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Use the Vercel primitive that matches the job’s failure and runtime requirements. A regular Vercel Function is still bounded: Fluid Compute gives every plan a 300-second default, Hobby tops out at 300 seconds, and Pro and Enterprise reach 800 seconds. Supported Node.js and Python functions have an 1,800-second (30-minute) extended-duration beta. Use waitUntil only for short work that can finish during the invocation lifecycle. Use Workflow when state, retries, and recovery must survive pauses or crashes. Use Queues when producers and consumers need buffering and at-least-once delivery. Use a Vercel Daemon when a container must stay running and continuously pull work without an HTTP request.
What “always-on” means on Vercel
There are three different ideas that are often conflated:
- Long request execution: one Function invocation runs for a bounded number of seconds and then must exit.
- Durable background execution: a job can pause, retry, resume, or recover after a crash or deployment because its state is stored outside the process.
- Process continuity: a worker container remains available to poll a queue or perform scheduled work without waiting for an incoming HTTP request.
A function that contains an infinite loop is not an always-on worker. It eventually reaches the plan’s maximum duration, and any in-memory progress disappears when the invocation ends. Conversely, a continuously running Daemon is not automatically a durable job system: the process can still be restarted, so important progress belongs in a persistent store or queue.
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Vercel’s 2025 Fluid Compute documentation describes the limits and lifecycle features below. Check the current limits for your account before committing to an architecture, especially where the documentation labels a feature as beta.
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Choose the primitive by runtime, durability, and process model
| Requirement | Recommended primitive | What it provides | Important boundary |
|---|---|---|---|
| The job finishes within five minutes on any plan | Fluid Compute Function | Bounded request execution with a 300-second default and Hobby maximum | The handler must return before the applicable limit |
| The job needs up to 800 seconds on Pro or Enterprise | Function with a route-level maxDuration |
A larger per-invocation ceiling | 800 seconds is the documented maximum for those plans |
| A supported Node.js or Python job needs up to 30 minutes | Extended-duration Function beta | An 1,800-second per-function limit | Availability is limited to supported runtimes and the beta |
| A small task should continue after an HTTP response | waitUntil |
Post-response work within the function lifecycle | It is not an unlimited worker or durable job store |
| A multi-step job needs retries, pause/resume, and crash recovery | Workflow | Retained state, automatic retries, and durable orchestration | Design each step so it can safely be retried |
| Producers and consumers need buffering and fan-out | Queues with consumers | Durable topics, retries, and at-least-once delivery | Consumers must tolerate duplicate deliveries |
| A process must continuously poll or consume work | Daemon | A continuously running container on Fluid Compute, including operation without an HTTP server | Persist job state; process continuity alone is not crash-proof storage |
| You need a complete HTTP server or custom backend process | Vercel Services | A backend deployment model for HTTP servers and background workers | Choose it when a Function or Daemon’s model does not fit |
The table is a selection tool, not a promise that every feature has identical pricing or availability. The supplied Vercel documentation establishes the runtime and behavior limits, but does not state a universal price for these primitives.
Start with a bounded Function when the work really fits
Fluid Compute’s documented default execution time is 300 seconds (five minutes) on every plan. Hobby has a 300-second maximum. Pro and Enterprise can reach 800 seconds when the route is configured accordingly. A supported Node.js or Python runtime may use the 1,800-second (30-minute) extended-duration beta. These are per-invocation ceilings, not a way to reserve a machine indefinitely.
Set the ceiling deliberately
For a route whose worst-case execution time is known, set a route-level maxDuration that is no higher than the plan and runtime allow. Keep a margin for startup, network calls, and cleanup rather than setting the value equal to an optimistic average. The exact configuration location depends on the framework and route type; use the route’s Vercel configuration mechanism and verify the resulting deployment settings.
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export const maxDuration = 300;Treat that snippet as a configuration pattern, not a substitute for checking your framework’s current syntax. A setting cannot exceed the plan’s ceiling, and it cannot make an unbounded loop safe.
Keep the handler’s contract explicit
- Validate input before starting expensive work.
- Return a response within the configured limit.
- Use a job identifier when the caller does not need to wait for completion.
- Record progress or an outcome before the invocation exits.
- Make external side effects idempotent so a timeout or client retry does not apply them twice.
If the function never returns, Vercel can terminate it at the applicable maximum. The resulting timeout is a platform limit, not evidence that the code is still running safely in the background.
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Use waitUntil for short post-response work
Fluid Compute supports an extended function lifecycle through waitUntil. Vercel documents it for tasks such as logging, analytics, and database updates that can continue after the client has received a response.
The lifecycle looks like this:
- Receive and validate the request.
- Perform the work required to produce the response.
- Schedule a bounded follow-up task with
waitUntil. - Return the response immediately.
- Allow the function invocation to finish the scheduled task within its lifecycle and duration limit.
// Illustrative pattern; import and API shape depend on your runtime.
waitUntil(recordAnalytics({ requestId }));
return new Response(JSON.stringify({ accepted: true }), {
headers: { "content-type": "application/json" }
});Do not use this pattern for a video transcode, an unbounded crawler, or a job that must survive a process crash. The promise is still associated with the invocation. If the function is terminated, paused, or otherwise loses its execution context, in-process state is not a durable queue. Persist a job record and use a durable primitive when completion matters.
Use Workflow when completion must survive failures
Vercel describes Workflow as the durable option for work that exceeds a Function’s duration. Its documented capabilities include pause and resume, retained state, automatic retries, and crash-safe execution. That makes it suitable for multi-step jobs such as importing data, waiting for an external approval, calling several APIs, and then publishing a result.
Model the job as recoverable steps
- Create a durable job record. Store the caller, input reference, current status, and a stable job identifier before launching work.
- Split the operation at failure boundaries. A step should represent one meaningful unit, such as fetching a page, transforming a batch, or writing a final artifact.
- Persist outputs between steps. Do not rely on local variables surviving a pause or retry.
- Define retry behavior. Transient network and rate-limit failures can be retried; permanent validation errors should become a terminal state.
- Make side effects idempotent. Use a job-step key, unique constraint, or upsert so a retried step cannot charge, publish, or enqueue the same effect twice.
- Expose status to the caller. Return the job identifier quickly and let a status endpoint, webhook, or UI read the persisted state.
Workflow is the right answer when the question is “How do I guarantee that this multi-step job eventually reaches a known state?” It is not merely a longer timeout setting.
Add a Queue when producers and consumers need separation
Vercel’s queue guidance describes durable topics, retries, and consumer fan-out, with at-least-once delivery. A queue is useful when requests arrive in bursts, when several workers should share work, or when the producer should not wait for a slow consumer.
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- The request handler validates the payload and writes a message containing a stable job ID and the minimum data needed to process it.
- The handler returns an acceptance response rather than holding the HTTP request open.
- One or more consumers receive messages and perform the work.
- The consumer acknowledges a message only after its side effects and progress record are safely committed.
- A failed delivery is retried according to the queue policy; a poison message is isolated for inspection instead of being retried forever.
At-least-once delivery means duplicate messages are possible. Use a deduplication key or an idempotency table, and make “already completed” a successful no-op. A queue supplies buffering and delivery semantics; it does not by itself define the business workflow, long-term result storage, or user-facing status model.
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Run a Vercel Daemon for a continuously running worker
Vercel’s official backend documentation states: “Vercel Daemons lets you deploy containers as continuously running background workers on Fluid compute.” The same documentation presents Daemons as able to pull work from queues or other systems without an HTTP server.
When a Daemon is the direct match
- The process should poll or consume continuously rather than start only for an HTTP request.
- The worker needs a custom runtime, long-lived connections, or a loop that is awkward to express as one Function invocation.
- Work arrives through a queue or another external system and does not need an inbound web route.
- You can package the worker as a container and implement graceful shutdown and restart handling.
What a Daemon does not guarantee
“Continuously running” describes the worker process, not immutable uptime or permanent in-memory state. Deployments, failures, maintenance, and platform scheduling can restart a container. A Daemon should therefore:
- Read pending work from a durable queue or database on startup.
- Checkpoint progress after each safe unit of work.
- Handle termination signals by stopping new intake, finishing or releasing the current item, and closing connections.
- Use bounded polling delays and backoff so an empty queue does not create a tight loop.
- Emit structured logs containing the worker ID, job ID, attempt number, and outcome.
For a worker that only needs process continuity, a Daemon is more natural than keeping an HTTP Function open. For a job whose state must survive every restart and retry, combine the Daemon with a queue and durable job records, or use Workflow for orchestration.
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Use Vercel Services for a full backend process
Vercel’s backend documentation presents Services for HTTP servers and background workers. Choose this model when you need a custom server process, multiple internal endpoints, or a backend that does not map cleanly to a request-scoped Function or a queue-consuming Daemon.
The same reliability rules still apply: externalize important state, make shutdown safe, bound retries, and expose health and readiness information. A custom server is a deployment model, not a replacement for durable storage or job semantics.
A practical implementation plan
- Measure the workload. Record typical and worst-case runtime, memory and network behavior, whether the operation can be retried, and whether it must continue through a deployment or process failure.
- Choose the shortest viable primitive. Keep sub-five-minute work in a Function. Move post-response housekeeping to
waitUntil. Use Workflow for durable multi-step state, Queues for buffering and fan-out, and a Daemon for continuous consumption. - Separate acceptance from completion. The HTTP route should validate, create a job ID, enqueue or start the durable work, and return. Do not make a browser connection wait for an operation that may exceed the invocation ceiling.
- Persist state before doing irreversible work. Store status values such as
queued,running,completed, andfailed, plus timestamps, attempt counts, and an error category. - Define idempotency. Pick a stable key for each business effect. A retry should detect an existing successful effect instead of creating a duplicate.
- Configure limits and retries. Set
maxDurationonly for bounded Functions. Set queue visibility, retry, and dead-letter behavior according to the failure modes of the job. For Workflow, distinguish retryable from terminal errors. - Deploy the consumer separately from the producer when useful. This allows request traffic and background throughput to scale and fail independently.
- Test interruption. Stop a worker during processing, redeploy it, delay an upstream API, deliver the same message twice, and force a timeout. Verify that the job resumes or reaches a clear failed state without duplicate side effects.
Design for retries, duplicates, and partial completion
The most damaging background-job bugs occur after a partial success: the payment was sent but the status write failed, or the file was uploaded but the completion message was lost. A robust design records an operation key and makes the external effect queryable or repeatable.
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- Use transactional boundaries where available. Commit the job-state transition and the local result together.
- Prefer upserts and unique constraints. They turn a replay into a deterministic update.
- Store checkpoints for large jobs. A batch number or cursor prevents restarting an entire import after one failure.
- Classify errors. Authentication failures, invalid input, and unsupported formats generally need operator or user action; timeouts and temporary service failures may be retried.
- Set a terminal policy. After a bounded number of attempts, mark the job failed and retain enough context to replay it safely.
These practices are engineering consequences of bounded execution and retry-based delivery: a process can stop between any two instructions, so completion must be represented outside that process.
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The function reaches exactly the plan limit
Check the route’s configured maxDuration, the deployed plan, and the runtime. Hobby cannot exceed 300 seconds. Pro and Enterprise can reach 800 seconds. The 1,800-second value is an extended beta for supported Node.js and Python runtimes, not a universal setting. If the job still exceeds the largest applicable limit, move it to Workflow, a queue consumer, or a Daemon.
The response returns but work is missing
Verify whether the task was registered with waitUntil and whether it can complete within the invocation lifecycle. For anything important, write a durable job record and use a queue or Workflow. A detached promise without durable state is not a recovery mechanism.
The worker runs twice
Assume duplicate delivery or a restart. Inspect the job ID and attempt number, then enforce idempotency at the database or external API boundary. Do not “fix” duplicates by ignoring retries; retries are how transient failures recover.
The queue appears empty while work is stuck
Inspect consumer acknowledgements, visibility or lease expiration, retry counts, and any dead-letter area. Confirm that the consumer records progress after the side effect, not before it. A message acknowledged too early can disappear even though the job did not finish.
Deployments interrupt a Daemon
That is expected for a process-based design. Ensure the worker handles shutdown, releases or renews its current lease, and reloads pending jobs on startup. Move checkpoints and final results out of memory.
Logs show no clear reason for failure
Log a correlation ID from the request through the queue, Workflow steps, or Daemon process. Include start and end timestamps, attempt number, duration, external dependency, and a sanitized error category. Monitor timeout errors specifically; Vercel documents that a function that never returns can reach the plan maximum and time out.
Decision checklist
- Can the worst case finish in 300 seconds? Use a Function unless durability or decoupling requires more.
- Does the caller only need an immediate acknowledgement while a small update finishes? Use
waitUntil. - Must the job pause, resume, retry, or recover with retained state? Use Workflow.
- Do you need burst absorption, multiple consumers, or at-least-once delivery? Add a Queue.
- Must a process continuously pull work without an HTTP server? Use a Daemon.
- Do you need a custom HTTP server or complete backend process? Consider Vercel Services.
- Can every side effect be retried safely, and is progress stored outside the process? If not, fix that before increasing a timeout.
The key distinction is bounded request execution versus durable orchestration versus a continuously running worker. Select the primitive from the job’s failure contract, not from the desire to keep one Function alive indefinitely.
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