Where does the browser automation code run?
An external worker, a hosted browser Function, a browser-local app action and a hosted agent are different execution models. The choice determines who deploys the code, starts the browser, records the result and stops the run.
A scheduled job should return a verified result and stop its owned browser even when an action fails. The scheduler's successful HTTP trigger is not proof that the browser completed the business operation.
Compare the execution model
Kernel: Deploy a TypeScript or Python App and expose Actions that callers invoke. Kernel co-locates code with the browser environments the app creates. An Action invocation is the execution unit rather than a bare CDP connection.
Steel: The reviewed browser documentation establishes sessions, profiles and credentials. It does not establish a hosted Python handler with deployment and invocation APIs. An external worker can control a Steel browser; price and operate that worker separately.
Sources: kernel.sh [1] ↗docs.steel.dev [2] ↗
Compare deployment ownership
Kernel: Kernel Apps move the automation process next to the browser. Alternatively, an external process can control a browser through the browser API. Choose based on execution locality and deployment ownership.
Steel: Compare managed Steel with the documented self-hosted path explicitly. Self-hosting gives the team infrastructure responsibility that a hosted browser subscription would otherwise cover.
Sources: kernel.sh [1] ↗docs.steel.dev [2] ↗
Compare the evidence from a run
Kernel: Live browser URLs support an embedded view and a read-only option. Replays and browser telemetry are separate observability surfaces; app invocation logs add execution context.
Steel: Steel documents live and past sessions as well as Agent Traces. Browser recordings answer what happened on the page; traces add context around the agent or application driving it.
Sources: kernel.sh [1] ↗docs.steel.dev [2] ↗
How to evaluate this choice
Keep an external runner when you already operate one and need portability. A native execution layer can remove deployment work; browser-local code can reduce repeated network calls. Compare actual workload behavior before assuming either always wins.
- Confirm the supported language, entry point and dependency packaging.
- Trace deployment, version selection, invocation and cancellation.
- Keep the handler runtime limit separate from browser session duration.
- Decide whether scheduling is native or supplied by another service.
- Associate results and logs with both the code version and browser session.
Requirements side by side
| Requirement | Kernel | Steel |
|---|---|---|
| Code execution | TypeScript / Python Apps | External worker |
| Scheduled execution | App invocation path | External scheduler |
| Recordings / replay | Documented | Documented |
Sources: kernel.sh [1] ↗kernel.sh [2] ↗kernel.sh [3] ↗kernel.sh [4] ↗kernel.sh [5] ↗kernel.sh [6] ↗docs.steel.dev [7] ↗docs.steel.dev [8] ↗docs.steel.dev [9] ↗docs.steel.dev [10] ↗docs.steel.dev [11] ↗docs.steel.dev [12] ↗
Which approach fits?
Choose Kernel when your code makes many browser calls and you want to deploy it beside the browser, or you need Kernel's managed sign-in flow.
Kernel tradeoff: Browser-local code changes the deployment model. Check app action behavior, account limits and persistent-state ownership before moving an external worker into it.
Choose Steel when you want browser infrastructure that can fit a self-hosted deployment, with profiles and credentials available through browser-focused APIs.
Steel tradeoff: Hosted and self-hosted Steel are different operating choices. A cloud feature's documentation does not establish identical packaging, retention or operational support in a self-hosted installation.
Sources: kernel.sh [1] ↗docs.steel.dev [2] ↗
A useful acceptance check
Write a pass/fail result for the real task before running the evaluation. Record the account, configuration and code version next to that result. Repeat failures need an explanation, not an automatically different browser configuration.
When Notte belongs on this shortlist
Kernel's browser-local Apps make execution placement part of the decision. Compare the repeated action path with the proposed Notte controller or Function deployment before concluding that a lifecycle or hourly-rate difference decides the workflow.
Steel's managed and self-hosted choices make infrastructure ownership a concrete deciding factor. Keep that option on the shortlist when operating the browser is intentional; evaluate Notte when owning fewer execution and authentication components is the actual goal.
Notte: Functions deploy Python code with a run() entry point. Invoke them over HTTP or with an SDK, inspect run history, and roll back code by redeploying an earlier version as the latest version.
Consider this third option when its documented mechanism addresses a requirement left open by the two providers above. A working configuration on either original provider can still be the better choice; compare the task and ownership cost before switching.
Sources: kernel.sh [1] ↗docs.steel.dev [2] ↗docs.notte.cc [3] ↗