AI Workflow Orchestration

Inngest

Name: Inngest
Brand: Inngest
Availability: InStock

Durable execution engine that automates workflows and AI agent runs — event-driven functions with retries, flow control, and step-level observability with no infrastructure to manage.

Listed starting priceFree

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💡

In Plain English

Durable execution engine that automates workflows and AI agent runs — event-driven functions with retries, flow control, and step-level observability with no infrastructure to manage.

Overview

Inngest is a durable execution platform that lets you write background jobs, workflows, and AI agents as plain functions in TypeScript, Python, or Go without provisioning queues, workers, or state stores. You annotate a function with an event trigger or a schedule, break it into 'steps' — each of which is automatically checkpointed — and Inngest handles the entire runtime: durable state across restarts, exponential retries per step, concurrency and rate-limit controls, throttling, sleeps that can span days, and fan-out with parallelism controls. For LLM workloads that is a killer combination: expensive tool calls are retried at the step level (never the whole workflow), you get per-step observability with inputs, outputs, and costs, and you can throttle to keep provider rate limits in check. Inngest is used by companies like Contentful, Resend, and Retool to run event-driven backends and by many AI teams to run agent workflows. The developer experience is one of the reasons it's popular — a local Inngest dev server gives you a real-time inspector of every function run without needing to deploy. Inngest has native SDKs for TypeScript, Python, and Go, integrates with Next.js, Bun, Deno, Cloudflare Workers, and Vercel, and offers a generous free tier. Enterprise plans add SSO, VPC deployments, and dedicated concurrency.

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Quick verdict on Inngest

Good fit for: AI agents that need durable state across long tool chains; Event-driven backend workflows on Next.js and Bun stacks
Think twice if: Event-driven and step-based paradigm requires a mindset shift from teams used to procedural code or simple cron+queue patterns; Free tier capped at 50,000 executions per month, which can be exhausted quickly by chatty agent workflows or high-frequency webhooks
Pricing at a glance: Free; Check the vendor's site for current plans.

Key Features

step.run wraps a block so its result is memoized — if the function crashes and retries, previously-completed steps are skipped and the workflow resumes exactly where it left off, which is what makes Inngest 'durable' vs. a normal queue worker.+

step.waitForEvent lets a function pause for hours, days, or weeks waiting for a specific event to arrive — this is what makes it possible to model human-in-the-loop AI agents without polling.+

AgentKit adds typed tool-calling, guardrails, and streaming to the same durable primitives, so agent tool calls survive retries and network failures without duplicate side effects.+

The Dev Server runs locally with an event stream, function inspector, and time-travel debugging — you replay a failed workflow with the exact same event payload to reproduce bugs.+

Self-hosted Inngest server is open source and API-compatible with the cloud, letting teams start on Inngest Cloud and later move to self-hosted without a rewrite.+

Pricing Plans

Hobby

Basic

$50/mo

Pro

$300/mo

Enterprise

Custom

See Full Pricing →Free vs Paid →Is it worth it? →

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Getting Started with Inngest

1Install the SDK: `npm install inngest` (TypeScript) or `pip install inngest` (Python)
2Add the Inngest HTTP handler to your existing Next.js, FastAPI, or Express app
3Start the local dev server with `npx inngest-cli dev` and open the inspector at localhost:8288
4Write your first function with `inngest.createFunction`, wrap side effects in `step.run`
5Trigger it with `inngest.send({ name: 'my.event', data: {...} })` and watch it run
6Deploy the HTTP handler to production; add the INNGEST_EVENT_KEY env var and you are live

Ready to start? Try Inngest →

Best Use Cases

🎯

AI agents that need durable state across long tool chains

⚡

Event-driven backend workflows on Next.js and Bun stacks

🔧

Batch LLM pipelines with rate-limit-aware concurrency

🚀

Business automations that need to sleep for days or weeks

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Inngest doesn't handle well:

⚠Event-driven and step-based paradigm requires a mindset shift from teams used to procedural code or simple cron+queue patterns
⚠Free tier capped at 50,000 executions per month, which can be exhausted quickly by chatty agent workflows or high-frequency webhooks
⚠Managed-service dependency creates vendor lock-in for critical pipelines unless you commit to operating the self-hosted version
⚠Retry policies and error-handling customization are less granular than what's achievable with fully self-hosted engines like Temporal
⚠AgentKit ecosystem is newer than mature AI orchestration frameworks, with fewer pre-built integrations to vector DBs and agent tools

Pros & Cons

✓ Pros

✓Step-level checkpointing means retries do not re-burn expensive LLM calls
✓Local dev server exposes production-grade tracing during development
✓Concurrency and throttling knobs are exactly what LLM apps need for rate limits
✓Deploys on top of your existing HTTP framework — no separate worker infra
✓Multi-day sleeps and event waits make human-in-the-loop trivial to model

✗ Cons

✗Pricing scales with execution count and step count — chatty agents can add up fast
✗Enterprise VPC / on-prem tier is the only self-hosted option (no open-source runtime)
✗Debugging complex fan-out patterns still requires reading the trace graph carefully
✗Python SDK is newer than the TypeScript one — some features land in TS first
✗For simple cron jobs, Inngest can feel heavier than a native scheduler

Frequently Asked Questions

How is Inngest different from a queue like BullMQ or SQS?+

Queues can retry a whole job but they don't remember partial progress. Inngest's step primitive checkpoints each side effect, so a retried run resumes at the next step with prior outputs available — critical for expensive LLM chains.

Can I self-host Inngest?+

The open-source dev server is for local development. Production self-hosting is available through the Enterprise tier as a VPC or on-prem deployment; there is no fully self-managed OSS runtime today.

Does Inngest work with Vercel and Cloudflare Workers?+

Yes — Inngest is designed for serverless. Your functions live inside your normal HTTP framework; Inngest just calls their handler. It runs cleanly on Vercel, Cloudflare Workers, AWS Lambda, Fly, and Render.

How does pricing work in practice?+

You are billed per execution (function invocation) and per step. A workflow with 5 steps and 1,000 runs a month uses 1,000 executions and 5,000 steps. Concurrency limits and seat counts also factor in on higher tiers.

Is Inngest a fit for cron jobs?+

Yes, but it may be overkill if all you need is 'run this once a day.' The value shows up when you want retries, concurrency limits, or the ability to pause and resume — for a pure cron with no state, a platform-native scheduler is simpler.

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What's New in 2026

In 2026 Inngest expanded the Python SDK to reach TypeScript parity, added first-class OpenTelemetry export for step traces, and shipped an 'AI Kit' with helpers for OpenAI, Anthropic, and Vercel AI SDK that treat every LLM call as an automatic step.

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Spot something out of date?

Pricing and features change often. If anything on this Inngest page is wrong or missing, tell us and include a link to the source. We review every correction.

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Quick Info

More about Inngest

Pricing Review Alternatives Free vs Paid Pros & Cons Worth It?Tutorial

Overview

Quick verdict on Inngest

Good fit for

AI agents that need durable state across long tool chains

Event-driven backend workflows on Next.js and Bun stacks

Think twice if

Event-driven and step-based paradigm requires a mindset shift from teams used to procedural code or simple cron+queue patterns

Free tier capped at 50,000 executions per month, which can be exhausted quickly by chatty agent workflows or high-frequency webhooks

Pricing at a glance

Free

Check the vendor's site for current plans.

Key Features

step.waitForEvent lets a function pause for hours, days, or weeks waiting for a specific event to arrive — this is what makes it possible to model human-in-the-loop AI agents without polling.+

AgentKit adds typed tool-calling, guardrails, and streaming to the same durable primitives, so agent tool calls survive retries and network failures without duplicate side effects.+

The Dev Server runs locally with an event stream, function inspector, and time-travel debugging — you replay a failed workflow with the exact same event payload to reproduce bugs.+

Self-hosted Inngest server is open source and API-compatible with the cloud, letting teams start on Inngest Cloud and later move to self-hosted without a rewrite.+

Getting Started with Inngest

1Install the SDK: `npm install inngest` (TypeScript) or `pip install inngest` (Python)

2Add the Inngest HTTP handler to your existing Next.js, FastAPI, or Express app

3Start the local dev server with `npx inngest-cli dev` and open the inspector at localhost:8288

4Write your first function with `inngest.createFunction`, wrap side effects in `step.run`

5Trigger it with `inngest.send({ name: 'my.event', data: {...} })` and watch it run

6Deploy the HTTP handler to production; add the INNGEST_EVENT_KEY env var and you are live

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Inngest doesn't handle well:

⚠Event-driven and step-based paradigm requires a mindset shift from teams used to procedural code or simple cron+queue patterns

⚠Free tier capped at 50,000 executions per month, which can be exhausted quickly by chatty agent workflows or high-frequency webhooks

⚠Managed-service dependency creates vendor lock-in for critical pipelines unless you commit to operating the self-hosted version

⚠Retry policies and error-handling customization are less granular than what's achievable with fully self-hosted engines like Temporal

⚠AgentKit ecosystem is newer than mature AI orchestration frameworks, with fewer pre-built integrations to vector DBs and agent tools

Pros & Cons

✓ Pros

✓Step-level checkpointing means retries do not re-burn expensive LLM calls
✓Local dev server exposes production-grade tracing during development
✓Concurrency and throttling knobs are exactly what LLM apps need for rate limits
✓Deploys on top of your existing HTTP framework — no separate worker infra
✓Multi-day sleeps and event waits make human-in-the-loop trivial to model

✗ Cons

✗Pricing scales with execution count and step count — chatty agents can add up fast
✗Enterprise VPC / on-prem tier is the only self-hosted option (no open-source runtime)
✗Debugging complex fan-out patterns still requires reading the trace graph carefully
✗Python SDK is newer than the TypeScript one — some features land in TS first
✗For simple cron jobs, Inngest can feel heavier than a native scheduler

Frequently Asked Questions

How is Inngest different from a queue like BullMQ or SQS?+

Can I self-host Inngest?+

The open-source dev server is for local development. Production self-hosting is available through the Enterprise tier as a VPC or on-prem deployment; there is no fully self-managed OSS runtime today.

Inngest

In Plain English

Overview

Vibe Coding Friendly?

Quick verdict on Inngest

Key Features

Pricing Plans

Hobby

Basic

Pro

Enterprise

Getting Started with Inngest

Best Use Cases

AI agents that need durable state across long tool chains

Event-driven backend workflows on Next.js and Bun stacks

Batch LLM pipelines with rate-limit-aware concurrency

Business automations that need to sleep for days or weeks

Limitations & What It Can't Do

Pros & Cons

✓ Pros

✗ Cons

Frequently Asked Questions

How is Inngest different from a queue like BullMQ or SQS?+

Can I self-host Inngest?+

Does Inngest work with Vercel and Cloudflare Workers?+

How does pricing work in practice?+

Is Inngest a fit for cron jobs?+

New to AI tools?

Get updates on Inngest and other AI tools

What's New in 2026

Alternatives to Inngest

Trigger.dev

Temporal

Prefect

Spot something out of date?

Quick Info

Try Inngest Today

Need help choosing the right AI stack?

More about Inngest

Inngest

In Plain English

Overview

Vibe Coding Friendly?

Quick verdict on Inngest

Key Features

Pricing Plans

Hobby

Basic

Pro

Enterprise

Getting Started with Inngest

Best Use Cases

AI agents that need durable state across long tool chains

Event-driven backend workflows on Next.js and Bun stacks

Batch LLM pipelines with rate-limit-aware concurrency

Business automations that need to sleep for days or weeks

Limitations & What It Can't Do

Pros & Cons

✓ Pros

✗ Cons

Frequently Asked Questions

How is Inngest different from a queue like BullMQ or SQS?+

Can I self-host Inngest?+

Does Inngest work with Vercel and Cloudflare Workers?+

How does pricing work in practice?+

Is Inngest a fit for cron jobs?+

New to AI tools?

Get updates on Inngest and other AI tools

What's New in 2026

Alternatives to Inngest

Trigger.dev

Temporal

Prefect

Spot something out of date?

Quick Info

Try Inngest Today

Need help choosing the right AI stack?

More about Inngest