Vercel Daemons
A Vercel platform feature for long-running process workloads that keeps containers running for extended periods and packages Python code together with its third-party dependencies and system-level libraries. It is suited to long-running workers that pull tasks from a queue or database.
Timeline (last 90 days)
Vercel Daemons has entered public beta; it is a continuously running background Worker solution that supports deploying code or container images and setting the number of instances, with Vercel responsible for keeping them running and replacing them on failure, and Celery, Dramatiq, Temporal Worker, or existing processes can be connected directly.
Vercel's compute forms are divided into two lines: request-driven applications scale elastically through Fluid compute, while long-running pull-based workloads are handled by Vercel Daemons.
Configuring Vercel Daemons requires packaging the application and its dependencies in a Dockerfile or Containerfile, and adding the relevant configuration (such as specifying root, entrypoint, command, and replicas) to the daemons entry in the configuration file at the project root.
Vercel Daemons supports running Celery workers, and the official example is running a Celery worker in worker/Dockerfile and deploying it to the iad1 region.
Vercel Daemons' supported scenarios include: clients consuming Vercel Queues in polling mode, Celery, BullMQ, RabbitMQ, Kafka consumers, Temporal or Cadence long-polling workers, and providing self-hosted CI runners for GitHub Actions, GitLab, or CircleCI.
Vercel Daemons' output appears in the project's Logs, and CPU and memory metrics can be viewed through Observability Plus.
Whether Vercel Daemons is suitable for carrying the background processing of core business still depends on specific reliability requirements and cost considerations.
Vercel Daemons has entered public beta, allowing developers to run long-running background processes on Vercel without wrapping tasks in an HTTP server.
Vercel Daemons is not triggered by inbound HTTP requests; instead, it runs continuously as a resident container and can actively pull tasks from Vercel Queues, databases, or third-party systems.
Vercel Daemons resource billing is closer to that of a resident container, calculated by number of instances and runtime, rather than by number of invocations.
4 more timeline events
All Facts (14)
Vercel Daemons has entered public beta, allowing containers to be deployed as continuously running background worker processes on Fluid compute, with support for specifying the number of instances per region and automatically maintaining capacity and restarting crashed processes.
60%UnverifiedVercel's compute forms are divided into two lines: request-driven applications scale elastically through Fluid compute, while long-running pull-based workloads are handled by Vercel Daemons.
50%UnverifiedConfiguring Vercel Daemons requires packaging the application and its dependencies in a Dockerfile or Containerfile, and adding the relevant configuration (such as specifying root, entrypoint, command, and replicas) to the daemons entry in the configuration file at the project root.
50%UnverifiedVercel Daemons resource billing is closer to that of a resident container, calculated by number of instances and runtime, rather than by number of invocations.
50%UnverifiedVercel Daemons supports running Celery workers, and the official example is running a Celery worker in worker/Dockerfile and deploying it to the iad1 region.
50%UnverifiedVercel Daemons' supported scenarios include: clients consuming Vercel Queues in polling mode, Celery, BullMQ, RabbitMQ, Kafka consumers, Temporal or Cadence long-polling workers, and providing self-hosted CI runners for GitHub Actions, GitLab, or CircleCI.
50%UnverifiedVercel Daemons' output appears in the project's Logs, and CPU and memory metrics can be viewed through Observability Plus.
50%UnverifiedWhether Vercel Daemons is suitable for carrying the background processing of core business still depends on specific reliability requirements and cost considerations.
50%UnverifiedVercel Daemons is not triggered by inbound HTTP requests; instead, it runs continuously as a resident container and can actively pull tasks from Vercel Queues, databases, or third-party systems.
50%UnverifiedVercel Daemons keeps containers running for long periods and packages Python code together with the third-party packages and system-level libraries it depends on, making it suitable for long-running workers that pull tasks from a queue or database.
50%UnverifiedDuring the Vercel Daemons public beta, only pull-based workloads are supported, that is, code actively requesting tasks from other systems and processing them, such as consuming queue messages or pulling pending jobs from a database.
50%UnverifiedVercel Daemons configuration is written in vercel.toml, where you can define service routing and declare a daemon and the number of instances per region through the Dockerfile.
50%UnverifiedVercel Daemons has entered public beta; it is a continuously running background Worker solution that supports deploying code or container images and setting the number of instances, with Vercel responsible for keeping them running and replacing them on failure, and Celery, Dramatiq, Temporal Worker, or existing processes can be connected directly.
50%UnverifiedVercel Daemons has entered public beta, allowing developers to run long-running background processes on Vercel without wrapping tasks in an HTTP server.
50%