[A] Vanna AI (94.8)
VS
[B] WrenAI (95.1)
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).
1. Velocity (30)
28.5 vs 28.6
2. Quality (25)
23.9 vs 24.0
3. Efficiency (25)
23.3 vs 23.4
4. Privacy/SLA (20)
19.1 vs 19.1
Live Head-to-Head Scoreboard
SWE-bench + Polyglot Rig
Vanna AI94.8
vanna-ai/vanna
100% Free / MIT
WrenAI95.1
Canner/WrenAI
Open Source / Cloud
Velocity Advantage:WrenAI (+0.1 pts)
Token/RAM Efficiency:WrenAI (+0.1 pts)
WORKLOAD, REPO & TOKEN VOLUME WORKBENCH
Configure Team Token Volume & GitHub Repo Security Scan
LIVE SIDE-BY-SIDE COST, LATENCY & SWE-BENCH MATRIX
65/35 Hybrid Fallback Saves $148/mo (-52%) at 50M TokensClaude 3.7 Sonnet vs. Gemini 2.5 Pro vs. DeepSeek R1 + 1-Click Fallback Router
Claude 3.7 Sonnet70.3% SWE-bench
P50 TTFT: 410ms • $285/mo
Gemini 2.5 Pro68.8% SWE-bench
P50 TTFT: 340ms • $150/mo
DeepSeek R1 (BYOK)65.4% SWE-bench
P50 TTFT: 520ms • $48/mo
+ [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)
Vanna AI vs WrenAI: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
Vanna AIWrenAI
| Evaluation Dimension (Weight) | [A] Vanna AI | [B] WrenAI |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 94.8 / 100 | 95.1 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 28.5 / 30 23,817+ GitHub stars under a permissive MIT license that embeds into any Python, FastAPI, Slack, or Streamlit app | 28.6 / 30 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%) | 23.9 / 25 Two-step RAG architecture (vn.train on DDL + golden SQL, then vn.ask) achieves 89.6% accuracy at 580ms latency | 24.0 / 25 91.4% execution accuracy on multi-table enterprise schemas plus automatic AI chart & BI dashboard generation |
| Pillar 3: Token & RAM Efficiency (25%) | 23.3 / 25 100% Free / MIT • Works with any vector DB (Qdrant, Chroma, PGVector) and any SQL engine (Snowflake, BigQuery, Postgres, DuckDB) | 23.4 / 25 Open Source / Cloud • 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%) | 19.1 / 20 Zero-retention configurable; upstream index: vanna-ai/vanna | 19.1 / 20 Open/BYOK telemetry control; upstream index: Canner/WrenAI |
| Seat Pricing & Fast-Pool Economics | 100% Free / MIT | Open Source / Cloud |
| Verified Production Trade-Off | Requires golden SQL training examples | Multi-container Docker uses ~1.6GB RAM |
| Context Compression Fix (-38% Tokens)Verified Production Failure Mode | Send CLAUDE.md + .cursorrules ($0) ↓ | Send CLAUDE.md + .windsurfrules ($0) ↓ |
Velocity: Vanna AI (28.5/30)
•
Efficiency: WrenAI (23.4/25)
1-Tap Verdict & TCO Simulator
Select your primary engineering priority:
Team Size:
ACTIVE VERDICT: Vanna AI (94.8/100) (Saves ~$312/mo for 5 Devs with Root CLAUDE.md)
Velocity Winner (28.5/30): 23,817+ GitHub stars under a permissive MIT license that embeds into any Python, FastAPI, Slack, or Streamlit app
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 (Vanna AI (94.8/100) • vanna-ai/vanna • 5 Devs): + [CLAUDE.md] max_context_tokens: 9400 | ignore: node_modules, dist, .git, *.lock (-58% token bloat) + [Verdict TCO] vanna-ai/vanna | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)
.cursor/rules/architecture.mdc
litellm_router.yaml (-45% API Spend)
.github/workflows/ai-ci-gate.yml
promptfooconfig.yaml (50-Probe OWASP)
Interactive Workload & TCO Simulator: Vanna AI vs WrenAI by Repo ScaleConfigure ▾
INTERACTIVE WORKLOAD & TCO CALCULATOR
Est. Spend: ~$20 to $38/dev/mo (Seat + Fast Pool)
Which Wins for Your Specific Engineering Workload?
RECOMMENDED FOR YOUR WORKLOAD: Vanna AI (94.8/100)
23,817+ GitHub stars under a permissive MIT license that embeds into any Python, FastAPI, Slack, or Streamlit app - pair with a production CLAUDE.md + CLAUDE.md spec to prevent P99 tail-latency and cost spikes.
+ [CLAUDE.md] max_context_tokens: 9400 | ignore: node_modules, dist, .git, *.lock (-58% token bloat)
1-Tap Engineering Poll (Optional)
What is your main goal while comparing Vanna AI and its alternatives today?
DECISION GUIDE
When to pick each, and when to skip both.
Three plain-English paths: the case for Vanna AI, the case for WrenAI, and the free bring-your-own-key route that avoids seat fees entirely.
When to Pick 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 Pick 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%)
When to Skip Both (BYOK OSS)
Aider / Cline- $0/mo Seat Fee: Pay pure upstream Anthropic/DeepSeek/OpenAI API rates or run local Ollama models for $0.
- Zero IDE Lock-In: Keep standard VS Code, Neovim, or JetBrains with deterministic git commits.
- 45% Lower API Burn: Route simple edits to Haiku/DeepSeek and hard diffs to Sonnet/Opus via LiteLLM.
DROP-IN REPO CONFIGS & FOUNDER VERIFICATION
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.
PRODUCTION .CURSORRULES, CLAUDE.MD & COST-ROUTER PACK
Scan Your Repo →
Wire Vanna AI or WrenAI Into Your Repository & Cut Infra / API Spend by 45%
Export your repository's production CLAUDE.md, .cursor/rules/architecture.mdc, and CLAUDE.md in one click.
FREE STARTER ($0 EMAIL BRIDGE)INSTANT
$0 • Root CLAUDE.md
Email yourself a starter CLAUDE.md context map to prune node_modules and build bloat.
DROP-IN REPO CONFIGSONE-TIME
$4.99 / $9.99 • Config Packs
Complete Pack: scoped .cursor/rules/*.mdc, CLAUDE.md, LiteLLM 45% Router & CI Gate.
FOR TOOL FOUNDERSVERIFIED BOOST
$49 • Badge + X Thread
Verified Tier-1 Scorecard Badge, README SVG & automated X comparison thread on @mulak.
Building a commercial AI devtool? Pin your verified benchmark scorecard on this shootout for 30 days.Claim $149 Vendor Category Pin →
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: 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 28.5/30, Quality 23.9/25, Efficiency 23.3/25, Privacy/SLA 19.1/20) and WrenAI scores 95.1/100 (Velocity 28.6/30, Quality 24.0/25, Efficiency 23.4/25, Privacy/SLA 19.1/20).
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://pulse-forge.io/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.
More Coding Agent & Infrastructure Head-to-Head Shootouts
View All 11 AI Coding Agents →Cursor vs ClineClaude Code vs ClineClaude Code vs WindsurfClaude Code vs AiderOpenAI Codex CLI vs Claude CodeWindsurf vs ClineZed AI vs CursorRoo Code vs ClineCursor vs GitHub CopilotClaude Code vs GitHub CopilotOpenCode vs Claude CodeBlock Goose vs Claude CodeContinue.dev vs ClineOpenHands vs Claude CodeAider vs ClineOpenHands vs AiderCursor vs AiderOpenCode vs AiderCursor vs WindsurfClaude Code vs CursorOpenHands vs SWE-agentContinue.dev vs TabbyZed AI vs Void EditorRoo Code vs Kilo CodeVercel AI SDK vs CopilotKitLonghorn vs Rook CephContext7 MCP vs RepomixPlaywright MCP vs Browser-UseCodeRabbit vs Qodo PR-AgentCodeRabbit vs GreptileSemgrep vs SonarQubeLiteLLM vs OpenRouterLiteLLM vs TensorZeroQdrant vs pgvectorQdrant vs WeaviateMilvus vs pgvectorLanceDB vs QdrantOllama vs llama.cppvLLM vs TensorRT-LLMSGLang vs OllamaLangfuse vs Arize PhoenixLangfuse vs LangSmithPydanticAI vs LangGraphBAML vs InstructorSupabase vs NeonValkey vs RedisOllama vs vLLMSuperwhisper vs GranolaE2B vs DaytonaCrewAI vs LangGraphFastMCP vs MCP-AgentQdrant vs MilvusUnsloth vs AxolotlPydanticAI vs InstructorMem0 vs Graphiti (Zep)Cognee vs Letta (MemGPT)Pipecat vs LiveKit AgentsvLLM vs SGLangBrowser-Use vs StagehandCrawl4AI vs FirecrawlLangfuse vs HeliconeLiteLLM vs Portkey GatewayPromptfoo vs DeepEvalMinIO vs SeaweedFSn8n vs ActivepiecesAppsmith vs ToolJetPlausible Analytics vs UmamiCoolify vs DokkuBruno vs HoppscotchDocling vs MarkerDocling vs UnstructuredDify vs n8nOpen WebUI vs AnythingLLMLangGraph vs AutoGenAgno vs CrewAILightRAG vs Microsoft GraphRAGRAGFlow vs LlamaIndexBolt.diy vs DyadUnsloth vs LLaMA-FactoryTRL vs Axolotlfaster-whisper vs whisper.cppFish Speech vs F5-TTSVanna AI vs WrenAIPandasAI vs MindsDBQwen2.5-VL vs InternVL 2.5MiniCPM-o 2.6 vs Moondream 2BOutlines vs BAMLNVIDIA NeMo Guardrails vs Guardrails AITemporal vs InngestTrigger.dev vs HatchetDeepEval vs RagasComet Opik vs Arize PhoenixRepomix vs GitingestUI-TARS Desktop vs Microsoft OmniParser V2Cua vs Open InterpreterByteDance DeerFlow vs GPT-ResearcherStanford STORM vs Open Deep ResearchApple MLX vs Mozilla llamafileWebLLM vs Transformers.jsComposio vs NangoStripe Agent Toolkit vs Coinbase AgentKitBGE FlagEmbedding vs FlashRankStanford ColBERT vs Byaldi (ColPali)KTransformers vs NVIDIA TensorRT-LLMLMCache vs MooncakeQodo PR-Agent vs ReviewdogSemgrep vs Qodo PR-AgentKeploy vs Qodo-CoverHypothesis vs SchemathesisStanford DSPy vs AdalFlowTextGrad vs Argilla DistilabelGhostty vs WezTermWave Terminal vs AIChat Rust CLIassistant-ui vs ChainlitCherry Studio vs LibreChatJan Local AI vs Chatbox AIProjectDiscovery Nuclei vs OWASP ZAPAqua Trivy vs GitleaksAmazon Chronos-Bolt vs Google TimesFM 2.0Unit8 Darts vs Meta ProphetDaft Multimodal Dataframe vs PixeltableApache DataFusion vs Delta-rs (Delta Lake Rust)SkyPilot vs KubeRay OperatorCNCF Volcano vs CNCF HAMi (vGPU)ast-grep vs GritQLExcalidraw vs tldraw SDKChatwoot (+ Captain AI) vs Typebot.ioNovu Notification Engine vs Listmonk (Go Binary)GrowthBook vs UnleashAuthentik vs Keycloak (CNCF)Meilisearch (Rust) vs Typesense (C++)ClickHouse (+ ClickStack OTel) vs VictoriaMetrics (+ VictoriaLogs)Redpanda (C++ Seastar) vs NATS.io (+ JetStream)Dagster (Software-Defined Assets) vs Prefect 3.0Infisical vs HashiCorp Vault (+ OpenBao)Traefik Proxy vs Caddy ServerValkey (Linux Foundation) vs Dragonfly (C++ Shared-Nothing)FalkorDB (GraphBLAS Cypher) vs Neo4j (+ KùzuDB Embedded)Synthetic Data Vault (SDV) vs DistilabelPerplexica / Vane vs SearXNG (Private Metasearch)Crawl4AI (Async Playwright) vs Spider (Rust Multi-Threaded)Docling (IBM Research) vs MinerU (OpenDataLab)Bytebase (Database DevOps) vs Atlas (Declarative Migration)Headscale vs NetmakerSupabase vs PocketBaseSentry vs SigNozKeep vs Uptime KumaStrapi vs Payload CMSInfisical vs OpenBaoGitea vs GitLab CETiDB vs YugabyteDBCilium vs IstioHarbor vs ZotTrino vs StarRocksOpenTofu vs PulumiOpenFaaS vs Knative ServingMLflow vs ClearMLFeast vs HopsworksAirbyte vs MeltanoDagger vs Drone CIGreat Expectations vs Soda CoreHazelcast vs Apache IgniteDuckDB vs libSQLBentoML vs KServeGrafana Loki vs OpenSearchLiveKit vs MediasoupGrafana Pyroscope vs ParcaOpenCost vs InfracostProwler vs CheckovTruffleHog vs GitleaksFRP vs Cloudflaredk6 vs LocustDocusaurus vs AstroFormbricks vs Typebot.ioRestic vs KopiaNetdata vs PrometheusOpenReplay vs PostHogJaeger vs TempoBunkerWeb vs Coraza WAFArgo CD vs Flux CDKeycloak vs ZitadelKong vs Apache APISIXTemporal vs BullMQCoder vs DaytonaAqua Trivy vs Anchore GrypeMicrosoft Playwright vs CypressPgBouncer vs PgCatWazuh vs SuricataAnsible vs SaltStackApache Airflow vs DagsterGrowthBook vs UnleashVictoriaMetrics vs TimescaleDBFeast vs HopsworksDebezium vs Redpanda ConnectKong vs TraefikSuperTokens vs Ory KratosPixie vs Grafana BeylaStarRocks vs Apache DorisMemgraph vs NebulaGraph