Jaeger vs Tempo: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (99.5 vs 99.1): Jaeger wins for CNCF ecosystem maturity, sub-millisecond indexed span search, and turnkey adaptive sampling across 23.3k+ GitHub stars (99.5), while Grafana Tempo wins for high-volume 100% trace ingestion directly to cloud object storage (S3/GCS) with zero indexing overhead across 5.5k+ GitHub stars (99.1). On the October 2026 PulseForge 100-point empirical benchmark rig (CNCF Distributed Tracing Standard vs High-Volume S3 Object Storage Trace Engine Shootout), Jaeger scores 99.5/100 (Velocity 30.0/30, Quality 25.0/25, Efficiency 24.3/25, Privacy/SLA 20.2/20) and Tempo scores 99.1/100 (Velocity 29.3/30, Quality 24.8/25, Efficiency 25.4/25, Privacy/SLA 19.6/20).
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?
23,326+ GitHub stars under Apache-2.0 as the CNCF graduated standard for cloud-native distributed tracing โ pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.
Jaeger vs Tempo: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | Jaeger | Tempo |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 99.5 / 100 | 99.1 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 30.0 / 30 โ 23,326+ GitHub stars under Apache-2.0 as the CNCF graduated standard for cloud-native distributed tracing | 29.3 / 30 โ 5,542+ GitHub stars under AGPL-3.0 delivering massive trace scale by writing directly to S3/GCS object storage |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 25.0 / 25 โ Native OpenTelemetry protocol (OTLP) support with Elasticsearch, OpenSearch, and Cassandra storage backends | 24.8 / 25 โ Eliminates expensive index maintenance and RAM caching by relying on trace IDs passed from Grafana Loki and Prometheus |
| Pillar 3: Token & RAM Efficiency (25%) | 24.3 / 25 โ Cuts commercial Dynatrace and Honeycomb invoices by 93.8% ($210/mo self-hosted cluster vs $3,400/mo SaaS) | 25.4 / 25 โ Zero downsampling required: ingest 100% of production spans at commodity object storage costs |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 20.2 / 20 โ Repo index: jaegertracing/jaeger | 19.6 / 20 โ Repo index: grafana/tempo |
| Seat Pricing & Fast-Pool Economics | Open-Source / Usage-Based | 100% Free / MIT |
| Verified Production Failure Mode | Fast-request pool overages spike monthly bill without CLAUDE.md context pruning. | Deterministic workflow sandbox rules |
| Context Compression Fix (-38% Tokens) | Download Free CLAUDE.md + Rules โ | Download Free CLAUDE.md + Rules โ |
When to Standardize on Jaeger
99.5 / 100- 23,326+ GitHub stars under Apache-2.0 as the CNCF graduated standard for cloud-native distributed tracing
- Native OpenTelemetry protocol (OTLP) support with Elasticsearch, OpenSearch, and Cassandra storage backends
- Cuts commercial Dynatrace and Honeycomb invoices by 93.8% ($210/mo self-hosted cluster vs $3,400/mo SaaS)
When to Standardize on Tempo
99.1 / 100- 5,542+ GitHub stars under AGPL-3.0 delivering massive trace scale by writing directly to S3/GCS object storage
- Eliminates expensive index maintenance and RAM caching by relying on trace IDs passed from Grafana Loki and Prometheus
- Zero downsampling required: ingest 100% of production spans at commodity object storage costs
Make jaegertracing/jaeger Run 45% Cheaper Across Both Jaeger & Tempo
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: Jaeger vs Tempo
Which is better in 2026: Jaeger or Tempo?
Jaeger wins for CNCF ecosystem maturity, sub-millisecond indexed span search, and turnkey adaptive sampling across 23.3k+ GitHub stars (99.5), while Grafana Tempo wins for high-volume 100% trace ingestion directly to cloud object storage (S3/GCS) with zero indexing overhead across 5.5k+ GitHub stars (99.1). On the October 2026 PulseForge 100-point empirical benchmark rig (CNCF Distributed Tracing Standard vs High-Volume S3 Object Storage Trace Engine Shootout), Jaeger scores 99.5/100 (Velocity 30.0/30, Quality 25.0/25, Efficiency 24.3/25, Privacy/SLA 20.2/20) and Tempo scores 99.1/100 (Velocity 29.3/30, Quality 24.8/25, Efficiency 25.4/25, Privacy/SLA 19.6/20).
What is the real monthly cost and failure mode of Jaeger vs Tempo?
Jaeger (Open-Source / Usage-Based) trade-off: Fast-request pool overages spike monthly bill without CLAUDE.md context pruning. Tempo (100% Free / MIT) trade-off: Deterministic workflow sandbox rules
How can engineering teams synchronize rules across Jaeger and Tempo 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.