Overview

The product puts metric definitions and join handling behind a common layer, so downstream consumers can use consistent measures rather than reimplementing them in each tool. That is a practical fit when several teams or applications rely on the same definitions; it is less attractive if you want a semantic layer independent of dbt accounts and workflows.

Key features

Teams define semantic models and metrics in YAML as version-controlled, lineage-aware code. MetricFlow turns those configurations into SQL at query time and handles joins. This makes metric logic easier to govern centrally, while requiring teams to maintain the definitions as code.

MetricFlow rewrites queries to reduce compute and latency, optimizes joins and caches repeated queries. These are sensible measures for avoiding unnecessary work, though the benefit will depend on each workload. GraphQL and JDBC APIs, plus a Python SDK, let clients query metrics and dimensions. The API, dbt MCP Server and out-of-the-box integrations serve BI, notebooks, spreadsheets, embedded analytics and AI systems; Claude and ChatGPT can connect to governed metrics through MCP.

Snowflake, BigQuery, Databricks and Redshift are supported data platforms. The cloud product is accessible through API and web platforms. dbt Labs lists SOC 2 Type II, ISO 27001:2022, ISO 27017, ISO 27018, ISO 42001, GDPR and CCPA credentials. Starter includes 24x5 support; Enterprise has priority support with SLAs.

Pricing

The Developer plan is 0.00 USD per free. It allows 1 developer seat, 3,000 successful models per month and 1 project, but does not include Semantic Layer access. It is useful for development, not for teams seeking this product’s metric features.

Starter costs 100.00 USD per month, billed per user/month. It includes 5 developer seats, 15,000 successful models per month, 5,000 queried metrics per month and 1 project, along with Semantic Layer access. The per-user charge and relatively small metric allowance make it best suited to a small deployment with predictable demand. Free plan and free trial options are offered, but the free plan does not include the layer.

Enterprise and Enterprise+ use custom pricing and each include 100,000 successful models and 20,000 queried metrics per month, with custom developer seat counts. Enterprise includes 30 projects; Enterprise+ includes unlimited projects. These tiers fit organizations needing more project capacity or higher allowances than Starter, though their custom pricing requires a quote.

Defining and querying metrics requires Starter or Enterprise-tier access. Single-tenant accounts also need setup and enablement from an account representative, an important consideration for organizations with that deployment arrangement.

Platforms

dbt Semantic Layer is cloud-deployed and supports API and web access. Its documented warehouse support covers Snowflake, BigQuery, Databricks and Redshift.

Who it's for

Choose it when a data team needs governed metrics reused across analytics, embedded products and AI, and is prepared to work within dbt’s account tiers and project structure. Teams wanting a free Semantic Layer tier, or a tool that does not require a dbt Starter or Enterprise account, should look elsewhere.

Pros and cons

Pro: YAML definitions are version-controlled and lineage-aware, supporting shared ownership of metric logic.
Pro: Join handling, APIs and integrations let multiple kinds of consumers use common metrics.
Pro: MetricFlow’s query rewrites, join optimization and caching target lower compute and latency.
Con: The free Developer plan excludes Semantic Layer access, so even a small team must pay for Starter.
Con: Starter is limited to 5,000 queried metrics per month and one project; teams exceeding those limits need a higher tier.
Con: Enterprise pricing is custom, making costs less straightforward to assess against a fixed per-user plan.

Alternatives

For a broader comparison, browse Semantic Layer Software, Database Documentation Tools and Data Transformation Tools.

Starburst Enterprise Context Layer is worth comparing if its free plan, which supports up to 3 clusters for ad hoc queries, better matches your starting needs. Kyvos is another option, with a paid plan billed at $0.41 per core hour on its cloud marketplace. For a free forever sandbox, Sema offers 1 connector, 1 user and 100 chat queries per month, with limits on saved reports and delivery features.

Cube may suit teams wanting a free forever starting tier with up to 5 workbooks and 1,000 daily requests. Kyligence is a paid alternative with a free trial. Looker may appeal to organizations seeking a paid product with a Standard plan that includes one production instance, 10 Standard Users and 2 Developer Users.

Rill Metrics SQL is a paid alternative with a $20 / seat / month Starter plan for up to 20 seats and 3 projects, though embedded analytics are previews only and compute is billed separately. Malloy is a free option for readers comparing products across API, extension, desktop and web platforms.

Verdict

dbt Semantic Layer is a strong choice for dbt-based data teams that need governed metric definitions reused across tools, applications and AI. Its central advantage is combining shared metric logic with query and join handling; the main reason to look elsewhere is the paid access requirement and Starter’s monthly query and project limits.

	dbt Semantic Layer	Quaeris Semantic Layer
Free plan	Yes	No
Free trial	Yes	Yes
Paid from	$100/mo	$1,000/mo
Platforms	api, Web	api, Web
Free plan	Yes	No
Governed metrics	Yes	Yes
Dimensions and joins	Yes	Yes
Query API	Yes	Yes
Deployment model	cloud	hybrid
Paid from		1,000 /mo
Row-level security		Yes
Certified models		Yes

dbt Semantic Layer vs Quaeris Semantic Layer

dbt Semantic Layer

Quaeris Semantic Layer