Analytics & BI · Data & Analytics
Should you build or buy Semantic / Metrics Layer?
A semantic or metrics layer is a centralized data modeling layer that defines business metrics, such as revenue, churn rate, and monthly active users, in a single place so that every downstream tool, BI platform, or AI agent calculates them the same way from the same underlying logic.
The build-vs-buy decision for the Semantic / Metrics Layer turns on how much proprietary business logic lives in your metric definitions and how far open-source tooling like dbt's MetricFlow has come at delivering production-grade serving; the scale of your dbt investment and the number of downstream consumers settle it quickly.
Build it, buy it, or bridge?
When building makes sense
Building a semantic layer is the natural path for teams already running dbt. MetricFlow is open-source, actively maintained, and in production at enough organizations that it has stopped being a risk and started being the default. Metric definitions — how you calculate MRR, what counts as an active user, which edge cases get excluded from churn — are genuinely proprietary. A competitor with access to your metric definitions would understand your business logic in ways that matter. Owning this layer means faster iteration when business definitions change, and direct control over how metrics are exposed to BI tools, AI agents, and embedded applications. The cost case is straightforward: MetricFlow is free, and for a team already paying for dbt Cloud or running self-hosted dbt, adding the semantic layer costs engineering time, not license fees. The 3-5x cost advantage over managed Cube or AtScale is real and persistent. The Open Semantic Interchange specification, now with broad vendor backing, also reduces the risk that building on MetricFlow today creates lock-in problems tomorrow.
When buying makes sense
Buying makes sense when the requirement goes beyond metric definition into managed serving, governance, and cross-tool query APIs. Cube Cloud and AtScale add a query layer that sits between the semantic model and its consumers, handling caching, access control, and the API surface that embedded analytics or third-party BI tools need. If the analytics layer serves external customers rather than internal analysts, or if AI-assisted exploration is a product requirement rather than an internal tool, managed platforms are materially ahead of what self-hosted MetricFlow delivers today. Buying also makes sense for teams that don't have established dbt infrastructure, where the starting point is a managed cloud data warehouse and no existing transformation layer. In that case, rebuilding the path to get to MetricFlow may take longer than simply purchasing a platform that bundles data modeling and serving in one product. The question is whether the governance and API surface you're buying is functionality you'll actually use.
The desk read
Metric definitions encode business logic that's genuinely proprietary. How you calculate MRR, what counts as an active user, which edge cases get included in churn rate: these aren't generic questions and a consistent, centrally defined answer to them is increasingly valuable as more tools consume the same metrics. dbt's MetricFlow is open-source and in production at enough organizations that it's stopped being a risk and started being the default path for teams already using dbt. The build case gets serious when your team has dbt expertise, when metric consistency across BI tools and downstream AI consumers matters, and when you'd rather own the definition layer than depend on a vendor's interpretation.
Cube Cloud and AtScale add managed serving, governance features, and cross-tool query APIs that go beyond what MetricFlow provides out of the box. Buying earns its keep when you need embedded analytics or API exposure for external consumers, or when your team lacks the infrastructure capacity to run a self-hosted semantic layer reliably. The Open Semantic Interchange specification, with broad industry backing, is worth watching: it signals that the metric definition layer is converging toward a standard that multiple vendors and open-source tools will implement, which changes the lock-in risk of building on MetricFlow today.
Frequently asked
What is a Semantic / Metrics Layer?
A semantic or metrics layer is a centralized data modeling layer that defines business metrics, such as revenue, churn rate, and monthly active users, in a single place so that every downstream tool, BI platform, or AI agent calculates them the same way from the same underlying logic.
When does building a Semantic / Metrics Layer make sense?
Building makes the most sense for teams already running dbt. MetricFlow is open-source, production-ready, and free, making the economics clear for organizations that want to own their metric definitions and expose them consistently across BI tools and AI consumers.
When does buying a Semantic / Metrics Layer make sense?
Buying makes sense when you need managed serving, cross-tool query APIs, or embedded analytics capabilities beyond what MetricFlow provides, or when your team lacks existing dbt infrastructure and wants a single platform that handles both modeling and serving.
What are the main Semantic / Metrics Layer vendors?
Representative vendors include Cube Cloud, AtScale, Snowflake Semantic Views, Databricks Metric Views. B4 Pro scores the full set.
What is the Open Semantic Interchange specification?
The Open Semantic Interchange spec is an emerging standard, backed by 40+ vendors and projects, for how metric definitions are shared across different tools and platforms. Its broad adoption signals that building on open standards like MetricFlow today carries less vendor lock-in risk than it did a few years ago.