I’m excited to share that AtScale has once again been recognized as both a Leader and Fast Mover in the 2025 GigaOm Radar Report for Semantic Layers and Metric Stores. This marks the third consecutive year AtScale has been placed in the Leader quadrant, highlighting our consistent innovation and leadership in the space.
This year’s report is especially significant because it validates how semantic layers are evolving to meet the needs of enterprise AI, agentic systems, and next-generation analytics.
Independent Validation of the Semantic Layer
One of the most critical takeaways from this year’s Radar is that semantic layers are no longer optional. As GigaOm notes, semantic layers help organizations achieve their most fundamental business objectives by making sure analytics results are thorough, meaningful, deterministic, and consistent.
Semantic layers abstract the complexity of underlying systems and provide a high-level, governed view of data, allowing management to trust the numbers they see in dashboards, reports, and AI outputs. In GigaOm’s words, the modern semantic layer “represents the best of both worlds” by combining trusted dimensional modeling with modern query engines, automation, and composability.
This context matters. With so many vendors rushing to “own” the semantic layer, independent validation cuts through the noise. Our placement as both a Leader and Fast Mover underscores that AtScale is not only delivering value today but also pushing the category forward.
What GigaOm Highlighted About AtScale
The 2025 report called out several areas where AtScale stands out:
• GenAI Enablement: AtScale serves as a semantic foundation for GenAI workloads, with support for the Model Context Protocol (MCP), the AI-Link Python SDK, and compatibility with leading LLMs. GigaOm reinforced why this matters: “Enriching technical data with meaningful context is key to giving GenAI models the semantic meaning of data that’s needed for them to perform effectively.”
• Composable Modeling: Recognition of our Semantic Modeling Language (SML), which supports modularization and composability of semantic definitions. With Git-native workflows, version history, and CI/CD integration, SML brings modern software engineering practices into data modeling.
• Performance and Cost Efficiency: Validation of AtScale’s autonomous query optimization, which delivers sub-second performance and reduces compute costs without data duplication.
• Open Standards and Interoperability: Support for SQL, MDX, DAX, Python, and R; plus integration across BI tools, data platforms, and AI agents, showcasing our commitment to openness and avoiding vendor lock-in.
• Ease of Use: GigaOm gave AtScale its highest rating for ease of use, citing the drag-and-drop interface as well as code-first approach to modeling, the pre-built model templates, as well as the GenAI assistance for one-click modeling.
GigaOm positioned AtScale in the Innovation/Platform Play quadrant, closest to the bullseye, which reflects both technical depth and forward momentum in the category.
Market Context: From Emerging to Established
This year’s Radar is also a signal of market maturity. GigaOm emphasized that the evaluation has evolved from a Sonar report, focused on cutting-edge, emerging technologies, into a Radar report, which assesses established categories with proven impact.
As the report explains, this shift reflects both the rapid adoption of semantic layer platforms and the investments incumbents have made to enhance their offerings. Semantic layers are no longer experimental. They are a critical and recognized layer of the enterprise data stack.
They also highlighted the importance of having a deep query language support, as it was an opportunity for most vendors to support DAX and MDX more deeply. AtScale understood this from the start, and as stated in the report, recently announced DAX level 1600 support.
Why This Matters for Enterprises
Without a semantic layer, definitions drift across BI tools, data products, and AI systems. Finance’s “Revenue” doesn’t match Marketing’s “Revenue,” and neither aligns with what an LLM returns in Slack. The result is lost trust, wasted time, and higher risk.
Composable semantics solve this problem at the architectural level. They ensure that business metrics and definitions remain consistent, explainable, and governed, no matter where they’re consumed.
GigaOm underscored this point, noting that semantic layers are becoming critical for enterprises adopting and integrating generative AI. By mapping technical data to business meaning, semantic layers provide the enriched context GenAI models need to produce refined, explainable, and trustworthy output.
The Road Ahead
As enterprises embrace GenAI and agentic AI, the need for governed, explainable, and interoperable semantics will only grow. The semantic layer is what bridges raw data with AI-ready intelligence.
AtScale’s mission is to solve the complex problem: scaling trusted, governed analytics without breaking what already works. This year’s GigaOm Radar validates that approach and highlights why composability, openness, and AI readiness are essential for the future of data and analytics.
As the report concluded, semantic layers help businesses future-proof their data estates and position them to gain and maintain the competitive edge that can come only from properly harnessing their data.
I encourage you to explore the full report to see how AtScale compares with other solutions and why we continue to lead the way.
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