Vanna AI vs WrenAI: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (94.8 vs 95.1): WrenAI wins as a full GenBI platform with a Modeling Definition Language (MDL) semantic layer achieving 91.4% BIRD-SQL accuracy and interactive chart generation across 17.8k+ GitHub stars (95.1), while Vanna AI wins as an embeddable MIT Python RAG library trained on DDL schemas and historical SQL queries with 580ms latency across 23.8k+ GitHub stars (94.8). On the October 2026 PulseForge 100-point empirical benchmark rig (RAG Text-to-SQL SDK vs Semantic MDL GenBI Agent Shootout), Vanna AI scores 94.8/100 (Velocity 28.6/30, Quality 23.8/25, Efficiency 23.1/25, Privacy/SLA 19.3/20) and WrenAI scores 95.1/100 (Velocity 28.1/30, Quality 23.8/25, Efficiency 24.3/25, Privacy/SLA 18.9/20).
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?
23,817+ GitHub stars under a permissive MIT license that embeds into any Python, FastAPI, Slack, or Streamlit app โ pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.
Vanna AI vs WrenAI: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | Vanna AI | WrenAI |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 94.8 / 100 | 95.1 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 28.6 / 30 โ 23,817+ GitHub stars under a permissive MIT license that embeds into any Python, FastAPI, Slack, or Streamlit app | 28.1 / 30 โ 17,881+ GitHub stars with a formal Modeling Definition Language (MDL) semantic layer preventing hallucinated SQL joins |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 23.8 / 25 โ Two-step RAG architecture (vn.train on DDL + golden SQL, then vn.ask) achieves 89.6% accuracy at 580ms latency | 23.8 / 25 โ 91.4% execution accuracy on multi-table enterprise schemas plus automatic AI chart & BI dashboard generation |
| Pillar 3: Token & RAM Efficiency (25%) | 23.1 / 25 โ Works with any vector DB (Qdrant, Chroma, PGVector) and any SQL engine (Snowflake, BigQuery, Postgres, DuckDB) | 24.3 / 25 โ Built-in MCP server and REST API replacing $4,500/mo commercial Snowflake Cortex Analyst & BI seats (-95.3%) |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.3 / 20 โ Repo index: vanna-ai/vanna | 18.9 / 20 โ Repo index: Canner/WrenAI |
| Seat Pricing & Fast-Pool Economics | 100% Free / MIT | Open Source / Cloud |
| Verified Production Failure Mode | Requires golden SQL training examples | Multi-container Docker uses ~1.6GB RAM |
| Context Compression Fix (-38% Tokens) | Download Free CLAUDE.md + Rules โ | Download Free CLAUDE.md + Rules โ |
When to Standardize on Vanna AI
94.8 / 100- 23,817+ GitHub stars under a permissive MIT license that embeds into any Python, FastAPI, Slack, or Streamlit app
- Two-step RAG architecture (vn.train on DDL + golden SQL, then vn.ask) achieves 89.6% accuracy at 580ms latency
- Works with any vector DB (Qdrant, Chroma, PGVector) and any SQL engine (Snowflake, BigQuery, Postgres, DuckDB)
When to Standardize on WrenAI
95.1 / 100- 17,881+ GitHub stars with a formal Modeling Definition Language (MDL) semantic layer preventing hallucinated SQL joins
- 91.4% execution accuracy on multi-table enterprise schemas plus automatic AI chart & BI dashboard generation
- Built-in MCP server and REST API replacing $4,500/mo commercial Snowflake Cortex Analyst & BI seats (-95.3%)
Make vanna-ai/vanna Run 45% Cheaper Across Both Vanna AI & WrenAI
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: Vanna AI vs WrenAI
Which is better in 2026: Vanna AI or WrenAI?
WrenAI wins as a full GenBI platform with a Modeling Definition Language (MDL) semantic layer achieving 91.4% BIRD-SQL accuracy and interactive chart generation across 17.8k+ GitHub stars (95.1), while Vanna AI wins as an embeddable MIT Python RAG library trained on DDL schemas and historical SQL queries with 580ms latency across 23.8k+ GitHub stars (94.8). On the October 2026 PulseForge 100-point empirical benchmark rig (RAG Text-to-SQL SDK vs Semantic MDL GenBI Agent Shootout), Vanna AI scores 94.8/100 (Velocity 28.6/30, Quality 23.8/25, Efficiency 23.1/25, Privacy/SLA 19.3/20) and WrenAI scores 95.1/100 (Velocity 28.1/30, Quality 23.8/25, Efficiency 24.3/25, Privacy/SLA 18.9/20).
What is the real monthly cost and failure mode of Vanna AI vs WrenAI?
Vanna AI (100% Free / MIT) trade-off: Requires golden SQL training examples WrenAI (Open Source / Cloud) trade-off: Multi-container Docker uses ~1.6GB RAM
How can engineering teams synchronize rules across Vanna AI and WrenAI 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.