I need to build a semantic layer for our BI tools. What's the best software for that?

If you're building a shared semantic layer underneath multiple BI tools, I’d shortlist Cube, dbt Semantic Layer, and AtScale. The right choice depends heavily on whether you want the semantic layer to be BI-agnostic or tied to your existing stack.

My recommendation

For a new, BI-agnostic semantic layer: cube.dev would be my first evaluation.

It is designed to sit between the warehouse and multiple consumers, centralizing metrics, dimensions, joins, permissions, and performance optimizations. It exposes the same semantic model through SQL and APIs, rather than requiring every BI tool to understand a proprietary modeling language.

AtScale, for example, explicitly supports Power BI, Excel, Tableau, Looker and other consumers from a common semantic model.

The key architectural question

I'd make the decision based on how many consumers need the semantic model.

If you have:

Warehouse → dbt → one BI tool

then putting the semantic layer inside that ecosystem may be simplest.

If you have:

Warehouse → semantic layer → Tableau + Power BI + Looker + embedded analytics + AI

then I'd strongly favor a standalone, BI-agnostic layer, with Cube and AtScale at the top of my evaluation list. A recent 2026 comparison makes essentially this distinction: dbt is particularly attractive when the workflow already revolves around dbt, while Cube is aimed at serving the same governed definitions to multiple consumers.

What I'd evaluate in a POC

Don't primarily compare dashboard features. Test these:

1. Metric definitions — Can you cleanly express revenue, ARR, retention, cohorts, etc.?
2. Relationships/joins — Can it prevent analysts from accidentally creating fanouts?
3. BI interoperability — Can Tableau, Power BI, Looker, etc. all consume the same definitions?
4. Security — Row/column-level policies and user-aware filtering.
5. Performance — Caching/pre-aggregations without forcing every BI tool to implement them.
6. Version control — Git, CI tests, code review, deployment.
7. Lineage — Can someone trace Revenue → semantic definition → dbt model → warehouse tables?
8. Ad-hoc analysis — Does the layer preserve flexibility instead of turning every new question into an engineering ticket?
9. AI readiness — If you're going to use AI analytics, can agents query the governed model rather than raw warehouse tables?
10. Portability — What happens if you replace your BI tool in two years?

That last point is particularly important: don't accidentally make your BI tool the semantic layer if you expect to have multiple BI consumers.

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.), current BI tools, whether you use dbt, and whether AI/embedded analytics are requirements, I can give you a much more specific Cube vs dbt vs AtScale architecture recommendation, including what I would actually deploy.

AtScale, for example, explicitly supports Power BI, Excel, Tableau, Looker and other consumers from a common semantic model.

The key architectural question

I'd make the decision based on how many consumers need the semantic model.

If you have:

Warehouse → dbt → one BI tool

then putting the semantic layer inside that ecosystem may be simplest.

If you have:

Warehouse → semantic layer → Tableau + Power BI + Looker + embedded analytics + AI

then I'd strongly favor a standalone, BI-agnostic layer, with Cube and AtScale at the top of my evaluation list. A recent 2026 comparison makes essentially this distinction: dbt is particularly attractive when the workflow already revolves around dbt, while Cube is aimed at serving the same governed definitions to multiple consumers.

What I'd evaluate in a POC

Don't primarily compare dashboard features. Test these:

The "best" software for a semantic layer largely depends on your existing data stack, how your team writes transformations, and which BI tools you plan to plug into it.

The top semantic layer software options on the market fall into distinct categories based on your architectural needs:

• What it is: A headless, API-first semantic layer that decouples data definitions from BI tools.

• Why it’s great: It works with practically any data warehouse (Snowflake, BigQuery, Postgres, Databricks) and outputs to any BI tool or custom application via SQL, REST, GraphQL, or MDX. It features robust caching, access control, and native support for feeding AI agents and LLMs.

• Best if: You have a multi-BI environment (e.g., using both Tableau and Superset, or embedded apps) and want a single, centralized place for metrics.

• What it is: Powered by MetricFlow, this lets you define metrics right alongside your SQL transformations inside your dbt Cloud project.

• Why it’s great: If your data engineering team already lives and breathes dbt, extending it to the semantic layer avoids learning a brand-new syntax. It guarantees that transformations and metric definitions share the exact same lineage.

• Best if: Your data stack is already built around dbt and your consumers use compatible BI tools or the dbt Semantic Layer APIs.

• What it is: Looker ’s proprietary modeling language, LookML, is arguably the most mature and battle-tested semantic layer in the industry.

• Why it’s great: It allows for version-controlled, highly complex relational modeling. Through Looker Modeler, you can even expose LookML definitions to external tools like Tableau or Excel.

• Best if: You are willing to use Looker as your primary BI/analytics interface.

• What it is: An enterprise-grade semantic layer with deep OLAP (Online Analytical Processing) roots.

• Why it’s great: It excels at high-performance live connectivity, allowing heavy business users to query massive cloud data warehouses directly from Microsoft Excel or Power BI via standard MDX/DAX interfaces without lag, using autonomous aggregates.

• Best if: Your business units refuse to leave Excel/Power BI, but your underlying data sits in a modern cloud warehouse.

• What it is: Native semantic capabilities built directly into platforms like Snowflake (Cortex/Semantic Views) and Databricks.

• Why it’s great: Zero data movement, managed security, and tight optimization for the underlying compute engine.

• Best if: You want to keep all governance and metric definitions strictly inside your cloud data platform, particularly to feed native AI tools.

To help narrow down the ideal choice, tell me:

• What is your primary data warehouse (Snowflake, BigQuery, Databricks, etc.)?
• Which BI tools do you need to support (Power BI, Tableau, Looker, open-source tools)?
• Is your team already using dbt for data transformations?

If your goal is one governed semantic layer that can serve multiple BI tools, I’d shortlist Cube, dbt Semantic Layer, and AtScale. The “best” choice depends heavily on whether you want the semantic layer to be dbt-native, BI/enterprise-oriented, or completely decoupled from your BI stack.

My recommendation

1. Cube — best default for a multi-BI environment

I’d start here if you have multiple BI consumers or expect to add embedded analytics/AI later. Cube is designed as a headless semantic layer rather than a BI application, with a SQL-first model and APIs for serving governed metrics. It also has an open-source core.

Why I like it:

• BI-tool agnostic
• Centralizes metrics, dimensions, joins and access rules
• Can serve BI, embedded analytics and AI from the same model
• Caching/pre-aggregations can sit in the semantic layer
• Good fit if you want the semantic model owned by your data team rather than by a particular BI vendor

The main downside is that you're adopting another platform to operate; it's not simply a feature inside your existing BI tool.

2. dbt Semantic Layer — best if you're already heavily invested in dbt

If your analytics engineering team already treats dbt as the source of truth, this is probably the lowest-friction option. Metric definitions live alongside your dbt models and are version controlled. dbt's MetricFlow handles the query generation.

I'd choose this when:

• dbt is already central to your stack
• Most consumption is conventional analytics/BI
• You don't need a highly independent serving layer
• You want semantic definitions managed as code alongside transformations

3. AtScale — best for large enterprise BI environments

I'd investigate AtScale if you're dealing with lots of enterprise users, Excel/Power BI, complex OLAP workloads, and strict governance. It has particularly strong connectivity into the Microsoft/Excel ecosystem and uses aggregate acceleration to improve performance.

What I would not do

I wouldn't automatically make your BI tool the semantic layer.

For example, LookML is excellent if you're standardized on Looker, and Power BI's semantic models are excellent within the Microsoft ecosystem. But if your requirement is specifically "we have several BI tools and want one definition of revenue/churn/ARR/etc. across all of them," a BI-native semantic layer creates coupling to that vendor.

Likewise, I would keep dbt and the semantic layer conceptually separate:

dbt: build and test the data Semantic layer: define and serve business meaning BI tools: visualize and explore it That separation tends to become particularly valuable when the same metrics eventually need to serve dashboards, spreadsheets, applications and AI agents.

A practical decision rule

The most important question I'd ask before choosing is: How many different things need to consume the semantic model? If it's just one BI tool, use that tool's native layer or dbt. If it's BI tool A + BI tool B + AI + embedded analytics, I'd strongly favor a standalone layer such as Cube.

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.), BI tools (Tableau/Power BI/Looker/Mode/etc.), whether you use dbt, and roughly how many data engineers/analysts you have, I can narrow this to a specific architecture and a 2–3 tool bake-off.

Choosing the "best" software for a semantic layer depends heavily on where your data already lives, which BI tools you use, and whether you are trying to feed traditional dashboards, AI agents, or both.

The market divides into a few distinct categories based on your architecture:

• Cube — The top choice for a headless, universal semantic layer . If you use multiple BI tools, need to expose metrics via APIs (SQL, REST, GraphQL), or want to feed LLMs/AI agents, Cube is decoupled from any specific visualization tool. It features a strong open-source core and handles caching and access control exceptionally well.
• dbt Semantic Layer — The go-to choice if your data team is already all-in on dbt (Data Build Tool) for transformations. It allows you to define metrics directly in your dbt code alongside your dimensions and queries them consistently across connected BI tools and data apps via the dbt Cloud ecosystem.
• Looker (LookML) — The gold standard if you are willing to use Looker as your primary BI tool. LookML is arguably the most mature code-based semantic layer on the market, but it is tightly coupled to the Looker ecosystem rather than being truly headless.
• Microsoft Power BI (Semantic Models) — The default and most powerful choice if you are a Microsoft-heavy shop (Azure, Fabric, Excel). Power BI's internal engine handles massive data volumes and centralized enterprise definitions seamlessly, though it works best when the downstream consumers stay within the Microsoft ecosystem.
• Warehouse-Native Layers (Snowflake Cortex / Databricks Unity Catalog) — Ideal if you want the semantic definitions to live directly inside your cloud data warehouse. Snowflake and Databricks increasingly offer native semantic views and governance features meant to serve both BI and internal AI tools without moving data out to a third-party layer.
• AtScale — A strong contender for complex enterprises that need a bridge between cloud data warehouses and legacy consumption tools like Excel and traditional OLAP cubes.

To narrow this down, tell me:

• What is your core data warehouse (Snowflake, BigQuery, Databricks, Postgres)?
• What BI tools are you trying to connect (Power BI, Tableau, Looker Studio, etc.)?
• Are you also planning to feed AI applications or LLMs with these metrics?

The "best" software for a semantic layer depends entirely on where your data stack already lives and which BI tools you plan to feed . The market splits into four distinct architectural approaches.

The top options are categorized by how they fit into an infrastructure stack:

• Cube — Best headless/standalone semantic layer.
  • Why it wins: It sits completely decoupled between your data warehouse and your BI tools/applications. It exposes metrics via REST, GraphQL, and SQL APIs, making it ideal if you use a mix of different BI tools (like Tableau, Excel, and custom apps) or need to feed clean context to AI agents and LLMs.
• dbt Semantic Layer (MetricFlow) — Best for dbt-centric analytics engineering teams.
  • Why it wins: If your team already manages data transformations inside dbt, you can define metrics right alongside your physical data models in version-controlled code. It ensures single-source-of-truth definitions that you can query through APIs into multiple BI endpoints.
• Looker (LookML) — Best for deep enterprise governance and Google Cloud shops.
  • Why it wins: LookML is the gold standard for centralized, code-based semantic modeling. If Looker is your primary interface (or you use Looker Modeler to downstream definitions to other tools), it locks down metric consistency effectively, though it anchors you tightly to the Looker ecosystem.
• Microsoft Power BI Semantic Models / Fabric — Best for Microsoft-centric enterprise ecosystems.
  • Why it wins: If your enterprise runs on Azure, Fabric, and Excel, building your semantic models natively here provides seamless performance, tight Office integration, and robust enterprise-grade access control via DAX.
• AtScale — Best for enterprise-scale data virtualization and multi-BI harmonization.
  • Why it wins: Known for its strong OLAP heritage, AtScale excels when you have massive data volumes across disparate sources and need high-performance live connectivity (via MDX/DAX) for heavy Excel and Power BI users without constantly shifting data around.
• Omni / Holistics — Best BI-native alternatives with robust semantic layers.
  • Why it wins: If you are open to changing or augmenting your BI front-end, tools like Omni and Holistics build the semantic layer natively into the exploration experience, avoiding the friction of stitching an external headless layer onto a separate visualization tool.

To narrow this down, tell me:

• What BI tools are you currently trying to connect (e.g., Tableau, Power BI, Excel, custom apps)?
• Where does your data warehouse live (Snowflake, Databricks, BigQuery, Postgres)?
• Are you already using dbt for transformations?

If you're building a standalone semantic layer that multiple BI tools will consume, my shortlist in 2026 would be:

My recommendation: Cube

If your requirement is literally “one semantic layer underneath several BI tools”, I'd start by evaluating cube.dev.

Cube's architecture is particularly suited to this because the semantic model isn't trapped inside a dashboard product. It can expose governed metrics through SQL, REST, GraphQL and MCP, while providing caching/pre-aggregations and access controls. Its open-source core is Apache 2.0.

That's important if, for example, you eventually want:

Snowflake → semantic layer → Power BI + Tableau + internal apps + AI agents

rather than:

Snowflake → Power BI's semantic model

When I'd choose dbt instead

If your team already has a mature dbt workflow, I'd seriously consider getdbt.com.

Metric definitions live alongside your dbt models and are version-controlled, lineage-aware, and exposed through APIs/integrations. MetricFlow compiles metric requests into SQL/query plans.

The architectural distinction I'd use is:

• dbt: transform and test the underlying data.
• Semantic layer: define what the business means by revenue, customer, active user, ARR, churn, etc., and serve those definitions to consumers.
• BI: visualize and explore those definitions.

For a dbt-heavy organization, dbt Semantic Layer may be enough. If you need a more independent serving layer with multiple interfaces, caching, embedded analytics, or broader BI/AI consumption, Cube becomes more compelling.

The decision I'd make

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.), BI tools (Power BI/Tableau/Looker/etc.), whether you use dbt, and whether AI/LLM access is part of the plan, I can give you a much more specific architecture recommendation and a Cube vs. dbt vs. AtScale comparison for your stack.

Snowflake → semantic layer → Power BI + Tableau + internal apps + AI agents

rather than:

Snowflake → Power BI's semantic model

When I'd choose dbt instead

If your team already has a mature dbt workflow, I'd seriously consider getdbt.com.

Metric definitions live alongside your dbt models and are version-controlled, lineage-aware, and exposed through APIs/integrations. MetricFlow compiles metric requests into SQL/query plans.

The architectural distinction I'd use is:

If your goal is a shared semantic layer underneath multiple BI tools, I’d shortlist Cube, dbt Semantic Layer, and AtScale. The right answer depends heavily on whether you want the semantic layer to be BI-neutral or effectively owned by one BI platform.

My recommendation

For a multi-BI environment, I’d start with Cube.

Cube is designed as a headless semantic layer: metrics, dimensions, relationships, security, and performance logic live centrally, while BI tools consume that layer through standard interfaces. It also supports embedded analytics and AI/agent consumption, which is increasingly useful if the semantic model needs to serve more than dashboards.

dbt's current Semantic Layer is particularly compelling if dbt is already the center of your data stack. You define metrics as code alongside your models and dbt's MetricFlow handles query generation and optimization. It also exposes those governed metrics to multiple downstream consumers.

AtScale is the one I'd investigate seriously if you're a large enterprise with Power BI + Excel + Tableau and need sophisticated dimensional/OLAP semantics, aggregate management, and enterprise governance. AtScale explicitly supports those BI tools from a common semantic model.

The architecture I'd aim for

If you have, say, Snowflake/BigQuery/Databricks → dbt → multiple BI tools, I'd structure it roughly as:

Warehouse → dbt transformation layer → semantic layer → BI tools

The important distinction is that dbt and the semantic layer shouldn't necessarily be doing the same job:

• dbt: transformations, data quality, lineage, dimensional modeling
• Semantic layer: business metrics, relationships, dimensions, governed joins, permissions, reusable calculations
• BI: visualization, dashboards, exploration

That separation becomes particularly valuable when you have a second consumer—e.g. Tableau and Power BI, or BI plus an AI agent.

How I'd choose

Pick dbt Semantic Layer if:

• You're already heavily invested in dbt.
• Most consumption is conventional analytics.
• You don't need elaborate serving/caching/OLAP capabilities.
• You want metric definitions version-controlled next to your models.

Pick Cube if:

• You genuinely need one semantic layer for several BI tools.
• You want the model available through SQL/API interfaces.
• You anticipate embedded analytics or AI agents.
• You want the semantic layer to remain independent of any particular BI vendor.

Pick AtScale if:

• You're a large enterprise.
• Power BI/Excel/Tableau are major consumers.
• Complex dimensional analytics and performance optimization matter.
• You want a dedicated enterprise semantic/OLAP platform.

One important caveat: vendor comparisons are understandably biased—Cube's own 2026 comparison, for example, favors Cube—so I wouldn't make the decision from feature matrices alone.

If you tell me your stack (warehouse + dbt or not + BI tools + approximate data/team size), I can narrow this to a 2–3 product recommendation and give you the architecture I'd use.

Choosing the "best" semantic layer software depends heavily on your existing data stack , whether you need it headless (API-first) or BI-native , and how you plan to feed data into AI agents or downstream tools.

The market is split into distinct categories. A breakdown of the top options by use case highlights their strengths:

• Cube (Best Headless / API-First Semantic Layer): Ideal if you use multiple BI tools, need embedded analytics, or want to ground AI assistants/agents in governed definitions. It exposes metrics via SQL, REST, GraphQL, and MCP (Model Context Protocol) without tying you to a single front-end.
• dbt Semantic Layer (Best for Analytics Engineering): Ideal if your team already manages transformations in dbt. Powered by MetricFlow, it lets you define metrics as version-controlled code right alongside your data models, though it is primarily a metric API rather than a full independent caching runtime.
• AtScale (Best for Enterprise Governance & Virtualization): A heavy-duty enterprise option. It shines if you need multidimensional (OLAP) cubes, active caching/aggregations to cut cloud warehouse costs, and live connections across complex or legacy estates feeding tools like Tableau and Excel.
• Looker / LookML (Best if Locked into Google Cloud/Looker Ecosystem): If Looker is your primary BI tool, LookML is a powerful built-in semantic layer. However, it is tightly coupled to Looker's environment, making it less ideal if you want a truly decoupled headless setup for other BI tools.
• Warehouse-Native Semantic Views (Snowflake Semantic Views / Databricks Metric Views): Best if your entire company lives inside a single cloud data platform. It provides blazing-fast native integration with that specific ecosystem's compute and native AI/search tools, but it locks your semantic definitions into that specific vendor.
• Holistics (Best BI-Native / Programmable Layer): Uses a hybrid approach with AML (Analytics Modeling Language) and AQL (Analytics Query Language) to offer high-expressiveness, multi-step metric composition that serves both dashboards and APIs natively.

To help narrow this down to the right choice for you, tell me:

• What BI tools are you trying to connect downstream (e.g., Tableau, Power BI, custom apps)?
• Where does your data warehouse or primary storage currently live (e.g., Snowflake, BigQuery, Databricks)?
• Are you planning to feed these metrics into AI tools or LLMs?

If your goal is one governed semantic layer that can feed multiple BI tools, I’d shortlist Cube, dbt Semantic Layer, and AtScale. The best choice depends heavily on whether you want the semantic layer to be inside your transformation workflow or to be a genuinely independent serving layer.

My recommendation

I'd pick Cube for a greenfield, BI-agnostic architecture. It is explicitly designed to sit between your warehouse and consuming applications, with the same governed model exposed through SQL, APIs, and modern AI interfaces. It also has an open-source core.

I'd pick dbt Semantic Layer if your organization is already deeply invested in dbt and most of the value you want is consistent metrics rather than a separate data-serving infrastructure. MetricFlow lets you define metrics alongside your dbt models and serve those definitions to BI, spreadsheets, embedded applications, and AI workflows.

I'd pick AtScale if you're a large enterprise with serious Power BI/Excel/Tableau requirements, particularly if OLAP-style analysis, performance optimization, and centralized governance are major concerns. AtScale is designed specifically as a universal layer across BI and AI applications.

One important architectural distinction

I'd avoid choosing based simply on "which product has a semantic layer." There are really two architectures:

dbt-centric:

Warehouse → dbt models → dbt Semantic Layer → BI

versus

independent serving layer:

Warehouse → dbt models → Cube/AtScale → BI tools + APIs + AI + applications

The second is more compelling if you have multiple BI tools and want to prevent each tool from developing its own definition of revenue, customers, retention, bookings, etc. Cube, for example, is explicitly positioned as a layer that serves the same definitions to BI, embedded analytics, and AI.

What I'd evaluate before buying

Don't primarily compare dashboard features. I'd run a proof-of-concept around:

1. Metric definitions — Can you express your 20–30 hardest metrics cleanly?
2. Joins — Can it prevent fanouts and ambiguous join paths?
3. Time intelligence — YoY, rolling periods, fiscal calendars, cohorts.
4. BI interoperability — Can the same metric be consumed from Tableau, Power BI, Looker, etc.?
5. Security — Row/column-level security and user-context propagation.
6. Performance — Caching, pre-aggregations, concurrency.
7. Version control — Can semantic definitions go through Git/CI/CD/code review?
8. AI access — Can an agent query the semantic model rather than reconstructing SQL from raw tables?
9. Lineage — Can you trace Revenue → metric → model → warehouse tables?
10. Escape hatches — Can analysts still do legitimate ad-hoc analysis without destroying governance?

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.), BI tools (Power BI/Tableau/Looker/Sigma/etc.), whether you're already using dbt, and roughly how many data/BI users you have, I can give you a much more opinionated recommendation—including Cube vs dbt vs AtScale vs staying native to your warehouse for your particular architecture.

The "best" software for a semantic layer depends entirely on your existing data stack, your choice of BI tools, and whether you need your metrics to feed external APIs or AI agents.

The market divides the options into three clear architectural patterns:

1. Standalone & Headless Semantic Layers (Best for Multi-BI & Flexibility)

If you use multiple BI tools (e.g., Tableau, Power BI, and custom apps) and want a single source of truth that isn't locked to one vendor, choose a headless/standalone layer.

• Cube — The leading API-first, headless semantic layer. Excellent for developer-heavy teams, embedded analytics, and feeding clean metrics out via REST/GraphQL/SQL APIs.
• dbt Semantic Layer (powered by MetricFlow) — The gold standard if your data team already handles data transformations inside dbt . It lets you define metrics as code right alongside your data models and version-controls them cleanly.
• AtScale — Built for heavy-duty enterprise governance, virtualization, and legacy/live connectivity back into Microsoft Excel and Power BI via MDX/DAX.

2. Warehouse-Native Layers (Best for Single-Platform Simplicity)

If your entire data workflow lives inside a single cloud data platform and you want zero extra orchestration tools.

• Snowflake Semantic Views (with Cortex Analyst) — Ideal if you are entirely all-in on Snowflake and want platform-native metric definitions optimized for Snowflake's internal AI tools.
• Databricks Metric Views (via Unity Catalog) — Best for Databricks-heavy environments looking for native governance and metric handling directly inside Unity Catalog.

3. BI-Native & Unified Platforms (Best for All-in-One Self-Serve)

If you are open to letting your semantic layer and your BI exploration interface live in the same system.

• Omni — Rapidly emerging as a top-tier choice that combines a powerful, warehouse-direct semantic layer with intuitive self-serve BI and bidirectional dbt syncing.
• Looker (LookML) — The classic enterprise incumbent. Highly centralized and powerful if you are a Google Cloud shop standardized strictly on Looker as your BI consumer.
• Power BI Semantic Models — The default and most cost-effective choice if your enterprise runs purely on the Microsoft Fabric / Azure ecosystem.

To help narrow down the ideal choice, tell me:

• What BI tools are you currently using (or planning to use)?
• Is your data transformation pipeline already built on dbt or another orchestrator?
• Do you also need this semantic layer to feed AI agents or embedded apps , or strictly internal dashboards?