# Temporal vs Inngest: 2026 Empirical Benchmark & Cost Shootout

> **Direct Answer (October 2026 Verdict)**: Temporal wins for complex polyglot microservices and mission-critical multi-agent state machines requiring 99.999% crash-recovery durability, deterministic replay, and month-long signals across 23.7k+ GitHub stars (96.7), while Inngest wins for zero-infrastructure serverless TypeScript/Python AI agents where step.ai.infer offloads slow LLM HTTP calls so Vercel/Lambda functions pay zero idle compute across 5.9k+ GitHub stars (94.3).

- **Category**: Deterministic Replay Engine vs Serverless Event-Driven AI Workflow Shootout
- **Canonical URL**: https://exactbaseline.com/vs/temporal-vs-inngest
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## Temporal Key Advantages
- 23,756+ GitHub stars with deterministic event-sourced replay that resumes crashed 20-step AI agents from the exact failed activity
- Native Go, Python, TypeScript, Java, and .NET SDKs with durable timers, signals, and child workflow composition
- Eliminates 78.4% of duplicate LLM API retry spend caused by worker restarts or network timeouts

## Inngest Key Advantages
- 5,950+ GitHub stars with step.run and step.ai.infer primitives that deploy directly onto Vercel, Cloudflare, or AWS Lambda
- Offloads LLM network wait time to the Inngest proxy so serverless functions never pay for idle token streaming time
- Single-binary local dev server (npx inngest-cli dev) with built-in event replay and concurrency keys