Your LLM is working too hard, and spending too much
Stop running out of tokens mid-month. CData Connect AI stretches your AI budget, and use cases, further.
fewer tokens per query
more accurate responses
faster workflows
The cost curve
LLM spend is rising, and it isn't slowing down
Token overhead compounds at every layer: tool definitions, discovery chains, and multi-source round-trips. As adoption spreads across your teams, your bill grows with it—fast.
Projected from Goldman Sachs' 24× agent-token growth, applied to one organization
A single question now spans a CRM, a warehouse, and an ITSM tool. Each source it touches adds another schema, another round-trip, another block of context.
A default Salesforce Account tool exposes 70+ fields. Most queries need a handful. The unused fields still burn tokens on every call.
More teams, more agents, more prompts—each running the same expensive discovery chain. Overhead multiplies with adoption instead of amortizing.
The right architecture enables users to do more, while keeping your AI budget in check
Same query, two paths. One dumps raw multi-source data into the context window. The other federates, filters, and pre-analyzes before Claude ever sees it.
The Connect AI capabilities that drive token efficiency
Each capability removes a specific category of overhead before the request reaches Claude. Configure once, then reuse across every workflow.
Calculate your before and after context-related token spend
Based on an average 93% reduction in context processing on a live multi-source query. Adjust the inputs below to fit your organization.
Estimate uses each model provider's published pricing per million input/output tokens at the time of development of this estimator. Actual savings depend on workflow shape, model, and which Connect AI features you deploy.
Not just managing spend, impacting it
Most tools tell you what you spent, after you spent it. Connect AI removes the tokens before the request ever reaches the model.
Observe & manage
The common approach: visibility after the fact.
- Usage dashboards report what was already consumed
- Budget alerts fire once the spend has happened
- Rate limits cap volume, not the waste inside each call
- The underlying query still ships full schemas and raw rows
Actively reduce
The Connect AI approach: spend drops at the source.
- Scoped tools expose only the fields a workflow reads
- Derived Views pre-join sources so orchestration disappears
- Caching serves recurring queries without the live round-trip
- The model reasons over less, so accuracy goes up as cost goes down
Your enterprise data, finally AI-ready
One governed MCP endpoint. Hundreds of connectors. Token efficiency configured once and reused across every Claude workflow.