Decision comparison
Metabase vs Omni Analytics
Metabase and Omni Analytics both document dashboards, AI-assisted data querying, semantic-model capabilities, embedded analytics, and governance controls, but their published commercial information differs. Metabase publishes monthly and yearly plans, including a free Open Source edition, $100-per-month Starter, $575-per-month Pro, and custom-priced Enterprise; it also offers a 14-day trial. Omni documents an analytics platform with natural-language querying, semantic modeling, SSO embedding, APIs, and MCP-server access, but its public materials do not disclose a price or a named Enterprise tier. A buying evaluation should test the required data connections, embedding workflow, user permissions, tenant isolation, AI workflow, and contract terms.
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are BI platforms.
Quick Comparison
| Decision factor | Metabase | Omni Analytics |
|---|---|---|
| Best For | Self-service BI and embedded analytics with interactive dashboards, a visual query builder, SQL, and an SDK for advanced embedded setups. | Analytics teams and product teams that need dashboards, point-and-click analysis, spreadsheets, SQL, a semantic model, and white-label embedding through SSO, APIs, or an MCP server. |
| Pricing | Community Edition is free, open-source and self-hosted, with unlimited users. Paid Metabase Cloud plans start with Starter at $100/month, or $90/month billed annually, including the first 5 users, then $6 per user/month. Pro is $575/month, or $517.50/month annually, including the first 10 users, then $12 per user/month. Enterprise is custom pricing starting at $20,000/year. Both paid plans offer a 14 days free trial. | Omni publishes no prices on its website. It sells enterprise contracts, and buyers contact sales for a quote. Verified 2026-10-30 against https://omni.co and https://omni.co/articles/self-serve-analytics-for-marketing-and-sales-best-tools-for-non-technical-teams-2026. |
| Deployment | Self-hosted deployment or Metabase Cloud. Metabase says Cloud handles setup, backups, and upgrades. | Cloud deployment details are not verified here. Omni documents product embedding through SSO, APIs, and an MCP server. |
| Learning Curve | Visual query builder for no-code queries, with a SQL editor available for more complex analysis. | Point-and-click analysis, spreadsheets, SQL, and natural-language querying are documented; implementation requirements should be evaluated for the intended semantic-model workflow. |
| AI Capabilities | Plain-English AI data querying through Metabot AI, SQL generation, and MCP-server access. | Natural-language querying with follow-up context. Omni also documents agents that create dashboards from prompts, generate workbook queries and draft measures, and draft semantic-model measures, dimensions, and relationships. |
| Data Governance | A governed semantic layer for canonical tables and metrics, plus granular permissions and row- and column-level security for multi-tenant analytics. | A semantic model for reusable metrics, along with role-based access and audit logs. |
Metabase
- Best For:
- Self-service BI and embedded analytics with interactive dashboards, a visual query builder, SQL, and an SDK for advanced embedded setups.
- Pricing:
- Community Edition is free, open-source and self-hosted, with unlimited users. Paid Metabase Cloud plans start with Starter at $100/month, or $90/month billed annually, including the first 5 users, then $6 per user/month. Pro is $575/month, or $517.50/month annually, including the first 10 users, then $12 per user/month. Enterprise is custom pricing starting at $20,000/year. Both paid plans offer a 14 days free trial.
- Deployment:
- Self-hosted deployment or Metabase Cloud. Metabase says Cloud handles setup, backups, and upgrades.
- Learning Curve:
- Visual query builder for no-code queries, with a SQL editor available for more complex analysis.
- AI Capabilities:
- Plain-English AI data querying through Metabot AI, SQL generation, and MCP-server access.
- Data Governance:
- A governed semantic layer for canonical tables and metrics, plus granular permissions and row- and column-level security for multi-tenant analytics.
Omni Analytics
- Best For:
- Analytics teams and product teams that need dashboards, point-and-click analysis, spreadsheets, SQL, a semantic model, and white-label embedding through SSO, APIs, or an MCP server.
- Pricing:
- Omni publishes no prices on its website. It sells enterprise contracts, and buyers contact sales for a quote. Verified 2026-10-30 against https://omni.co and https://omni.co/articles/self-serve-analytics-for-marketing-and-sales-best-tools-for-non-technical-teams-2026.
- Deployment:
- Cloud deployment details are not verified here. Omni documents product embedding through SSO, APIs, and an MCP server.
- Learning Curve:
- Point-and-click analysis, spreadsheets, SQL, and natural-language querying are documented; implementation requirements should be evaluated for the intended semantic-model workflow.
- AI Capabilities:
- Natural-language querying with follow-up context. Omni also documents agents that create dashboards from prompts, generate workbook queries and draft measures, and draft semantic-model measures, dimensions, and relationships.
- Data Governance:
- A semantic model for reusable metrics, along with role-based access and audit logs.
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Metabase | Omni Analytics |
|---|---|---|
| Docker Hub pulls(Product adoption) | 279.2M | Not available |
| GitHub commits, 90d(Product adoption) | 2.3k | Not available |
| GitHub stars(Product adoption) | 50,000+ | Not available |
| Search interest(Market interest) | 1 | Unavailable |
| Hacker News mentions, 90d(Community interest) | 11 | Not available |
| npm weekly downloads(Developer adoption) | 80.6k | 17 |
| Product Hunt comments(Community interest) | 30 | Not available |
| Product Hunt rating(Community interest) | 4.9/5 | Not available |
| Product Hunt reviews(Community interest) | 24 | Not available |
| Product Hunt votes(Community interest) | 314 | Not available |
| Stack Overflow questions(Community interest) | 374 | Not available |
| GitHub commits, 90d(Developer adoption) | Not available | 37 |
| GitHub stars(Developer adoption) | Not available | 68 |
| PyPI weekly downloads(Developer adoption) | Not available | 824 |
As of November 9, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Metabase
November 9, 2026Package vulnerabilities
npm · @metabase/embedding-sdk-react@0.65.2
0 vulnerabilities
across 1 package
Repository security score
github.com/metabase/metabase
7.1/10
Omni Analytics
November 9, 2026Package vulnerabilities
npm · @omni-co/model-local-editor@0.2.0 · PyPI · omni-python-sdk@0.1.11
0 vulnerabilities
across 2 packages
Repository security score
Not available
Interface Preview
Metabase
Omni Analytics
Feature Comparison
| Feature | Metabase | Omni Analytics |
|---|---|---|
| Core Analytics | ||
| Dashboards and exploration | Interactive dashboards, chart drill-through, a visual query builder, and a SQL editor. | Dashboards, custom visualizations, drill-downs, filters, point-and-click analysis, spreadsheets, and SQL. |
| AI data querying | Metabot AI supports plain-English data queries; Metabase also documents MCP-server access. | Natural-language querying with follow-up questions, plus MCP-server, API, and integration access to the semantic layer. |
| Semantic layer | Data Studio provides a governed semantic layer of canonical tables and metrics. | A semantic model supports defining metrics once and using them across customer instances. |
| Embedded Analytics | ||
| Embedding and developer integration | Supports embedded dashboards and an SDK for advanced embedded setups. | Documents SSO embedding, APIs, and an MCP server for white-label product use. |
| Customization | Documents white-labeling, dynamic styling, and interactive controls. | Documents CSS and Markdown customization for customer-facing analytics. |
| Tenant and access controls | Documents multi-tenant data segregation, granular permissions, and row- and column-level security. | Documents role-based access and audit logs. |
| AI Workflows | ||
| AI-assisted building | Metabase documents AI data querying and SQL generation; dashboard-generation capabilities are not verified here. | The Omni Agent can design and publish a dashboard from a prompt; the Workbook Agent can generate queries and draft measures; the Modeling Agent can draft measures, dimensions, and relationships. |
| AI governance context | Metabase documents a governed semantic layer of canonical tables and metrics. | Omni describes AI access to its semantic layer and governed definitions. |
| Commercial Information | ||
| Free edition and trial | Open Source is free with unlimited users, questions, and dashboards; Metabase offers a 14-day trial. | No public price, free-edition terms, or trial terms are listed in the documented commercial information. |
| Published plans | Starter is $100 per month and Pro is $575 per month; Enterprise has custom pricing. Yearly billing is advertised with a 10% saving. | Omni sells its analytics platform through sales quotes; no public price or named Enterprise tier is identified. |
Core Analytics
Dashboards and exploration
AI data querying
Semantic layer
Embedded Analytics
Embedding and developer integration
Customization
Tenant and access controls
AI Workflows
AI-assisted building
AI governance context
Commercial Information
Free edition and trial
Published plans
Which to choose
Metabase and Omni Analytics both document dashboards, AI-assisted data querying, semantic-model capabilities, embedded analytics, and governance controls, but their published commercial information differs. Metabase publishes monthly and yearly plans, including a free Open Source edition, $100-per-month Starter, $575-per-month Pro, and custom-priced Enterprise; it also offers a 14-day trial. Omni documents an analytics platform with natural-language querying, semantic modeling, SSO embedding, APIs, and MCP-server access, but its public materials do not disclose a price or a named Enterprise tier. A buying evaluation should test the required data connections, embedding workflow, user permissions, tenant isolation, AI workflow, and contract terms.
Best-fit scenarios
Choose Metabase if:
Consider Metabase when a free Open Source edition, self-hosting, or a published plan structure matters. The pricing page lists Open Source as free with unlimited users, questions, and dashboards; Starter at $100 per month; Pro at $575 per month; and Enterprise with custom pricing. Confirm user counts, per-user charges, yearly terms, deployment, and Enterprise licensing before purchase.
Choose Omni Analytics if:
Consider Omni when product embedding through SSO, APIs, or an MCP server and governed metrics across customer instances are central requirements. Omni also documents AI agents for dashboard creation, workbook queries and draft measures, and semantic-model drafting. Omni’s commercial terms are sold through sales quotes; its public materials do not list a public price or a named Enterprise tier. Confirm the quote, license model, deployment terms, support, and included capabilities.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
Can Metabase handle enterprise-scale deployments?
Metabase lists an Enterprise plan with custom pricing. Its pricing page lists all Pro features plus procurement help, a dedicated success engineer, an air-gap option, and a 1-day support SLA. Pro includes row- and column-level permissions, SSO, caching controls, usage analytics and auditing, multi-tenant embedded analytics, white-label readiness, and AI usage auditing and controls. Metabase also documents granular permissions for multi-tenant data segregation and supports self-hosted deployment as well as Metabase Cloud. Buyers should confirm the Enterprise quote, deployment arrangement, licensing scope, and the precise security and support commitments that apply to their implementation.
Does Omni Analytics require SQL knowledge?
Omni presents several ways to work with data: AI chat for natural-language questions, dashboards, point-and-click analysis with a field picker and chart editor, spreadsheets with formulas, forecasting and modeling, and raw SQL in an IDE with intelligent autocomplete. Its embedded-analytics page also describes report creation using AI, Excel calculations, SQL, or a point-and-click interface. These documented interfaces mean SQL is not the only way to explore or build analysis. Teams that want governed reusable definitions can use Omni’s semantic model, which the vendor describes as a place to define metrics once and use them across customer instances.
Which tool is suitable for embedded analytics in a SaaS product?
Both products document embedded analytics capabilities. Metabase describes embedding full dashboards and using an SDK for advanced embedded setups; it also documents white-labeling, dynamic styling, interactive controls, multi-tenant data segregation, and granular permissions. Omni describes white-label use within a product through SSO embedding, APIs, and its MCP server. Its embedded-analytics page additionally describes CSS and Markdown customization, role-based access, audit logs, and customer-facing reports. The appropriate choice depends on the integration and governance requirements a product team needs to validate, including the desired embedding method, identity flow, customization scope, tenant isolation, and commercial terms.
How do the AI features in Metabase and Omni Analytics compare?
Metabase documents plain-English AI querying through Metabot AI and access through an MCP server. Its pricing page lists AI questions and SQL generation for Open Source, Starter, and Pro plans, while its current pricing page describes asking questions with AI on a preferred model. Omni documents natural-language querying, follow-up questions whose context carries over, and AI access to its semantic layer through an MCP server, APIs, and integrations. Omni also describes agents that can build a dashboard from a prompt, generate workbook queries and draft measures, and draft semantic-model measures, dimensions, and relationships. Feature availability, model choices, and plan entitlements should be confirmed for the intended deployment.