Inngest vs Cua

Detailed side-by-side comparison to help you choose the right tool

Inngest

🔴Developer

AI Agents

Durable-execution platform for AI workflows and agents — write step-functions in TypeScript or Python, get retries, scheduling and observability for free.

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Starting Price

Free

Cua

🔴Developer

AI Agents

Cloud desktops for AI agents — give Claude Code, Codex or any computer-use agent its own isolated macOS or Linux VM.

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Starting Price

Custom

Feature Comparison

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FeatureInngestCua
CategoryAI AgentsAI Agents
Pricing Plans8 tiers157 tiers
Starting PriceFree
Key Features
  • Step-based function execution with automatic retries
  • Event-driven workflow triggering and orchestration
  • Local development server with production parity

    Inngest - Pros & Cons

    Pros

    • Durable execution survives crashes and resumes mid-workflow
    • AgentKit framework purpose-built for multi-step AI agents
    • Generous free tier: 50k runs/month with full features
    • Beautiful dashboard with traces, logs, and replay
    • Works on Vercel, Cloudflare Workers, Lambda, and containers

    Cons

    • TypeScript-first — Python SDK is less mature
    • Step-function programming model has a learning curve
    • Self-hosted Inngest available but most teams use the cloud
    • Pricing jumps from $30 Basic to $150 Pro tier feel steep mid-stage

    Cua - Pros & Cons

    Pros

    • macOS cloud desktops are rare and useful for Apple-only app workflows
    • MCP-native — works with any agent runtime that speaks MCP
    • Per-session isolation, recording, and revocation solve real compliance blockers
    • Removes the operational tax of running your own VM fleet for computer-use agents

    Cons

    • Per-hour billing can be expensive for always-on agents — model carefully
    • Hourly rate must be verified directly with vendor (no live confirmation from curl)
    • Windows support tends to lag macOS/Linux in cloud-desktop products
    • Cua removes infra friction but does not fix the underlying fragility of computer-use models

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