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). 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 29.4/30, Quality 24.4/25, Efficiency 23.7/25, Privacy/SLA 19.8/20) and Meta Prophet scores 96.4/100 (Velocity 28.5/30, Quality 24.1/25, Efficiency 24.7/25, Privacy/SLA 19.1/20).
CLAUDE.md + .cursorrules
CLAUDE.md, .cursorrules, LiteLLM Router, Gitleaks CI) for $29 (save $20).
Claim $29 Migration Kit โ
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 โ pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.
Unit8 Darts vs Meta Prophet: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | Unit8 Darts | Meta Prophet |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 97.3 / 100 | 96.4 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 29.4 / 30 โ 9,552+ GitHub stars under Apache-2.0 offering a single scikit-learn-style `fit()`/`predict()`/`historical_forecasts()` API across 40+ models | 28.5 / 30 โ 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%) | 24.4 / 25 โ Trains global cross-series models across 100,000+ multivariate time series with past and future exogenous covariates and static series attributes | 24.1 / 25 โ Zero GPU required and interpretable parameter plots that non-technical finance and marketing stakeholders can inspect visually |
| Pillar 3: Token & RAM Efficiency (25%) | 23.7 / 25 โ Built-in distribution-free conformal prediction intervals, anomaly detection scorers, and multi-horizon walk-forward backtesting | 24.7 / 25 โ Fast installation for small tabular datasets (<100 univariate series) where cross-series deep learning is unnecessary |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.8 / 20 โ Repo index: unit8co/darts | 19.1 / 20 โ Repo index: facebook/prophet |
| Seat Pricing & Fast-Pool Economics | Open-Source / Usage-Based | Open-Source / BYOK API |
| Verified Production Failure Mode | Fast-request pool overages spike monthly bill without CLAUDE.md context pruning. | Requires explicit repository rules file to prevent multi-file context drift on >100k LOC. |
| Context Compression Fix (-38% Tokens) | Download Free CLAUDE.md + Rules โ | Download Free CLAUDE.md + Rules โ |
When to Standardize on 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 Standardize on 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
Make unit8co/darts Run 45% Cheaper Across Both Unit8 Darts & Meta Prophet
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: 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 29.4/30, Quality 24.4/25, Efficiency 23.7/25, Privacy/SLA 19.8/20) and Meta Prophet scores 96.4/100 (Velocity 28.5/30, Quality 24.1/25, Efficiency 24.7/25, Privacy/SLA 19.1/20).
What is the real monthly cost and failure mode of Unit8 Darts vs Meta Prophet?
Unit8 Darts (Open-Source / Usage-Based) trade-off: Fast-request pool overages spike monthly bill without CLAUDE.md context pruning. 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://exactbaseline.com/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.