How a Semantic Engine Reduces AI Query Costs Without Sacrificing Performance

Real-time AI analytics feels like the future: instant insights, natural conversation, decisions without delay. But behind the scenes, that future comes with a big catch: every AI query costs money, and those costs compound fast.

Many of our customers see 10x spikes in query volume once they embed analytics into AI agents or copilots. Without intelligent optimization, performance degrades, latency becomes unpredictable, and cloud bills balloon.

In 2025, these pressures are only getting stronger.

• As enterprises scale AI, inference compute costs are becoming a primary line item.
• AI agents (not just copilots) are increasingly expected to act autonomously — meaning more back-and-forth with your data layer.
• And across LLM deployments, engineers are aggressively chasing 70–80% cost reductions via better routing, caching, and query planning.
  

AtScale’s answer to this rising tide is the semantic engine, a smarter way to route analytics workloads so you get both performance and cost-efficiency.

Why Traditional BI Optimization Breaks Under AI Load

BI workloads are stable and predictable: dashboards, scheduled reports, occasional ad hoc queries. Data warehouses were optimized for that world.

But AI workloads are volatile. A single prompt might unleash several semantic queries, each repeating across users and systems. This leads to:

• Concurrency overload: Many requests at once, sometimes bursts beyond what most warehouses can absorb.
• Explosive cost scaling: Each query triggers compute; repeated patterns multiply costs.
• Inconsistent latency: One moment it’s instant, the next, it’s sluggish, and users lose trust.
  

Static caching or manually built aggregates aren’t agile enough to deal with this kind of unpredictable load.

Semantic Engine: Built for AI and BI

The semantic engine is AtScale’s intelligent query optimization framework built to handle high-volume, high-variability workloads across both AI applications and BI tools.

Rather than forcing a trade-off between cached speed and live freshness, a semantic engine evaluates each request dynamically and routes it to the optimal path.

In practice, that means one system, AtScale’s semantic layer, decides whether to:

• Serve a query instantly from a pre-computed aggregate, or
• Push the query live to the data warehouse for real-time freshness.

Every decision balances performance, governance, and cost.

How It Works

1. Autonomous Aggregate Engine
   Learns query behavior across all connected BI and AI tools. Automatically builds and maintains aggregates for frequently queried metrics and dimensions. No manual tuning.
   
2. Aggregate-Aware Query Planner
   Chooses the most efficient execution path at runtime, aggregate or live. Users always get the same governed result, no matter how it’s served.
   
3. Smart, Governed Caching
   Stores frequently accessed query results for sub-second recall while enforcing row-level security, column permissions, and all governance policies.
   
4. Live Query Pushdown
   When freshness matters, AtScale generates optimized, warehouse-native SQL and executes it directly on the platform, leveraging Snowflake caching, Databricks Photon, BigQuery optimizations, and more.

Together, these layers form a self-optimizing system that continuously adapts to user behavior, ideal for today’s dynamic, AI-driven workloads.

Customer Wins: Sustainable AI in the Real World

These results aren’t theoretical; they’re happening at scale across industries.

A leading global retailer eliminated performance bottlenecks that once slowed analytics to a crawl. After implementing AtScale’s semantic engine, more than 80% of queries now return in under one second, even as AI copilots and dashboards hit the same warehouse concurrently. Business users no longer wait for reports; they ask a question and get a governed, consistent answer instantly, whether through Power BI, Excel, or a conversational AI interface.

Vodafone Portugal modernized its legacy OLAP systems while migrating to Google BigQuery. By deploying AtScale’s semantic layer, the data team cut analytics runtimes from three hours to forty-five minutes, streamlined operations, and established consistent metric definitions across finance, marketing, and customer analytics. That semantic consistency made it possible to extend governed data access to new AI and NLQ experiences without increasing risk.

In both cases, a semantic engine made AI adoption sustainable, not just faster.

Why It Matters Now

In 2025, this problem is getting louder, not quieter.

• Inference compute costs are rising across LLM and GenAI deployments, forcing teams to rethink architecture, not just scale budgets.
• Agentic AI systems, not just copilots, are now executing workflows autonomously, which means more real-time interaction with enterprise data.
• Cost optimization is a board-level KPI; CIOs are being asked to prove AI ROI through infrastructure efficiency.
• Governance is non-negotiable. AI systems must operate under the same metric definitions, access controls, and audit trails as BI; otherwise, semantic drift creeps in.

A semantic engine sits at the intersection of these challenges. It enables organizations to scale AI analytics without exceeding performance budgets or compromising data trust.

The Path Forward

Enterprises deploying AI today face a choice: scale through brute force (more compute, more cost) or scale through intelligence (better optimization and governance).

AtScale’s semantic engine is designed for the latter, a sustainable foundation for AI analytics that keeps costs predictable, responses fast, and semantics consistent.

Next Steps

• See it in action: Try AtScale’s interactive demo and watch the semantic engine in real time.
• Download the 2025 GigaOm Semantic Layer Radar Report: See why AtScale was named a Leader and Fast Mover for performance and AI readiness.
• Request a demo: Learn how your own workloads can scale smarter, not just faster.