Inference cost diagnosis
In plain wordsYour AI bill keeps growing. We show you which models, agents and retries are driving it, and what to change first.
Croutons evaluates where inference spend concentrates across models, agents, retries and context size, and recommends routing and workload changes.
The problem
Inference spend rises faster than useful output because expensive models, oversized context and retry loops are applied without workload-level diagnosis.
Signs you have it
- Token and API spend growing faster than request volume
- Frontier models used for routine classification or extraction
- Unexplained cost spikes after agent or retrieval changes
- No per-workflow cost attribution
Who it is for
Engineering and FinOps teams running multi-model AI in production.
What we look at
- Task class versus model tier
- Fan-out and retry amplification
- Context window size and retrieval payload cost
- Low-value calls on high-cost models
What to send us
- Sample traces or logs with model, tokens and latency
- Workflow or agent descriptions
- Current routing or gateway rules if available
- Cost reports or invoice exports for the period under review
How the diagnosis works
Map completed tasks to model selection, token cost and amplification sources. Cluster waste by workflow class. Propose the lowest-cost model tier that meets stated quality thresholds for each class.
What you get
- Inference cost map by workflow and model tier
- Ranked waste drivers
- Recommended routing and context controls
- Pilot measurement plan for cost deltas
Expected outcome
A clearer picture of where spend concentrates and a prioritized set of controls teams can implement, without claiming fixed percentage savings.
Limits, stated up front
- Savings depend on your workload mix and quality thresholds
- Does not replace FinOps tooling or billing systems
- First diagnosis uses samples, not continuous production metering unless later engaged
Related use cases
Cost–latency–quality optimization
Cheaper, faster and better pull against each other. We help you decide the trade-off per job instead of guessing.
Model routing strategy
Using your most expensive model for everything is like sending every letter by courier. We help you write the rules for which model does which job.
Prompt and context efficiency
Long prompts cost money on every call. We find the words your AI pays for but does not need.
Inference cost diagnosis: common questions
Why is our AI bill growing faster than our usage?
Usually because one user request turns into many model calls. Retries, agents that call other agents, oversized prompts and expensive models doing simple jobs all multiply cost without adding output. The diagnosis traces those multipliers back to specific workflows.
Do you need access to our billing or cloud accounts?
No. An invoice export, a cost report or a sample of logs with model names and token counts is enough for the first diagnosis. You choose what to share.
Will you promise a specific savings percentage?
No. Savings depend on your workload mix and the quality bar you set. You get a ranked list of waste drivers and a pilot plan that measures the real difference before you commit.
Book the ai layer diagnosis call.
Find where your AI spends money and where it breaks. 30 minutes, free.