SIGNLD vs dbt Semantic Layer: code vs graph metrics

The dbt Semantic Layer defines a metric once in YAML and serves it consistently to every BI tool through MetricFlow. SIGNLD skips the modelling step entirely and resolves an answer directly from connected systems, which makes it a dbt semantic layer alternative for questions that were never modelled in the first place.

TL;DR

The dbt Semantic Layer defines metrics as code in YAML on top of dbt models, then serves those metrics consistently to any connected BI tool through MetricFlow. SIGNLD is a decision intelligence platform that connects read-only to 800+ business systems, resolves entities across them, and returns a Decision Brief with source citations and a confidence signal. Choose the dbt Semantic Layer if your data is already modelled in dbt and you need one metric definition everywhere. Choose SIGNLD if the question spans systems dbt never touched.

For the wider context, see our roundup of the best business knowledge graph platforms in 2026.

What is the dbt Semantic Layer actually built for?

The dbt Semantic Layer lets an analytics engineer define a metric, such as monthly recurring revenue, once in YAML on top of models already built in dbt. MetricFlow, the engine underneath it, compiles that definition into SQL and serves the result consistently to whichever BI tool queries it, so a metric defined once looks the same in every downstream dashboard.

The genuine strength here is real. dbt gives engineering teams testing, lineage, version control, and one consistent metric definition across every BI tool, solving the common problem of two dashboards reporting different numbers for the same metric name. Because definitions live in the same repository as the models, a change to a metric is reviewed and tracked like any code change.

dbt Core is open source and free to run. dbt Cloud publishes tiers on its site, including a free developer plan and paid Team and Enterprise plans, and the Semantic Layer requires a paid plan rather than the free tier. All of this only covers data already modelled inside dbt.

What is SIGNLD actually built for?

SIGNLD is a decision intelligence platform from Inzata Analytics. It connects read-only to the systems a company already runs, from ERP and CRM through the spreadsheets an operations team maintains, and resolves the same customer, invoice, or job across all of them inside a private Knowledge Graph. See the underlying concepts and the list of supported connectors for specifics.

Nobody writes a metrics YAML file first. A leader asks a plain-language question and gets a Decision Brief: the finding, evidence linked back to source records, a confidence score, and a recommended action. Inference runs on a single-tenant AWS Bedrock instance, using a private LLM powered by AWS Bedrock that is never trained on your data. You can connect the first system in 15 minutes and get a first answer in minutes, without a warehouse or a dbt project.

SIGNLD's target user is a CEO, CFO, or COO at a 10 to 500 employee company with no dedicated data team needing an answer now, not a backlog.

Where the two genuinely differ

The core difference is scope. The dbt Semantic Layer only knows what dbt has modelled. If a metric depends on a system nobody has piped into the warehouse, or a spreadsheet the ops team keeps outside the data stack, the Semantic Layer has nothing to define, because MetricFlow only compiles against models that already exist.

SIGNLD resolves entities as each system connects, without requiring that data pass through a modelling layer first. A question about why churn moved this quarter can draw on a CRM, a billing system, and a support ticket tool at once, even if none of those three were ever loaded into a warehouse or modelled in dbt.

A second difference is how the definition is written. dbt's metrics are YAML, checked into Git, tested, and reviewed like code, producing an audit trail of every change. SIGNLD has no equivalent file; it resolves the relevant records directly and shows evidence behind an answer instead of a versioned formula.

A few structural differences worth naming directly:

dbt Semantic Layer metrics are written in YAML by an analytics engineer; SIGNLD resolves entities automatically as systems connect.
MetricFlow only compiles against models already built in dbt; SIGNLD works without a data warehouse or a modelling project.
dbt's metric changes go through Git review and tests; SIGNLD's answers cite source records directly rather than a versioned formula.
The dbt Semantic Layer serves BI tools that query it; SIGNLD serves the executive asking the question directly.
dbt Cloud's Semantic Layer requires a paid plan; SIGNLD's plans are published separately on its own pricing page.

	dbt Semantic Layer	SIGNLD
what it models	metrics defined in YAML on top of models already built in dbt, compiled by MetricFlow	business entities, metrics, and their relationships resolved automatically as systems connect
who builds it	an analytics engineer writing and testing metric definitions in the dbt project	no one authors it, entity resolution runs as part of each read-only connection
time to first cross-system answer	weeks to months, after the dbt models and metric YAML are built and tested	minutes after the relevant systems connect
source traceability	lineage back through dbt models to the warehouse tables they are built on	citations back to the source record in the originating system, with a confidence signal
where inference runs	no inference, MetricFlow compiles metric definitions into SQL against the warehouse	a single-tenant AWS Bedrock instance, private LLM powered by AWS Bedrock, never trained on your data
who it is for	analytics engineering teams standardizing metrics across BI tools on a modelled warehouse	companies of 10 to 500 employees running 10 to 40+ systems with no dedicated data team

Who has to build the model?

In a dbt Semantic Layer setup, an analytics engineer first builds the underlying dbt models, the transformations that turn raw warehouse tables into clean, tested entities, then writes metric definitions in YAML on top of them. Every new metric, and every change to how one is calculated, is a pull request reviewed and tested before it reaches a dashboard.

In SIGNLD, nobody writes that YAML or the models beneath it. Connecting a system triggers automatic entity resolution, matching the same account across a CRM and a billing system without an engineer writing the join. A leader still sees the evidence behind an answer, but the modelling work dbt assigns to an engineering team does not exist as a separate step in SIGNLD.

How long until you get a cross-system answer?

With the dbt Semantic Layer, time to a first cross-system answer depends on how much of the business is already modelled in dbt. A team with mature models and existing metric definitions can add a new metric in days. A team starting without dbt models in place faces a project measured in months, since transformations must be built and tested before a metric can sit on top of them.

With SIGNLD, the first system connects in 15 minutes and the first answer follows in minutes, because entity resolution runs automatically as data streams in. A question spanning sales pipeline, support tickets, and a spreadsheet does not wait on a dbt project or a new metric definition.

Where the dbt Semantic Layer is the better choice

The dbt Semantic Layer is genuinely better when an engineering team already has a mature dbt project and needs one governed metric served consistently across several BI tools. If two teams currently report different numbers for the same metric because each built its own SQL, defining that metric once in MetricFlow and serving it everywhere solves a real, common problem.

Teams with dbt testing, lineage, and version control already in place get more value from extending that investment than from adopting a separate tool. That value depends on the data already being inside dbt: a question about an unmodelled system returns nothing until an engineer builds that model, and the Semantic Layer requires a paid dbt Cloud plan.

What running both looks like

Some organizations keep the dbt Semantic Layer as the governed source for metrics already living in a well-modelled warehouse and feeding multiple BI tools, while SIGNLD answers cross-system questions that touch systems outside that warehouse. dbt stays the system of record for tested, version-controlled metrics; SIGNLD answers the question a CFO asks about a system that was never part of the dbt project.

Your data stays yours in SIGNLD through read-only connections, a single-tenant AWS Bedrock instance for the private LLM, and a policy of never training on customer data. See /security for specifics, and /concepts for how the Knowledge Graph and Topic model fit together.

Key takeaways

The dbt Semantic Layer defines metrics as code in YAML on top of dbt models, then serves those metrics consistently to any connected BI tool through MetricFlow.
SIGNLD is a decision intelligence platform from Inzata Analytics.
The dbt Semantic Layer is genuinely better when an engineering team already has a mature dbt project and needs one governed metric served consistently across several BI tools.
An analytics engineer, usually the same person who builds and maintains the dbt models underneath them.
If your question spans systems that were never modelled in dbt and you still need an answer this week, start with the tool that resolves the model for you.

FAQ

Is the dbt Semantic Layer free to use?

dbt Core, which builds the underlying models, is open source and free. The Semantic Layer itself is part of dbt Cloud and requires a paid plan; dbt Cloud's tiers, including a free developer plan and paid Team and Enterprise plans, are published on dbt's site, but the Semantic Layer is not included on the free tier.

Do I need a data warehouse to use SIGNLD?

No. SIGNLD works without a data warehouse and connects read-only to operational systems and spreadsheets directly. The dbt Semantic Layer, by contrast, only defines metrics on top of models already built inside a warehouse through dbt.

Can SIGNLD replace dbt for a team that already has models built?

Not necessarily. Many teams keep dbt models and the Semantic Layer for metrics that are already well modelled, and use SIGNLD for questions reaching into systems those models do not cover, such as a spreadsheet or a newly added SaaS tool.

Who typically writes dbt metric definitions?

An analytics engineer, usually the same person who builds and maintains the dbt models underneath them. Metric YAML is code, checked into Git and tested, which is part of why it takes ongoing specialist time to keep current as the business changes.

Which is faster to get a first cross-system answer?

SIGNLD, in most cases, because it does not require data to be modelled first. The dbt Semantic Layer is fast for a metric already built and tested in dbt, but a question outside the existing models needs new modelling work before it returns anything.

Try SIGNLD free

If your question spans systems that were never modelled in dbt and you still need an answer this week, start with the tool that resolves the model for you. Try SIGNLD free and connect a system in minutes, or Browse all articles for more on how the Knowledge Graph compares to other approaches.

SIGNLD vs the dbt Semantic Layer: metrics as code vs metrics as graph

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