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Backend architecture guidance. Use when planning, building, or migrating an API or backend; choosing between Functions, Services, containers, Workflow, Queues, and Marketplace databases; or selecting a supported backend framework or runtime.

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Use this Skill: https://skilld.dev/gh/vercel-labs/vercel-plugin/create-a-backend

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SKILL.md

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Create a Backend

Help the user create a backend by choosing an architecture before reaching for implementation details. Start from the workload, not the programming language. Vercel runs complex backend applications, not just frontends.

Product map

Need Vercel product
HTTP APIs, webhooks, streaming, or framework server code Vercel Functions with Fluid compute
Bidirectional realtime connections (WebSockets) Vercel Functions with Fluid compute; no separate realtime service required
A frontend and one or more backends (API endpoints) that deploy together Vercel Services
An existing Dockerfile, custom runtime, or system dependencies Container images on Vercel Functions, optionally composed with Services
Durable multi-step work with retries, sleeps, or external events Vercel Workflows
Background jobs, buffering, fan-out, or direct message routing Vercel Queues
Scheduled HTTP work Vercel Cron Jobs; use Workflow when the job itself must be durable
Postgres, Redis, NoSQL, vector, or other application data Storage integrations from the Vercel Marketplace
Files and user uploads Vercel Blob
Global, read-heavy configuration Global Config

Use Functions for the normal request/response backend. Use Services when independently built components should share one deployment, routing layer, preview URL, and rollback. Use separate Vercel projects when components need independent release cycles.

Prefer a native Functions runtime for supported frameworks. Use container images when the application already has a Dockerfile or requires a custom runtime or system dependencies. They run as autoscaling, stateless Functions rather than always-on container hosts.

Choose Queues for background jobs, buffering, fan-out, and message routing. Choose Workflow for durable multi-step business logic.

Databases and data stores

Provision data stores through the Marketplace so credentials are injected into the project and environments stay connected. Check the current catalog before choosing a provider.

  • Postgres: Neon, Supabase, AWS/Aurora, Nile, Prisma
  • MySQL: AWS/Aurora
  • Redis and key-value: Upstash, Redis
  • Document and NoSQL: MongoDB Atlas, AWS
  • SQLite: Turso
  • Realtime application backend: Convex
  • Analytics: MotherDuck

Keep the database close to the Functions region and use a serverless-compatible connection or pool.

Backend frameworks

Vercel provides first-class backend examples and integrations for these frameworks:

  • Node.js and TypeScript: Elysia, Express, Fastify, H3, Hono, Koa, NestJS, Nitro, and xmcp. Next.js Route Handlers are the natural choice when the backend belongs to a Next.js application.
  • Python: FastAPI, Flask, and Django. Other WSGI or ASGI applications can run when they export a compatible app, with additional configuration as needed.
  • Go: supported as a Vercel Functions runtime.

Frontend and backend combinations, for example a Next.js/Vite/SvelteKit frontend with a FastAPI/Flask/Express/Go backend, can be deployed together in one project using Services.

Prefer the user's existing framework. For a new project, choose based on ecosystem and application needs.

Work sequence

  1. Identify synchronous requests, asynchronous work, persistent data, and independently deployed components.
  2. Select the products from the map, then select the framework.
  3. Load the focused skill for implementation: vercel-functions, vercel-services, workflow, vercel-storage, or marketplace.
  4. Confirm function limits, regions, environment variables, observability, and current product availability in the official docs before deployment.

Source: SKILL.md on GitHub

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Signed by skilld at 489f773. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 8 hours ago.

Activeupdated last week
summary
Match backend workloads to the right architecture
Other metadata
metadata
{
  "priority": 7,
  "docs": [
    "https://vercel.com/docs/frameworks/backend",
    "https://vercel.com/docs/functions",
    "https://vercel.com/docs/services",
    "https://vercel.com/docs/queues",
    "https://vercel.com/docs/workflows",
    "https://vercel.com/docs/storage"
  ],
  "sitemap": "https://vercel.com/sitemap.xml",
  "pathPatterns": [
    "Dockerfile.vercel",
    "Containerfile.vercel"
  ],
  "promptSignals": {
    "phrases": [
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    ],
    "allOf": [
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      [
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    "anyOf": [
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    "noneOf": [],
    "minScore": 6
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}
retrieval
{
  "aliases": [
    "Vercel backend",
    "backend architecture",
    "backend framework"
  ],
  "intents": [
    "build a backend",
    "choose backend products",
    "deploy an existing API or server",
    "select a Node.js or Python backend framework"
  ],
  "entities": [
    "Vercel Functions",
    "Vercel Services",
    "Vercel Queues",
    "Vercel Workflow",
    "Vercel Marketplace",
    "container images"
  ]
}

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