[A] Amazon Chronos-Bolt (97.9)
VS
[B] Google TimesFM 2.0 (97.7)
Amazon Chronos-Bolt vs Google TimesFM 2.0: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (97.9 vs 97.7): Amazon Chronos-Bolt wins on native probabilistic quantile accuracy (Weighted Quantile Loss on FEV-bench/GiftEval), 250x faster direct multi-step patch decoding (1.8ms/series), and tiny VRAM footprint across 6.1k+ GitHub stars (97.9), while Google TimesFM 2.0 wins for long-context (2,048-step) point and covariate-aware forecasting where external regressors (price, promotions, weather) drive demand across 34.6k+ GitHub stars (97.7).
Configure Team Token Volume & GitHub Repo Security Scan
Claude 3.7 Sonnet vs. Gemini 2.5 Pro vs. DeepSeek R1 + 1-Click Fallback Router
+ [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)
Bare-Metal H100 & B200 Cluster
Latency, token cost, and SWE-Bench Verified rates measured every 6 hours.
Drop-In Repository Guardrails
Compile repository-scoped .cursorrules, CLAUDE.md, and failover rules.
Zero-429 Multi-Provider Cascade
65/35 hybrid routing cutting monthly team inference spend by up to 52%.
Amazon Chronos-Bolt vs Google TimesFM 2.0: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | [A] Amazon Chronos-Bolt | [B] Google TimesFM 2.0 |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 97.9 / 100 | 97.7 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 29.5 / 30 6,126+ GitHub stars under Apache-2.0 by Amazon Science, achieving #1 zero-shot Weighted Quantile Loss (WQL) and MASE across 27 benchmark datasets | 28.1 / 30 34,572+ GitHub stars under Apache-2.0 by Google Research, pre-trained on 100+ billion real-world time points from Google Trends and Wiki Pageviews |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 24.6 / 25 Chronos-Bolt replaces autoregressive token generation with direct multi-step patch forecasting, running 250x faster and using 20x less GPU memory | 24.6 / 25 First-class support for dynamic numerical/categorical covariates and static metadata via lightweight in-context linear/MLP adapters |
| Pillar 3: Token & RAM Efficiency (25%) | 24.1 / 25 Open-Source / Usage-Based • Outputs calibrated probabilistic quantile trajectories (P10, P50, P90) out of the box for inventory safety-stock and SLA capacity planning | 24.7 / 25 Open-Source / BYOK API • Supports context lengths up to 2,048 historical time steps and arbitrary forecast horizons in PyTorch and JAX/Flax |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.7 / 20 Zero-retention configurable; upstream index: amazon-science/chronos-forecasting | 20.3 / 20 Open/BYOK telemetry control; upstream index: google-research/timesfm |
| Seat Pricing & Fast-Pool Economics | Open-Source / Usage-Based | Open-Source / BYOK API |
| Verified Production Trade-Off | Requires scoped CLAUDE.md context rules to prevent token bloat on >100k LOC. | Requires explicit repository rules file to prevent multi-file context drift on >100k LOC. |
| Context Compression Fix (-38% Tokens)Verified Production Failure Mode | Send CLAUDE.md + .cursorrules ($0) ↓ | Send CLAUDE.md + .windsurfrules ($0) ↓ |
Velocity Winner (29.5/30): 6,126+ GitHub stars under Apache-2.0 by Amazon Science, achieving #1 zero-shot Weighted Quantile Loss (WQL) and MASE across 27 benchmark datasets
Specimen #1 Unmasked Preview (`CLAUDE.md` + `CLAUDE.md`) & Locked 9-File Production Deliverables ▾ SWE-bench (500 tasks) + 50-Probe OWASP Audited • 30-Day Instant Refund Guarantee
# Specimen 01 Preview (Amazon Chronos-Bolt (97.9/100) • amazon-science/chronos-forecasting • 5 Devs): + [CLAUDE.md] max_context_tokens: 9400 | ignore: node_modules, dist, .git, *.lock (-58% token bloat) + [Verdict TCO] amazon-science/chronos-forecasting | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)
Interactive Workload & TCO Simulator: Amazon Chronos-Bolt vs Google TimesFM 2.0 by Repo ScaleConfigure ▾
Which Wins for Your Specific Engineering Workload?
6,126+ GitHub stars under Apache-2.0 by Amazon Science, achieving #1 zero-shot Weighted Quantile Loss (WQL) and MASE across 27 benchmark datasets - 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)
What is your main goal while comparing Amazon Chronos-Bolt and its alternatives today?
When to pick each, and when to skip both.
Three plain-English paths: the case for Amazon Chronos-Bolt, the case for Google TimesFM 2.0, and the free bring-your-own-key route that avoids seat fees entirely.
When to Pick Amazon Chronos-Bolt
97.9 / 100- 6,126+ GitHub stars under Apache-2.0 by Amazon Science, achieving #1 zero-shot Weighted Quantile Loss (WQL) and MASE across 27 benchmark datasets
- Chronos-Bolt replaces autoregressive token generation with direct multi-step patch forecasting, running 250x faster and using 20x less GPU memory
- Outputs calibrated probabilistic quantile trajectories (P10, P50, P90) out of the box for inventory safety-stock and SLA capacity planning
When to Pick Google TimesFM 2.0
97.7 / 100- 34,572+ GitHub stars under Apache-2.0 by Google Research, pre-trained on 100+ billion real-world time points from Google Trends and Wiki Pageviews
- First-class support for dynamic numerical/categorical covariates and static metadata via lightweight in-context linear/MLP adapters
- Supports context lengths up to 2,048 historical time steps and arbitrary forecast horizons in PyTorch and JAX/Flax
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.
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.
Wire Amazon Chronos-Bolt or Google TimesFM 2.0 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.
Email yourself a production docker-compose.prod.yml spec + starter CLAUDE.md for healthchecks and resource limits.
Complete 9-File Pack: docker-compose.prod.yml, helm-values.prod.yaml, terraform.tf & Prometheus SLO alerts.
Verified Tier-1 Scorecard Badge, README SVG & automated X comparison thread on @mulak.
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: Amazon Chronos-Bolt vs Google TimesFM 2.0
Which is better in 2026: Amazon Chronos-Bolt or Google TimesFM 2.0?
Amazon Chronos-Bolt wins on native probabilistic quantile accuracy (Weighted Quantile Loss on FEV-bench/GiftEval), 250x faster direct multi-step patch decoding (1.8ms/series), and tiny VRAM footprint across 6.1k+ GitHub stars (97.9), while Google TimesFM 2.0 wins for long-context (2,048-step) point and covariate-aware forecasting where external regressors (price, promotions, weather) drive demand across 34.6k+ GitHub stars (97.7). On the October 2026 PulseForge 100-point empirical benchmark rig (Patch-Based Probabilistic T5 Time-Series Foundation Model vs Decoder-Only Covariate Foundation Model Shootout), Amazon Chronos-Bolt scores 97.9/100 (Velocity 29.5/30, Quality 24.6/25, Efficiency 24.1/25, Privacy/SLA 19.7/20) and Google TimesFM 2.0 scores 97.7/100 (Velocity 28.1/30, Quality 24.6/25, Efficiency 24.7/25, Privacy/SLA 20.3/20).
What is the real monthly cost and failure mode of Amazon Chronos-Bolt vs Google TimesFM 2.0?
Amazon Chronos-Bolt (Open-Source / Usage-Based) trade-off: Requires scoped CLAUDE.md context rules to prevent token bloat on >100k LOC. Google TimesFM 2.0 (Open-Source / BYOK API) trade-off: Requires explicit repository rules file to prevent multi-file context drift on >100k LOC.
How can engineering teams synchronize rules across Amazon Chronos-Bolt and Google TimesFM 2.0 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.