robust media engines that operate independently of transient client connections.
Core Architecture of a Streamline Deployment
A standard Streamline deployment is divided into two primary architectural components: the Media Engine, which is responsible for media input, output, and processing, and the controlling Application, which creates, configures, monitors, and terminates media sessions.
The Media Engine is hosted inside a container and manages all real-time media ingestion and distribution. It possesses the capability to pull RTMPS playback streams over the network from a Stream Live input and publish the processed RTMPS output back to another Stream Live input. Additionally, it can ingest video-on-demand content by pulling Cloudflare Stream HLS manifests and their corresponding segments, or accept direct video feeds supplied by the controlling application, such as live webcam feeds. To provide real-time feedback, the engine can also publish preview video feeds over an outbound WebSocket connection to a Durable Object relay, allowing monitoring interfaces to connect seamlessly.
The controlling application, built primarily using Cloudflare Workers, can take the form of a full-stack browser application, an automated agent, or an embedded internet-of-things system. During local development, the architecture simplifies significantly: the container runs as a local Docker instance without requiring Durable Objects, and authentication is bypassed to allow direct local connections. However, when deployed to production on Cloudflare, authorized users or automated agents can interact with the Worker API to spin up isolated container instances, manage their lifecycles automatically, and route media inputs and outputs directly through Cloudflare Stream.
Container Lifecycle and Session Management
Managing long-running media sessions on a serverless-adjacent platform requires specialized lifecycle controls. When a controlling Worker application initiates a media processing session, the underlying container continues to execute processing tasks even if the user or application temporarily disconnects and reconnects. To prevent abandoned sessions from running indefinitely, the system enforces a maximum duration limit, ensuring that every session is eventually terminated. While a media session is actively processing, that specific container instance remains dedicated to the task and is unavailable for other workloads.
Standard Cloudflare Containers are designed to scale down to zero and enter a sleep state automatically if they do not receive incoming requests within a designated time window. For video pipelines, however, processing must persist even in the complete absence of incoming requests. To achieve this, developers implement custom logic by overriding the activity expiration callback on the container. As long as a session remains valid and active, the container periodically renews its activity timeout; otherwise, it safely destroys itself to free up system resources.
Defining and Executing Video Processing Pipelines
Streamline exposes a streamlined, abstract API that shields developers from the low-level complexities of the underlying Go-based media harness and Durable Object storage. Through packages exported by Streamline, applications can instantiate sessions and trigger pipelines using structured JSON configuration objects.
These configurations define the precise sequence of operations applied to a video stream. For instance, developers can configure a pipeline to ingest an RTMP broadcast from a live stream feed, apply a semi-transparent overlay image, and re-encode the stream before sending the modified output to another RTMP destination for recording or broadcasting. Similar pipelines can be constructed to ingest video-on-demand content via HTTP Live Streaming (HLS), automatically extract embedded closed caption subtitles, render them as visible text burned directly into the video frames, and broadcast the resulting subtitled video in real time.
For rapid prototyping and development, applications can also stream video data directly into Streamline from local sources such as webcams or automated camera feeds. This proves particularly valuable for scenarios involving computer vision analysis or the composition of multiple camera angles into a single unified view. By utilizing WebSockets for low-latency preview delivery, developers can inspect the output of their custom pipelines instantly as fragmented MP4 data is pushed from the container through the Durable Object relay.
Security Considerations and Isolation
Because media infrastructure often handles sensitive broadcast keys and proprietary content, security is integrated into the core design of Streamline rather than treated as an afterthought. The system ensures that only verified and authorized users can create new sessions or take control of existing ones, and that individual sessions remain strictly isolated from one another. Stream RTMPS input and output keys are treated as critical secrets, stored securely as Worker secrets or write-only shared overrides within Durable Object storage, and are never exposed to browser storage or returned through settings APIs.
Access to the deployment is strictly gated using Cloudflare Access integration. The Worker verifies the identity of the principal before accepting any control requests, binding the active session exclusively to that verified user. Furthermore, resource consumption is bounded by strict concurrency, media, and session limits, preventing any single user from monopolizing system capacity or interfering with another user’s active workflows.
Open Source Release and Future Directions
Cloudflare has released both the Streamline container runtime and the example Worker application as open-source repositories on GitHub, accompanied by a fully functional public playground deployment. The released example application features an Astro-based web frontend demonstrating common use cases such as visual filters, image overlays, subtitle rendering, and picture-in-picture effects, complete with performance metrics and system tracing tools for debugging.
The introduction of Streamline highlights the immense potential of combining managed media services like Cloudflare Stream with low-level developer primitives. While the initial release relies on container CPU capacity for video processing—which can introduce bottlenecks at higher resolutions or frame rates—Cloudflare plans to work alongside the developer community to expand the architecture. Future explorations may include support for computer vision pipelines, hardware-accelerated media processing, ultra-low-latency real-time experiences utilizing next-generation protocols like WebRTC and Media over QUIC (MoQ), and ultimately native video encoding and decoding primitives within Cloudflare Workers.
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