Delivering an ONNX model through a CDN is an artifact distribution problem with a runtime compatibility problem attached. The CDN transfers bytes toward the client. ONNX Runtime loads the model and executes inference in the environment where the application runs. Faster transfer can improve startup, but it does not establish inference speed or model quality.
This deployment guide uses a browser image classifier as a running example. The proposed release process also applies to other client inference tasks: package compatible assets, publish them immutably, verify the download, activate a complete release, and preserve a tested recovery path. Our model delivery overview introduces the broader lifecycle.
1. Specify the complete release contract
Start with everything required to turn an image into a label. That may include application code, the ONNX graph, external weight files, runtime assets, preprocessing rules, label mappings, and model metadata. A new label order paired with an old model can produce plausible but incorrect results even when every file downloads successfully.
The official ONNX Runtime Web deployment guide identifies JavaScript, WebAssembly, and model files as deployment assets. It also explains that models using external data need that information supplied to the runtime. Use that asset inventory as a starting point, then add the application-specific files your own inference pipeline requires.
Define the contract for input dimensions, normalization, tensor types, output interpretation, and supported runtime configuration. Include a small set of known test inputs with expected properties or tolerances. Treat those checks as release acceptance criteria. Download success and session creation are useful signals, but neither proves that the application is interpreting predictions correctly.
2. Give every artifact an immutable identity
Publish release files under unique paths, such as a release identifier combined with a content digest. Once published, the bytes at that path must remain unchanged. Uploading a corrected file over an existing version makes cache behavior unpredictable and destroys the connection between an incident report and the artifact a user received.
Create a manifest that binds the release together. Include the release identifier, application compatibility range, exact runtime build, model and auxiliary asset locations, byte lengths, cryptographic checksums, input and output contracts, and supported execution configurations. Keep a release-specific manifest immutable as well.
A separate, small release pointer can identify which manifest a client should consider next. For example, a stable channel can point at release A while a test cohort receives release B. This separates discovery from artifact identity. The CDN and machine learning guide explains how distribution policy fits around a model lifecycle without replacing the model's execution environment.
3. Verify assets before making them discoverable
Upload all files before changing the release pointer. Fetch each published artifact through the delivery path, verify its length and checksum, and initialize a session using the same asset mapping that production clients will use. Test external weight files and supporting assets individually so a complete graph file does not disguise an incomplete release.
Specify exactly which bytes a checksum covers. A practical convention is the artifact bytes the application receives after HTTP content decoding. Apply the same convention in the build system and client verifier. Reject a mismatch and preserve the working release; do not try to continue with partially verified weights.
A checksum detects disagreement with an expected digest. Authenticity also depends on where that expectation comes from. If an attacker can replace both the file and its manifest, a matching checksum alone is insufficient. Anchor trust in a trusted application release or an appropriately verified signed manifest. The edge AI security guide develops these trust boundaries.
4. Use different cache policies for files and pointers
Cache immutable artifacts for reuse
Assign versioned artifacts a long freshness period when their access policy permits shared caching. Ensure cache keys distinguish any representation that actually changes the bytes. Verify the delivery configuration rather than assuming a filename automatically makes a response cacheable. Browser storage and CDN storage are separate layers with separate eviction and access behavior.
Keep release discovery deliberately fresh
Give the mutable release pointer a short freshness policy or require revalidation according to your update requirements. Set a maximum acceptable staleness. A client that keeps receiving an old pointer may continue running an old model after the deployment dashboard says an update is complete.
Restrict access before serving protected artifacts from shared caches, and verify the authorization path on both hits and misses. For public models, avoid adding per-user variation that needlessly fragments identical file delivery. Never place private credentials in the manifest. A model downloaded into a user's browser should not be treated as a secret from that device's owner.
5. Test the browser and runtime combination
Pin the JavaScript runtime and its WebAssembly files to the same build. Configure explicit asset locations when deployment moves them away from the JavaScript bundle; ONNX Runtime Web provides ort.env.wasm.wasmPaths for that purpose. Test the final hosted application, because a development server may resolve paths that fail after a production build.
Build a compatibility matrix covering supported browsers, device classes, model operations, memory requirements, and execution providers. Verify the chosen accelerated path and an acceptable fallback separately. Feature detection is useful, but successful detection does not establish that a particular model can initialize and run within the device's available resources.
Review secure-context requirements, cross-origin requests, worker loading, and the application's Content Security Policy together. The deployment documentation describes additional worker-loading considerations when assets come from another origin. Exercise the exact policy and hosting arrangement that users receive. The edge AI versus cloud AI comparison explains when an incompatible client should use an authorized remote route or a reduced local feature.
6. Activate a release only after it is ready
Use explicit client states: discovered, downloading, verified, initialized, and active. A failed transition should leave the previous active release usable whenever the product's policy allows it. Download into a staging area, check every required asset, then create the new inference session and run a small acceptance check.
Switch the active release only after preparation succeeds. Avoid replacing label mappings while a request still uses the old model session. Bind each in-flight prediction to one release contract, and release obsolete resources when they are no longer needed. Plan for the temporary memory and storage cost of keeping old and new packages together.
Handle storage exhaustion and interrupted downloads explicitly. Browser storage can be unavailable or cleared; offline readiness should reflect the package that is actually present. Let users retry a failed update without restarting a completed download unnecessarily, while preserving integrity checks for the assembled artifact.
7. Rehearse rollback and observe actual adoption
A rollback changes the release pointer to a known working manifest. It works only if the older assets still exist, the application remains compatible with them, and clients are allowed to adopt that release. Retain the necessary files and test the transition before relying on it during an incident.
Separate planned rollback from accepting arbitrary old metadata. If signed manifests use freshness or sequence checks to resist replay, design an explicit authorized rollback mechanism that those checks permit. Record why a rollback occurred and which release becomes active. An old cached pointer should not be mistaken for an approved recovery action.
Measure discovery, download, verification, initialization, activation, and inference failures by release and environment. Track actual active versions to reveal stale clients. Our analytics guide helps define these events. Report model transfer time separately from initialization and prediction time so an improvement in CDN delivery is not presented as an improvement in computation.
Conclusion: deploy a reproducible package
A reliable ONNX deployment lets you identify exactly which assets a client received and why they were activated. Immutable files, a verified manifest, explicit compatibility tests, and a fresh release pointer create that record. Preserve complete working releases, exercise rollback, and measure each stage independently. The result is a model update process that can recover from failed transfers, incompatible runtimes, and unsuccessful releases without guessing which bytes are in use.
