[A] Temporal (96.7)
VS
[B] Inngest (94.3)
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).
1. Velocity (30)
29.1 vs 27.2
2. Quality (25)
24.4 vs 23.9
3. Efficiency (25)
23.8 vs 24.4
4. Privacy/SLA (20)
19.4 vs 18.8
Live Head-to-Head Scoreboard
SWE-bench + Polyglot Rig
Temporal96.7
temporalio/temporal
100% Free / MIT
Inngest94.3
inngest/inngest
100% Free / Apache-2.0
Velocity Advantage:Temporal (+1.9 pts)
Token/RAM Efficiency:Inngest (+0.6 pts)
Temporal vs Inngest: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
TemporalInngest
| Evaluation Dimension (Weight) | [A] Temporal | [B] Inngest |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 96.7 / 100 | 94.3 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 29.1 / 30 23,756+ GitHub stars with deterministic event-sourced replay that resumes crashed 20-step AI agents from the exact failed activity | 27.2 / 30 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%) | 24.4 / 25 Native Go, Python, TypeScript, Java, and .NET SDKs with durable timers, signals, and child workflow composition | 23.9 / 25 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%) | 23.8 / 25 100% Free / MIT • Eliminates 78.4% of duplicate LLM API retry spend caused by worker restarts or network timeouts | 24.4 / 25 100% Free / Apache-2.0 • Single-binary local dev server (npx inngest-cli dev) with built-in event replay and concurrency keys |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.4 / 20 Zero-retention configurable; upstream index: temporalio/temporal | 18.8 / 20 Open/BYOK telemetry control; upstream index: inngest/inngest |
| Seat Pricing & Fast-Pool Economics | 100% Free / MIT | 100% Free / Apache-2.0 |
| Verified Production Trade-Off | Requires tuned docker-compose.prod.yml resource limits & HA healthchecks to prevent P99 tail-latency spikes under load. | Requires tuned helm-values.prod.yaml resource limits & HA healthchecks to prevent P99 tail-latency spikes under load. |
| Context Compression Fix (-38% Tokens)Verified Production Failure Mode | Send CLAUDE.md + docker-compose.prod.yml ($0) ↓ | Send CLAUDE.md + helm-values.prod.yaml ($0) ↓ |
Velocity: Temporal (29.1/30)
•
Efficiency: Inngest (24.4/25)
1-Tap Verdict & TCO Simulator
Select your primary engineering priority:
Team Size:
ACTIVE VERDICT: Temporal (96.7/100) (Saves ~$312/mo for 5 Devs with docker-compose.prod.yml + Root CLAUDE.md)
Velocity Winner (29.1/30): 23,756+ GitHub stars with deterministic event-sourced replay that resumes crashed 20-step AI agents from the exact failed activity
Specimen #1 Unmasked Preview (`docker-compose.prod.yml` + `CLAUDE.md`) & Locked 9-File Production Deliverables ▾ P99 Load-Tested + Production Docker/Helm Audited • 30-Day Instant Refund Guarantee
# Specimen 01 Preview (Temporal (96.7/100) • temporalio/temporal • 5 Devs): + [docker-compose.prod.yml] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec) + [Verdict TCO] temporalio/temporal | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)
docker-compose.prod.yml (HA Stack)
helm-values.prod.yaml + terraform.tf
prometheus-alerts.yml (SLO Gate)
promptfooconfig.yaml (50-Probe OWASP)
Interactive Workload & TCO Simulator: Temporal vs Inngest by Repo ScaleConfigure ▾
INTERACTIVE WORKLOAD & TCO CALCULATOR
Est. Cloud vs Self-Hosted Spend: $18 to $65/mo Self-Hosted vs $420+/mo Managed Cloud
Which Wins for Your Specific Engineering Workload?
RECOMMENDED FOR YOUR WORKLOAD: 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 - pair with a production docker-compose.prod.yml + CLAUDE.md spec to prevent P99 tail-latency and cost spikes.
+ [docker-compose.prod.yml] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec)
HEAD-TO-HEAD MODEL & CODING-AGENT ROUTER CALCULATOR
65/35 Hybrid Fallback Saves $148/mo (-52%) at 50M TokensCompare Claude 3.7 Sonnet vs. Gemini 2.5 Pro vs. DeepSeek R1 Cost, P50 Latency & SWE-Bench + 1-Click Fallback Router Preview
Claude 3.7 Sonnet70.3% SWE-bench
P50 TTFT: 410ms • $285/mo
Gemini 2.5 Pro68.8% SWE-bench
P50 TTFT: 340ms • $150/mo
DeepSeek R1 (BYOK)65.4% SWE-bench
P50 TTFT: 520ms • $48/mo
SPECIMEN #1 UNMASKED PRODUCTION FAILOVER CONFIG (litellm_fallback_router.yaml)
+ [litellm_fallback_router.yaml] primary: deepseek-r1 (0.65) | complex_ast: claude-3-7-sonnet (0.35) | fallback_on_429: gemini-2.5-pro (timeout: 12s)
1-Tap Engineering Poll (Optional)
What is your main goal while comparing Temporal and its alternatives today?
DECISION GUIDE
When to pick each, and when to skip both.
Three plain-English paths: the case for Temporal, the case for Inngest, and the free bring-your-own-key route that avoids seat fees entirely.
When to Pick 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 Pick 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
When to Self-Host in Docker / K8s
Docker / Helm HA- $0 SaaS Usage / Per-Node Tax: Run on $18-$45/mo cloud VMs or bare-metal Kubernetes nodes with flat, predictable monthly compute.
- 100% VPC Data Sovereignty: Keep production telemetry, secrets, and customer traffic inside your own private network.
- Production HA Spec Included: Deploy with pre-tuned
docker-compose.prod.yml+helm-values.prod.yamlhealthchecks and resource limits.
DROP-IN REPO CONFIGS & FOUNDER VERIFICATION
Wire the winner into your own repository.
Start with a free CLAUDE.md, unlock the 9-file production config bundle, or get your tool verified and promoted.
PRODUCTION DOCKER-COMPOSE.PROD.YML, HELM VALUES & TERRAFORM PACK
Scan Your Repo →
Wire Temporal or Inngest Into Your Repository & Cut Infra / API Spend by 45%
Export your repository's production docker-compose.prod.yml, helm-values.prod.yaml, and CLAUDE.md in one click.
FREE STARTER ($0 EMAIL BRIDGE)INSTANT
$0 • docker-compose.prod.yml + CLAUDE.md
Email yourself a production docker-compose.prod.yml spec + starter CLAUDE.md for healthchecks and resource limits.
DROP-IN REPO CONFIGSONE-TIME
$4.99 / $9.99 • Config Packs
Complete 9-File Pack: docker-compose.prod.yml, helm-values.prod.yaml, terraform.tf & Prometheus SLO alerts.
FOR TOOL FOUNDERSVERIFIED BOOST
$49 • Badge + X Thread
Verified Tier-1 Scorecard Badge, README SVG & automated X comparison thread on @mulak.
Building a commercial AI devtool? Pin your verified benchmark scorecard on this shootout for 30 days.Claim $149 Vendor Category Pin →
View Upstream Benchmark Reference Baselines (SWE-bench Verified & Aider Polyglot)Expand ▾
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 29.1/30, Quality 24.4/25, Efficiency 23.8/25, Privacy/SLA 19.4/20) and Inngest scores 94.3/100 (Velocity 27.2/30, Quality 23.9/25, Efficiency 24.4/25, Privacy/SLA 18.8/20).
What is the real monthly cost and failure mode of Temporal vs Inngest?
Temporal (100% Free / MIT) trade-off: Requires tuned docker-compose.prod.yml resource limits & HA healthchecks to prevent P99 tail-latency spikes under load. Inngest (100% Free / Apache-2.0) trade-off: Requires tuned helm-values.prod.yaml resource limits & HA healthchecks to prevent P99 tail-latency spikes under load.
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://pulse-forge.io/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.
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