TL;DR
- Python backends are now generally available on Vercel with support for FastAPI, Flask, Django, and PyPI dependencies, used by 150,000 teams including OpenAI and Polymarket
- Container support in Vercel Functions enables deployment of applications in any language with OS libraries and executables, with 5,000 teams shipping 150,000 container deployments since Public Beta
- Vercel Daemons, now in Public Beta, deploys containers as continuously running background workers on Fluid compute that pull work from queues and databases and restart on failure
- Containers deploy as Functions on Fluid compute with Active CPU pricing that charges only for active processing, not idle time waiting for I/O or model inference
- Python Status
- Generally Available
- Daemons Status
- Public Beta
- Teams Using Python
- 150,000+
What happened
Vercel announced three expansions to its backend platform at Vercel Ship in San Francisco. Python on Vercel is now generally available, moving beyond the zero-config support announced last year. Container support in Vercel Functions, which entered Public Beta previously, now enables developers to deploy applications written in any language with custom runtimes and system libraries. Additionally, Vercel introduced Daemons in Public Beta, a new service for deploying containers as continuously running background workers.
Python backends now GA
Developers can now build production Python backends on Vercel using standard CPython with PyPI dependencies. Vercel handles framework detection, builds, and scaling for frameworks such as FastAPI, Flask, and Django. The platform provides the same development experience as frontend deployments, including local development, previews, and production deployments. Already, 150,000 teams run Python backends on Vercel, with deployments from organizations including OpenAI, Polymarket, and Exa. Python SDKs enable integration with other Vercel products: Blob for file storage, Queues for background jobs, Workflows for durable step coordination, Sandbox for running untrusted code, and the AI SDK for Python for streaming model responses and tool calling. Delphi, a company building AI digital minds, migrated its Python backend from AWS ECS to Vercel; with ten engineers and no dedicated infrastructure role, the team deploys to production more than 100 times daily and uses Queues to handle background work.
Container runtime for any language
The Vercel container runtime allows developers to package applications in any language with the OS libraries and executables required to run them. Since entering Public Beta, 5,000 teams have shipped 150,000 container deployments on Vercel. Containers deploy as Vercel Functions on Fluid compute, scaling with traffic and using Active CPU pricing, which charges only for active processing time and does not charge for idle periods spent waiting for API responses, database queries, or LLM inference. Vercel Services enables frontend and backend development and deployment alongside each other. The Vercel CDN serves traffic on developer domains, Vercel WAF protects it, logs assist with investigation, and rollbacks restore earlier deployments. When deployed through Git or the Vercel CLI, Vercel builds the container image and stores it in the Vercel Container Registry.
Daemons for background workers
Vercel Daemons, now in Public Beta, enables deployment of containers as continuously running background workers on Fluid compute. Developers specify the number of instances to run in each region, and Vercel maintains that capacity by restarting processes when they exit or crash. Daemons launches with support for pull-based workloads, where code requests work from another system and processes it as it becomes available, such as consuming messages from Vercel Queues or fetching pending jobs from a database. In a video processing example, a Daemon runs a Python processor continuously while pulling jobs from Vercel Queues. Configuration is defined in vercel.toml, where developers specify the Next.js frontend, route requests, and add the processor using a Dockerfile.
[ services.frontend ]
framework = "nextjs"
root = "."
[ [ routes ] ]
src = "/(.*)"
destination.service = "frontend"
[ daemons.videos ]
root = "."
entrypoint = "Dockerfile"
instances.iad1 = 1FROM python:3.12-slim
RUN apt-get update \
&& apt-get install -y --no-install-recommends ffmpeg \
&& rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY processor.py .
CMD [ "python" , "processor.py" ]What to do
To start using Python backends, visit the Python knowledge base for framework details and examples. For container deployments, follow the containerized Functions deployment guide. To add background workers using Daemons, consult the Daemons deployment guide. Developers can also deploy eve agents with Python in one click using Vercel's integration.
Source
Vercel Bloghttps://vercel.com/blog/backends-on-vercel-python-containers-and-daemonsAI-assisted, verified against the source. This post was drafted with AI from the primary source linked above and checked automatically against it. For anything you depend on, read the original.
- #backend
- #python
- #containers
- #vercel
- #infrastructure
