How the Right Data Layer Can Reduce AI Costs by 178x
AI gateways can route prompts to cheaper models, but can you trust cheaper models to provide accuracy and safety? With the right data layer in place, the same correct answers were delivered by cheaper models, at 178x savings over the most expensive.
What we found
When the data layer controls for accuracy and safety, model choice becomes mostly a cost decision
We benchmarked 22 AI models, economy to frontier, across 1,034 runs on live CRM, warehouse, and ITSM data.
- On read tasks: With Connect AI, models that were correct 100% of the time varied in cost from 178× to 278×. Without a data layer, the best model was only correct 40% of the time.
- On write tasks: With Connect AI, 20 of 22 models wrote exactly the correct 100% of the time. Left to the model alone, the worst run wrote thousands of incorrect values.
Same answer, 178× the price
Once CData Connect AI carried the business logic, every tier model was uniformly accurate. The only thing that changed was cost.
Based on the optimized configuration for the prompt that required composition across data sources. Every model above returned the identical set of 44 accounts. Cost per correct answer spans 178× from cheapest to most expensive. On the ranking task, nine models scored identically and still spanned 279×.
How models fail without Connect AI
With Connect AI, context provides the business logic and guardrails for models to act accurately and safely. When models access raw data, it leads to unreliable AI behavior and outright failures.
Without CData Connect AI, models returned complete, well-formed lists of accounts that were simply incorrect. A wrong answer that looks exactly like a right answer is worse than an obvious error, because nothing downstream flags it.
Business logic left to guesswork
Raw data access exposes your schema, not your rules. The model has to infer which telemetry maps to which health signal, what threshold counts as at-risk, and how three separate systems relate. Encode that once in Connect AI, and every model that calls it inherits the same correct definition.
Unbounded writes
Given a general-purpose write operation, models inserted far beyond the authorized set. The boundary has to live somewhere the model can't reinterpret, or it decides scope for itself, differently from one run to the next.
Instability run to run
The same model on the same task might write the 21 correct records on one run and hundreds of wrong ones on the next. Without the limit enforced in the tool, behavior isn't repeatable.
Methodology
1,034 runs 22 models, open to frontier, across eight model developers, three tasks, two to three tool conditions each.
Live federated data Customer accounts in a CRM, usage telemetry in a cloud warehouse, and support incidents in an ITSM platform, connected through CData Connect AI. Nothing was pre-staged.
Same harness for every model One custom client-side tool loop for every model, so no vendor gets a home-field advantage. Tokens are counted the way each provider bills them, at list prices.
Model-independent scoring Reads are scored by set F1 against a golden key computed offline. Writes are graded on the target table's actual end state, read through a separate endpoint.
Get the research
Executive Summary (9 pages)
Key findings, the accuracy-equalizes/cost-varies result, and what it means for how you select and deploy models.
Full Research Report (21 pages)
Complete methodology, per-task results across all 22 models, failure-pattern analysis, and the write-safety findings in full.
Testing Harness (GitHub)
Run the benchmark against your own systems and data. Includes the verbatim task prompts, tool schemas for every condition, scoring code, and the golden-answer generator.