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Bonnard

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Agentic analytics layer for B2B products with governed AI metrics

AnalyticsAI AgentsData & Databases
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Tracked since2026

The Bottom Line

Entry price

Free plan available, paid tiers above

Biggest pro

Open-source core with Apache 2.0 license

Biggest con

Early-stage product (founded 2025)

What is Bonnard?

Editorial review
Bonnard is an agentic analytics layer that enables B2B SaaS companies to embed governed AI analytics into their products. Define metrics once in YAML, deploy them across React charts, MCP servers, REST and GraphQL APIs with built-in row-level security and multi-tenancy. Connects to data warehouses with automatic DBT model detection and prevents hallucinated aggregations by having AI agents query governed metric definitions.

Available on: Web, macOS, Linux

how we evaluateSourcebonnard.dev ↗

Pros & Cons

Pros

  • Open-source core with Apache 2.0 license
  • Prevents AI hallucinations with governed metrics
  • Multi-interface: React, MCP, REST, GraphQL
  • Built-in multi-tenancy and row-level security

Cons

  • Early-stage product (founded 2025)
  • Cloud pricing not publicly listed
  • Limited to B2B analytics use cases
  • Requires data warehouse setup

Key Features

Semantic metric layer (YAML)Row-level security per tenantMCP server for AI agentsReact SDK for embedded chartsREST & GraphQL APIsDBT model auto-detectionAudit loggingSelf-hosted or cloud deployment

Pricing Plans

7-day Free Trial

Pricing checked Sep 15, 2026

Open Source

Free

  • Apache 2.0 license
  • Self-hosted via Docker Compose
  • CLI workflow
  • Core semantic layer

Cloud

Usage-based

  • Managed infrastructure
  • Scale daily API calls
  • 7-day free trial
  • Audit logging
Calculate your costView full pricing

Is Bonnard worth the price?

85/100

Bonnard's pricing is fair and generous, especially with a robust Open Source tier that provides significant value for free.

The Cloud tier's usage-based model offers flexibility, though specific pricing for API calls is missing. This structure is best for startups and SMBs looking for powerful analytics without high upfront costs.

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Still deciding?

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Bonnard FAQ

How does Bonnard prevent AI hallucinations in analytics?

Bonnard uses a governed semantic layer where metrics are pre-defined in YAML. AI agents query these governed definitions rather than raw database tables, ensuring accurate aggregations and preventing hallucinated results.

Can I self-host Bonnard?

Yes, Bonnard offers an open-source Apache 2.0 licensed version that can be self-hosted via Docker Compose. There is also a managed cloud option for teams that prefer not to manage infrastructure.

What data warehouses does Bonnard support?

Bonnard connects to standard data warehouses and automatically detects DBT models. It creates views and cubes from raw tables with built-in multi-tenant isolation.

How does the MCP server integration work?

Bonnard includes an MCP server that exposes your governed metrics to AI agents. Agents can query defined metrics through the Model Context Protocol, ensuring they always use accurate, governed data definitions.

What security features does Bonnard provide for multi-tenant analytics?

Bonnard enforces row-level security per tenant with token-based authentication mapping. Every query automatically respects tenant boundaries, and all access is logged for compliance auditing.

Source: bonnard.dev

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