Open-Source Databases · rilldata

rill

Rill is an open-source business intelligence platform that combines a semantic layer with OLAP engines (DuckDB, ClickHouse) to enable fast data exploration. It supports agent-first workflows, allowing AI systems and users to query metrics via natural language or code, with deployment options for local development or cloud hosting.

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Source: GitHub — github.com/rilldata/rill

2.7k

GitHub stars

184

Forks

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	rilldata/rill
Owner	rilldata
Primary language	Go
License	Apache-2.0 — OSI-approved
Stars	2.7k
Forks	184
Open issues	207
Latest release	v0.87.8 (2026-07-03)
Last updated	2026-07-08
Source	https://github.com/rilldata/rill

What rill is

Built in Go with a SvelteKit frontend, Rill provides a SQL-based semantic layer (YAML-defined dimensions, measures, time grains), connectors to 20+ data sources, incremental ingestion, row-level access policies, and integrates with external OLAP engines. Includes MCP server support for agent-driven analytics and REST APIs for custom dashboards.

Quickstart

Get the rill source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/rilldata/rill.git2cd rill3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Agent-Assisted Analytics & BI Automation

Deploy Rill as a semantic layer for AI agents (Claude, ChatGPT) to autonomously author dashboards, models, and security policies via BI-as-code (YAML + SQL). Agents can query metrics in natural language through the MCP server interface.

Real-Time Operational Analytics at Scale

Leverage ClickHouse or DuckDB backends for sub-second queries across billions of rows. Ideal for cost monitoring, event analytics, bidstream analysis, and customer-facing embedded dashboards with strict latency requirements.

Multi-Tenant SaaS Metrics & Embedded Analytics

Use row access policies for per-user/per-group data control, custom REST APIs, and embeddable dashboards to ship metrics directly into your product. Git-backed deployments support rapid iteration and CI/CD integration.

Implementation considerations

Start with `rill init` to scaffold a local project; verify OLAP engine choice (DuckDB for <1TB, ClickHouse for >1TB) and data source connectors before committing to a deployment pattern.
Define a semantic layer governance model early: who owns YAML dimension/measure definitions, versioning strategy, and rollout process to avoid metric divergence across teams.
Plan for incremental ingestion configuration if datasets are large; full refreshes may incur latency or cost overhead depending on OLAP backend and data source volume.
Row access policies require clear identity mapping (user → groups → row filters); test policies in staging before enforcing in production.
Allocate time for agent prompt engineering and MCP server configuration if using AI-driven querying; natural language reliability depends on semantic layer clarity and model descriptions.

When to avoid it — and what to weigh

Proprietary BI Tool Lock-in Required — If your org mandates vendor BI solutions (Tableau, Looker) with commercial support SLAs, Rill's open-source model and community support may not align with enterprise procurement or SLA requirements.
Zero Data Transformation Needed — If your analytics queries require no data modeling or SQL transformation (purely ad-hoc exploration), tools like metabase or Superset with simpler UX may be faster to deploy. Rill's strength is in semantic consistency via YAML models.
Isolated Legacy Data Warehouse — Rill requires external OLAP engines or uses managed DuckDB/ClickHouse. If your data lives only in legacy warehouses (Teradata, Netezza) without connectors, integration friction is high. Check connector support first.
Minimal DevOps/Engineering Resources — Rill assumes some engineering involvement (YAML, SQL, Git workflows, model maintenance). Teams with no data engineers and purely business-user BI teams may struggle with the code-first paradigm.

License & commercial use

Apache License 2.0 (Apache-2.0): permissive OSI-approved license. Allows commercial use, modification, and distribution with attribution and no warranty. Source must remain available if distributed.

Apache 2.0 permits commercial use, including building closed-source products on top of Rill. However, review the license terms carefully: any distributed derivative must include Apache license notice and state material changes. No warranty or indemnification is provided. For mission-critical deployments, evaluate whether community support or commercial services (if available from Rill Data Inc.) are required; this cannot be confirmed from the data provided.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Row-level access policies are defined in YAML and enforce per-user/per-group data filtering. No statement of security audit, penetration testing, or vulnerability disclosure policy is evident from the data. OLAP engine security (ClickHouse, DuckDB) is separate responsibility. Credentials for data sources must be managed securely (mechanism not detailed). Evaluate authentication/authorization for deployed dashboards and APIs before handling sensitive data.

Alternatives to consider

Metabase

Simpler, UI-first BI tool. Good for self-service analytics without requiring SQL/YAML. Less agent-friendly; weaker semantic layer and code-as-infrastructure support.

Superset (Apache)

Open-source, SQL-based BI with lighter semantic modeling. Easier UI exploration but lacks Rill's agent-first capabilities and semantic layer YAML consistency.

Looker / Tableau

Mature, commercial BI platforms with enterprise support, strong data governance, and certified connectors. Higher cost and vendor lock-in; less agent-native.

Software development agency

Build on rill with DEV.co software developers

Rill combines semantic modeling with agent interfaces for fast, governance-first BI. Start with local development or connect to your OLAP engine to explore real-time data—with or without AI.

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rill FAQ

Can I use Rill without external cloud infrastructure?

Yes. Rill Developer runs locally with managed DuckDB included, suitable for datasets <1TB and local exploration. For production or larger data, you'll need to provision an external OLAP engine (ClickHouse Cloud, MotherDuck, etc.) or self-host.

Does Rill handle user authentication and authorization?

Row-level access policies are defined in YAML for per-user/per-group filtering. Full authentication (SSO, SAML) and custom authorization backends: not detailed in README. Requires review of full documentation and Rill Cloud features.

What is the licensing cost for commercial use?

Rill core is Apache 2.0 (free for commercial use under the license terms). Rill Cloud is a separate service; pricing not provided in the data. Consult rilldata.com for cloud hosting and support costs.

Can agents (Claude, ChatGPT) directly query my Rill metrics?

Yes, via the MCP server interface. Agents connect to your semantic layer to perform conversational BI queries and author dashboards/models in YAML. Requires MCP configuration and agent-side setup.

Custom software development services

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If rill is part of your open-source databases roadmap, our team can implement, customize, migrate, and maintain it.

Ready to Build Agent-Driven Analytics?

Rill combines semantic modeling with agent interfaces for fast, governance-first BI. Start with local development or connect to your OLAP engine to explore real-time data—with or without AI.

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