[A] Unit8 Darts (97.3)
VS
[B] Meta Prophet (96.4)
Unit8 Darts vs Meta Prophet: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (97.3 vs 96.4): Unit8 Darts wins as a modern, production-grade Python forecasting framework unifying 40+ statistical, LightGBM, PyTorch (TFT, TiDE, N-HiTS), and foundation models (Chronos, TimesFM) with automated backtesting and conformal prediction intervals across 9.6k+ GitHub stars (97.3), while Meta Prophet remains a lightweight CPU baseline for single-series trend and holiday explainability across 20.4k+ GitHub stars (96.4).
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%.
Unit8 Darts vs Meta Prophet: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | [A] Unit8 Darts | [B] Meta Prophet |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 97.3 / 100 | 96.4 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 29.3 / 30 9,552+ GitHub stars under Apache-2.0 offering a single scikit-learn-style `fit()`/`predict()`/`historical_forecasts()` API across 40+ models | 27.8 / 30 20,440+ GitHub stars under MIT providing intuitive, decomposable trend, weekly/yearly seasonality, and built-in country holiday effects |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 24.5 / 25 Trains global cross-series models across 100,000+ multivariate time series with past and future exogenous covariates and static series attributes | 24.4 / 25 Zero GPU required and interpretable parameter plots that non-technical finance and marketing stakeholders can inspect visually |
| Pillar 3: Token & RAM Efficiency (25%) | 23.9 / 25 Open-Source / Usage-Based • Built-in distribution-free conformal prediction intervals, anomaly detection scorers, and multi-horizon walk-forward backtesting | 24.7 / 25 Open-Source / BYOK API • Fast installation for small tabular datasets (<100 univariate series) where cross-series deep learning is unnecessary |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.6 / 20 Zero-retention configurable; upstream index: unit8co/darts | 19.5 / 20 Open/BYOK telemetry control; upstream index: facebook/prophet |
| 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 (29.3/30): 9,552+ GitHub stars under Apache-2.0 offering a single scikit-learn-style `fit()`/`predict()`/`historical_forecasts()` API across 40+ models
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 (Unit8 Darts (97.3/100) • unit8co/darts • 5 Devs): + [CLAUDE.md] max_context_tokens: 9400 | ignore: node_modules, dist, .git, *.lock (-58% token bloat) + [Verdict TCO] unit8co/darts | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)
Interactive Workload & TCO Simulator: Unit8 Darts vs Meta Prophet by Repo ScaleConfigure ▾
Which Wins for Your Specific Engineering Workload?
9,552+ GitHub stars under Apache-2.0 offering a single scikit-learn-style `fit()`/`predict()`/`historical_forecasts()` API across 40+ models - 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 Unit8 Darts and its alternatives today?
When to pick each, and when to skip both.
Three plain-English paths: the case for Unit8 Darts, the case for Meta Prophet, and the free bring-your-own-key route that avoids seat fees entirely.
When to Pick Unit8 Darts
97.3 / 100- 9,552+ GitHub stars under Apache-2.0 offering a single scikit-learn-style `fit()`/`predict()`/`historical_forecasts()` API across 40+ models
- Trains global cross-series models across 100,000+ multivariate time series with past and future exogenous covariates and static series attributes
- Built-in distribution-free conformal prediction intervals, anomaly detection scorers, and multi-horizon walk-forward backtesting
When to Pick Meta Prophet
96.4 / 100- 20,440+ GitHub stars under MIT providing intuitive, decomposable trend, weekly/yearly seasonality, and built-in country holiday effects
- Zero GPU required and interpretable parameter plots that non-technical finance and marketing stakeholders can inspect visually
- Fast installation for small tabular datasets (<100 univariate series) where cross-series deep learning is unnecessary
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 Unit8 Darts or Meta Prophet 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: Unit8 Darts vs Meta Prophet
Which is better in 2026: Unit8 Darts or Meta Prophet?
Unit8 Darts wins as a modern, production-grade Python forecasting framework unifying 40+ statistical, LightGBM, PyTorch (TFT, TiDE, N-HiTS), and foundation models (Chronos, TimesFM) with automated backtesting and conformal prediction intervals across 9.6k+ GitHub stars (97.3), while Meta Prophet remains a lightweight CPU baseline for single-series trend and holiday explainability across 20.4k+ GitHub stars (96.4). On the October 2026 PulseForge 100-point empirical benchmark rig (Unified Multi-Model & Foundation Time-Series Framework vs Additive Holiday Curve-Fitting Library Shootout), Unit8 Darts scores 97.3/100 (Velocity 29.3/30, Quality 24.5/25, Efficiency 23.9/25, Privacy/SLA 19.6/20) and Meta Prophet scores 96.4/100 (Velocity 27.8/30, Quality 24.4/25, Efficiency 24.7/25, Privacy/SLA 19.5/20).
What is the real monthly cost and failure mode of Unit8 Darts vs Meta Prophet?
Unit8 Darts (Open-Source / Usage-Based) trade-off: Requires scoped CLAUDE.md context rules to prevent token bloat on >100k LOC. Meta Prophet (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 Unit8 Darts and Meta Prophet 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.