AI Workload and Model Optimization
Better workload decisions. Better use of resources.
Evaluate how AI workloads use models, context, tools, and infrastructure against the quality, performance, security, and cost the business needs.
Start with one representative workload
We help establish what the workload does, who uses it, what a useful result looks like, and which constraints a change must preserve.
- Quality and acceptance criteria
- Latency, reliability, and data requirements
- A usage and cost baseline
Compare changes worth testing
The most capable model is not automatically the best fit for every task. We help structure a comparison of model choices, routing, repeated calls, context, and appropriate answer reuse.
- Model and task fit
- Context, token consumption, and tool use
- Routing, retry, and reuse candidates
Make the economics complete
A lower model bill is one part of the decision. Evaluation, engineering, infrastructure, and operating effort also affect whether a change is worthwhile.
- Cost per useful result
- Implementation and ongoing effort
- Pilot criteria and a measured rollout decision
A focused engagement
AI Workload and Token Optimization Assessment
The scope and resources are shaped around your priority. Use this as a starting point for that conversation.
Who it is for
AI, application, architecture, and FinOps teams with defined workloads and meaningful consumption.
Triggering events
Production expansion; high context or output costs; repeated agent steps; latency issues; proposed model migration; high-volume repeated questions.
Scope
Named workloads and representative evaluation cases. Review models, prompts, context and RAG, conversation history, output controls, tool calls, retries, routing, fallback, batching, and cache eligibility.
Customer inputs
Sanitized prompts, outputs, and traces; per-workload cost and latency records; quality criteria; test examples; data permissions; source-freshness needs; production constraints.
Deliverables
Baseline and coverage limits; ranked candidate changes; model and routing evaluation matrix; token and tool-call findings; cache eligibility and exclusion rules; validation results when testing is in scope, otherwise a test plan; net-benefit assumptions and rollout decision criteria.
Expected next step
Authorize controlled testing with qualified engineering resources. Adopt a change only after the customer’s quality, security, and operational acceptance.
Boundaries
No production changes, model substitution, or sensitive-data transfer by default. No promised token reduction. High-risk, personalized, transactional, changing, or permission-sensitive answers may be excluded from caching.
Model, prompt, routing, or caching changes require customer acceptance for quality, security, and operations before production use. Savings are evaluated, not guaranteed.
A practical next step
Start with your AI priority.
Tell us what you are trying to accomplish and where the decision needs more clarity.