Daft vs dbt Semantic Layer
Daft
daft.ai
dbt Semantic Layer
getdbt.comSide by side
| Figure | Daft | dbt Semantic Layer |
|---|---|---|
| Price from | Free | $0.09/mo |
| Free plan | Yes | Yes |
| Free trial | — | Yes |
| Open source | — | — |
| Web | — | Yes |
| macOS | Yes | — |
| Linux | Yes | — |
| API | — | Yes |
| Plans published | 1 | 5 |
| Spec score · Data Transformation Tools | 7.6 (#22) | 8.9 (#8) |
| Intended users | Eventual describes Daft as an open-source engine for engineers processing petabytes of multimodal data. | The product is presented for data teams seeking governed, consistent metrics for analytics, embedded applications, and AI use cases. |
| Purpose | Daft is a high-performance data engine for processing multimodal and structured data in AI workloads. | The Semantic Layer centralizes metric definitions on dbt models and automatically handles data joins for consistent use across downstream tools and applications. |
| Support | The documentation directs users to the Distributed Data Slack Community and the #daft-dev channel for contributor guidance. | The pricing page lists 24x5 support for Starter and priority support with SLAs for Enterprise. |
Plans
dbt Semantic Layer
- DeveloperFree
- dbt State add-on$0.09 a month
- Starter$100 a month
- EnterprisePriced on request
- Enterprise+Priced on request
In their own words
dbt Semantic Layer
The dbt Semantic Layer centralizes metric definitions on dbt models and handles joins so downstream tools and applications can use metrics consistently. MetricFlow generates SQL at query time from semantic model and metric configurations. Teams define those models and metrics in YAML as version-controlled, lineage-aware code. MetricFlow can optimize joins, rewrite queries to reduce compute and latency, and cache repeated queries. The product offers an API, the dbt MCP Server and integrations for BI tools, notebooks, spreadsheets, embedded analytics and AI systems. GraphQL and JDBC APIs and a Python SDK provide ways to query metrics and dimensions; Claude and ChatGPT can connect to governed metrics through the MCP server. Documented warehouse platforms include Snowflake, BigQuery, Databricks and Redshift. Defining and querying metrics requires a dbt Starter or Enterprise-tier account, and single-tenant accounts need account-representative setup and enablement. Starter includes 5,000 queried metrics per month; Enterprise and Enterprise+ each include 20,000. Starter costs $100 a month per user. The free Developer plan does not list the Semantic Layer.