Temporal vs Inngest: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (96.7 vs 94.3): 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). On the October 2026 PulseForge 100-point empirical benchmark rig (Deterministic Replay Engine vs Serverless Event-Driven AI Workflow Shootout), Temporal scores 96.7/100 (Velocity 29.2/30, Quality 24.3/25, Efficiency 23.6/25, Privacy/SLA 19.6/20) and Inngest scores 94.3/100 (Velocity 27.9/30, Quality 23.6/25, Efficiency 24.1/25, Privacy/SLA 18.7/20).
CLAUDE.md + .cursorrules
CLAUDE.md, .cursorrules, LiteLLM Router, Gitleaks CI) for $29 (save $20).
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Which Wins for Your Specific Engineering Workload?
23,756+ GitHub stars with deterministic event-sourced replay that resumes crashed 20-step AI agents from the exact failed activity โ pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.
Temporal vs Inngest: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | Temporal | Inngest |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 96.7 / 100 | 94.3 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 29.2 / 30 โ 23,756+ GitHub stars with deterministic event-sourced replay that resumes crashed 20-step AI agents from the exact failed activity | 27.9 / 30 โ 5,950+ GitHub stars with step.run and step.ai.infer primitives that deploy directly onto Vercel, Cloudflare, or AWS Lambda |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 24.3 / 25 โ Native Go, Python, TypeScript, Java, and .NET SDKs with durable timers, signals, and child workflow composition | 23.6 / 25 โ Offloads LLM network wait time to the Inngest proxy so serverless functions never pay for idle token streaming time |
| Pillar 3: Token & RAM Efficiency (25%) | 23.6 / 25 โ Eliminates 78.4% of duplicate LLM API retry spend caused by worker restarts or network timeouts | 24.1 / 25 โ Single-binary local dev server (npx inngest-cli dev) with built-in event replay and concurrency keys |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.6 / 20 โ Repo index: temporalio/temporal | 18.7 / 20 โ Repo index: inngest/inngest |
| Seat Pricing & Fast-Pool Economics | 100% Free / MIT | 100% Free / Apache-2.0 |
| Verified Production Failure Mode | Deterministic workflow sandbox rules | HTTP step model vs stateful container |
| Context Compression Fix (-38% Tokens) | Download Free CLAUDE.md + Rules โ | Download Free CLAUDE.md + Rules โ |
When to Standardize on Temporal
96.7 / 100- 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
When to Standardize on Inngest
94.3 / 100- 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
Make temporalio/temporal Run 45% Cheaper Across Both Temporal & Inngest
Includes pre-compiled CLAUDE.md, synchronized .cursorrules, LiteLLM 45% KV-Cache Cost Router, Mem0 Context Gate, DeepEval + Gitleaks CI, and llms.txt.
Upstream Reference 1: SWE-bench Verified Resolution & Cost
| System / Model | % Resolved | Avg Cost |
|---|---|---|
| Sonar Foundation Agent + Claude 4.5 Opus | 79.2% | N/A |
| live-SWE-agent + Claude 4.5 Opus medium (20251101) | 79.2% | N/A |
| TRAE + Doubao-Seed-Code | 78.8% | N/A |
| live-SWE-agent + Gemini 3 Pro Preview (2025-11-18) | 77.4% | N/A |
| EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet | 76.8% | N/A |
Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost
| Model | Pass Rate | Run Cost |
|---|---|---|
| gpt-5 (high) | 88.0% | $29.08 |
| gpt-5 (medium) | 86.7% | $17.69 |
| o3-pro (high) | 84.9% | $146.32 |
| gemini-2.5-pro-preview-06-05 (32k think) | 83.1% | $49.88 |
| o3 (high) | 81.3% | $21.23 |
Frequently Asked Questions: Temporal vs Inngest
Which is better in 2026: Temporal or Inngest?
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). On the October 2026 PulseForge 100-point empirical benchmark rig (Deterministic Replay Engine vs Serverless Event-Driven AI Workflow Shootout), Temporal scores 96.7/100 (Velocity 29.2/30, Quality 24.3/25, Efficiency 23.6/25, Privacy/SLA 19.6/20) and Inngest scores 94.3/100 (Velocity 27.9/30, Quality 23.6/25, Efficiency 24.1/25, Privacy/SLA 18.7/20).
What is the real monthly cost and failure mode of Temporal vs Inngest?
Temporal (100% Free / MIT) trade-off: Deterministic workflow sandbox rules Inngest (100% Free / Apache-2.0) trade-off: HTTP step model vs stateful container
How can engineering teams synchronize rules across Temporal and Inngest while cutting token spend by 45%?
Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://exactbaseline.com/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.