As companies move away from traditional BI tools, they lose the shared semantic layer that ensures everyone calculates KPIs the same way — leading to conflicting numbers across teams
A dedicated semantic modeling tool where data teams define business metrics, dimensions, and relationships once, then any AI tool or dashboard builder can query against the canonical definitions — ensuring consistency
Subscription SaaS, $1K-5K/mo based on data sources and team size
This is a well-documented, expensive problem. Conflicting KPIs across teams is cited as a top frustration by data leaders. Companies spend thousands of hours reconciling 'whose numbers are right.' The pain intensifies as organizations adopt multiple BI/AI tools that each calculate metrics differently. The Reddit thread and broader discourse confirm this is a deeply felt pain point.
TAM for semantic layer tools is estimated at $3-5B when you consider it replaces portions of BI spend ($25B+ market). The serviceable market for mid-market companies (500-5000 employees) transitioning off legacy BI is substantial — likely 10,000+ companies globally. At $1K-5K/mo, even capturing 500 customers = $6M-30M ARR. Not a trillion-dollar market, but a very healthy SaaS opportunity.
Data teams already pay $5K-50K+/mo for BI tools that include semantic layers (Looker, Power BI Premium). The willingness to pay for a dedicated semantic layer is proven by Cube Cloud and AtScale's enterprise contracts. The $1K-5K/mo price point is reasonable for mid-market data teams with 5-20 person budgets. However, the open-source alternatives (Cube, dbt Core) create downward pricing pressure, and many teams may expect this functionality to be 'included' in their existing stack.
This is where brutal honesty matters. A semantic layer is NOT a simple CRUD app. You need: (1) connectors to multiple data warehouses (Snowflake, BigQuery, Databricks, Redshift), (2) a query engine that translates semantic definitions into optimized SQL across different dialects, (3) a caching/performance layer, (4) an AI/LLM component for assisted modeling, (5) APIs for downstream consumers. A solo dev cannot build a production-quality MVP in 4-8 weeks. Minimum 3-4 months for a narrow-scope MVP targeting one warehouse with basic AI assistance. The 'AI-assisted' differentiator adds significant complexity — you need to train/prompt models on schema understanding, metric definition patterns, and business context.
The gap is real and specific: NO existing tool combines (a) AI-assisted metric discovery and definition, (b) support for blending warehouse data with team-maintained spreadsheets, and (c) an API-first design for LLM/AI tool consumption. dbt's semantic layer is code-only and locked to dbt Cloud. Cube is developer-heavy with no AI. AtScale is enterprise-only. Looker is ecosystem-locked. The AI-native semantic layer is genuinely underserved. However, dbt Labs and Cube are both moving in this direction, so the window is 12-18 months.
Excellent subscription fit. Semantic layers become infrastructure — once teams define their metrics and downstream tools consume them, switching costs are very high. Usage grows naturally as more metrics, users, and consuming applications are added. Data source and seat-based pricing scales naturally with customer growth. This is the kind of tool that becomes deeply embedded in workflows.
- +Genuine market gap: AI-assisted semantic modeling is unaddressed by incumbents
- +Strong timing: AI/LLM adoption is creating urgent new demand for structured metric APIs
- +High switching costs and natural expansion revenue once adopted
- +The 'spreadsheet blending' angle is a unique differentiator no competitor offers well
- +Mid-market pricing sweet spot: too expensive for AtScale, too complex for Cube self-hosted
- !dbt Labs is the 800-lb gorilla and will likely add AI-assisted features to their semantic layer within 12-18 months
- !Technical complexity is high — this is infrastructure software, not an app. Warehouse connectors, SQL dialect translation, and caching are non-trivial
- !Long sales cycles: data infrastructure purchases at mid-market/enterprise require security reviews, procurement, and stakeholder buy-in (3-6 month cycles)
- !The market may consolidate before you reach scale — Cube, dbt, or a cloud provider could absorb this niche
- !Proving 'AI-assisted' metric definition actually works reliably is a hard ML/LLM problem — hallucinated metric definitions could erode trust
Built-in semantic layer for dbt Cloud that lets data teams define metrics as code using MetricFlow
Open-source semantic layer and metrics platform that sits between data sources and any consuming application. Provides a universal API
Enterprise semantic layer platform that creates a virtual data model on top of cloud data warehouses, presenting data as if it were an OLAP cube. Strong focus on connecting legacy BI tools
Google Cloud's BI platform where LookML serves as a semantic modeling language. Defines dimensions, measures, and relationships in a code-based layer that powers all downstream Looker explores and dashboards.
Open-source BI tool built on top of dbt that uses dbt's model definitions as its semantic layer. Targets data teams who want self-serve analytics with dbt as the foundation.
Narrow ruthlessly: support ONE warehouse (Snowflake — largest mid-market adoption), build a visual metric builder with AI suggestions (LLM reads your schema and proposes metric definitions, dimensions, and relationships), and expose metrics via a simple REST/GraphQL API. Skip caching, skip spreadsheet blending, skip multi-warehouse support for V1. The core bet to validate is: 'Can AI meaningfully accelerate semantic model creation?' Ship a tool where a data engineer pastes a warehouse connection string and gets suggested metrics in under 5 minutes.
Free tier: 1 data source, 10 metrics, AI-assisted suggestions → Paid ($999/mo): unlimited metrics, team collaboration, API access, SSO → Enterprise ($3K-5K/mo): multiple warehouses, governance, audit logs, spreadsheet blending, custom LLM fine-tuning. Offer a 'migration assistant' that imports existing LookML or dbt metric definitions to reduce adoption friction.
4-6 months to MVP with paying design partners, 8-12 months to meaningful recurring revenue ($10K+ MRR). Data infrastructure products have longer sales cycles than consumer tools. Plan for 6 months of free/design-partner usage before conversion. The conditional GO verdict hinges on: (1) having a co-founder or contractor with deep data warehouse experience, and (2) securing 3-5 design partner companies before writing code.
- “proper semantic modeling is where the money's gonna be”
- “semantic layers that blend Enterprise Data with team maintained spreadsheets are going to become the main value proposition”
- “every company works differently, they have different KPIs”