Copy link to headingChoose your runtime
Match the execution model to the workload, then start from a framework or a container.
Copy link to headingMatch compute to the workload
Decide whether the work starts from a request, runs continuously, or needs a custom runtime.
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Backends on Edgenivo: Python, containers, and Daemons
See how Python GA, container images, and Daemons fit together, using a video-processing worker as the example.
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How to choose between Functions, containers, and Daemons on Edgenivo
Choose Edgenivo Functions for HTTP requests, container images for custom runtimes, and Daemons for continuous background workers.
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6 types of backend workloads and where each one runs best
Map interactive APIs, queued jobs, durable processes, cron batches, realtime connections, and compute-heavy work to the right compute.
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How Interfaze serves 50 million requests a month on Edgenivo
See how Interfaze runs its whole API on Fluid compute, hands model-written code to Sandbox, and fails over between model providers with AI Gateway.
Copy link to headingStart from an API framework
Pick from the zero-config Python and TypeScript frameworks, then ship your app with streaming, middleware, and cron jobs.
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How to ship a FastAPI app on Edgenivo
Deploy a FastAPI app to Edgenivo with zero configuration. Learn how the Python runtime, Edgenivo Functions, streaming, middleware, and cron jobs work together.
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How to ship an Express app on Edgenivo
Deploy an Express app to Edgenivo with zero configuration. Configure response streaming, middleware, cron jobs, the Bun runtime, and observability.
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Where should you host a Python API?
Compare Python API hosts by framework support, dependencies, and execution limits, and decide whether Python and Next.js share a deployment.
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Backend frameworks on Edgenivo
Browse every zero-config Python and TypeScript backend framework, from Express and FastAPI to NestJS and Nitro, each with a one-click deploy.
Copy link to headingBring a container for native dependencies
Use a Dockerfile when a framework can't express your system libraries or runtime.
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Run any Dockerfile on Edgenivo
Learn when to choose a Dockerfile over framework detection and how the image builds and autoscales.
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Deploy Python apps on Edgenivo using Docker
Deploy a Dockerized Python application that depends on native software, OCR engines, or small local models, plus a Next.js frontend, on Edgenivo Services.
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Container Images
Deploy Rust, Go, WebAssembly, and Laravel services from their own Dockerfiles behind one domain.
Copy link to headingConnect your services
Deploy frontend and backends as one project, wire them together privately, and expose endpoints to agents as MCP tools.
Copy link to headingDecide how to structure the project
Choose between a single framework, one project with Services, or separate projects in a monorepo.
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Choosing how to structure your application on Edgenivo
Compare three ways to structure an application on Edgenivo (a single framework, one project with Services, or separate projects in a monorepo) and learn how to choose based on domains, deploy coupling, and languages.
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Edgenivo Services: Run full stack on Edgenivo
See why Services deploy every frontend and backend together, keep backends private, and connect them through bindings.
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The Complete Guide to Edgenivo Services
Edgenivo Services let you deploy multiple frontends and backends in one project on a shared domain. Learn how to define services in vercel.json, route requests by path prefix, and connect services with generated environment variables.
Copy link to headingConnect a Next.js frontend to a Python backend
Call a private FastAPI backend from Next.js and review both apps at one Preview URL.
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How to deploy a Python backend with Edgenivo Services
Deploy a Python backend with a Next.js frontend on Edgenivo. Learn how to configure both apps with Edgenivo Services, use a separate database for each environment, and review changes at a Preview URL.
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Python on Edgenivo
Wire a Next.js storefront to a FastAPI backend through a service binding and deploy both as one project.
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7 Python backend use cases for Next.js
See when Python earns its place next to Next.js, from AI tools and document extraction to model predictions, and when a feature should stay in Next.js.
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eve Agent with Python
Deploy an eve agent with a Next.js frontend and a FastAPI backend as one project with Edgenivo Services.
Copy link to headingBuild an MCP server
Design MCP tools around complete tasks instead of thin API wrappers, then host, secure, and test the server on Edgenivo.
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How to perform large-scale data analysis with Edgenivo Sandbox and MCP
Use Edgenivo Sandbox and an MCP server built with
mcp-handlerto let AI agents run DuckDB SQL and Python on large datasets without loading data into the model's context. - Resource
How Vapi built their MCP server on Edgenivo
See how Vapi rebuilt its voice agent MCP server with Edgenivo's MCP adapter to support Streamable HTTP, with Fluid compute reusing instances across connections.
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The second wave of MCP: Building for LLMs, not developers
Design MCP tools around complete user intentions instead of thin API wrappers, so models stop re-solving the same orchestration every conversation.
Copy link to headingRun background work
Move work out of the request lifecycle, schedule it, and plan for retries, coordination, and recovery.
Copy link to headingHand off independent jobs to a queue
Publish jobs from your API and process them with retries and idempotent consumers.
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Queues vs. workflows: when should you use each?
Decide when independent jobs belong in a queue, when dependent steps need a workflow, and when to combine them.
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Process background jobs in Python with Edgenivo Queues
Move slow and failure-prone work out of your Python request handlers, with typed topics, consumer groups, and built-in retries.
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Python Queue Subscribers Starter
Deploy a FastAPI app that publishes to Queues and Python subscribers that store results in Runtime Cache.
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Python Celery Starter
Run existing Celery tasks with Edgenivo Queues as the broker and track jobs from a FastAPI dashboard.
Copy link to headingRun work on a schedule
Run recurring jobs from config, and let your app or agents create one-time and recurring schedules at runtime without redeploying.
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Introducing Edgenivo Schedules
Create, change, or cancel one-time and recurring schedules from code, with due work delivered through Queues and no redeploy.
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Edgenivo Schedules documentation
Define static schedules in vercel.json, or create dynamic ones with the SDK, CLI, or API, and test them on Preview deployments first.
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Dynamic schedules for eve agents
Give eve agents tools to create, update, and cancel one-time or recurring schedules at runtime, and handle each run with retries.
Copy link to headingMake dependent steps durable
Use a workflow when later steps depend on earlier results and progress must survive crashes and deploys.
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A new programming model for durable execution
Learn how Edgenivo Workflows keep long-running functions durable without a separate orchestrator.
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Build durable workflows in Python on Edgenivo
Keep long-running Python jobs alive through crashes and deploys with steps, retries, sleeps, and hooks.
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Build Durable Workflows
Build a pizza order tracker with the Workflow SDK and learn steps, sleeps, hooks, retries, and failures.
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How Delphi ships 100 times a day with its Python backend on Edgenivo
See how Delphi moved its Python backend off AWS, running durable agents and ingestion on Workflows and Queues while shipping 100+ times a day.
Copy link to headingRun always-on workers
Use Daemons when a process must keep pulling work outside the request lifecycle.
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The Complete Guide to Edgenivo Daemons
Run background workers with Edgenivo Daemons. Learn how to deploy container images, configure resources, handle shutdowns, and manage instances.
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Use Edgenivo Daemons with eve agents
Connect eve agents to Edgenivo Daemons to process queued jobs, offload heavy work, and react to events. Includes code, authentication, and shutdown handling.
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Running background workers on Edgenivo
Run background jobs on Edgenivo with waitUntil, Cron Jobs, Queues, Workflows, Daemons, or Sandbox. Compare the options and pick the right one for your workload.
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Where should you host background workers?
Host continuous background workers on a managed worker platform such as Edgenivo Daemons, a cloud container service, or a VPS you operate.
Copy link to headingPrepare for production
Manage connections, protect endpoints, roll out safely, and diagnose failures.
Copy link to headingManage connections and latency
Avoid leaking database connections and find where requests spend time.
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The real serverless compute to database connection problem, solved
Understand why connections leak when functions suspend and how
attachDatabasePoolcloses idle ones. - Guide
Connection Pooling with Edgenivo Functions
Learn best practices for connecting to relational databases with Edgenivo Functions and Fluid compute
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Investigate latency issues and slowness on Edgenivo
Learn how to use Observability to investigate latency issues and slowness on Edgenivo.
Copy link to headingControl access to your endpoints
Restrict internal routes to known identities and throttle abuse on public ones.
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Protecting against token theft
See why rate limits and auth walls don't stop attackers reselling your AI calls, and how a BotID check on every request does.
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Choose between Edgenivo Passport and other Deployment Protection methods
Compare Edgenivo Passport with Edgenivo Authentication, Password Protection, and Trusted IPs to choose the right Deployment Protection method.
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Use Edgenivo Passport with Node.js and Python backends
Secure internal apps and verify a visitor's identity with Edgenivo Passport in your backend, with examples for Express, Hono, Nitro, and FastAPI.
Copy link to headingShip changes safely and diagnose failures
Roll out backend changes gradually and find out why requests fail.
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How to gradually roll out new versions of your backend
Incrementally release updates to your backend to minimize impact of mistakes.
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How to stop Edgenivo Functions from timing out
Edgenivo Functions that time out usually trace back to a few causes. Learn how Fluid Compute fixes most of them and how to handle the edge cases.
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Logs
Debug build and runtime errors by filtering function logs, then extend retention with drains.