Applied research for AI agents and servers
Lower API costs.
More users on your servers.
Cut the cost of completed agent tasks with your current model.
Serve more users on your existing hardware and bandwidth.
Test one workload. Compare cost, quality and speed.
Find your baseline
What are you paying
per completed task?
Divide API spend by accepted tasks for the same period.
Include failed calls and retries. A task may use several model calls.
Choose a test
Find work
you can stop repeating.
Choose a symptom for a test plan, metrics and required data.
The first test
Cut extra agent work on the same model
Test subagents on and off, reasoning effort per role and the speed tier. Run the same tasks and model; count retries and failed attempts in the cost.
- Compare
- Cost per accepted task, tokens per task, quality, retries and time.
- Bring
- Agent sessions with model, effort and speed settings; the task list and acceptance criteria.
Research: 74 published materials on caching, API costs and model execution,
and about 27,000 messages from developers who run coding agents daily.
Use more of your infrastructure
Increase capacity.
Keep your infrastructure.
Test what your CPU, RAM, disks and connection can deliver.
Do less work
per request.
Trace repeated calculations, waits and oversized responses. Test fewer resources per successful action.
Find my server's bottleneck ↗- CPU
- Profile costly operations, locks and queues. Test concurrency under load.
- Memory & disk
- Check cache use, allocations and queries. Test fewer reads and calculations.
- Bandwidth
- Test compression, HTTP caching and response size. Compare bytes per action and CPU use.
With bandwidth fixed, test fewer bytes per result.
Start with bandwidth →Compare on your workload
Test capacity
before traffic spikes.
Replay user actions at a fixed rate. Increase concurrency; track latency, errors and resource limits.
- CPU, RAM, disk & network
- The same resources and limits
- User actions
- The same scenarios and frequency
- Data & cache warm-up
- Comparable conditions
- The target
- More successful actions within the latency target
Measure completed actions.
Track active users, successful operations per second, errors and p95/p99 latency.
Find growing queues and the limiting resource. Change one thing; repeat the test.
Plan my load test ↗Your record: baseline, results, tested settings and rollback.
Caching & compression: Microsoft Learn ↗One workload
Define success.
Measure the outcome.
Fix the model and quality criteria, or server resources and workload. Define success before comparing results.
What you get
- A cost and bottleneck map
Costly tokens, agent steps or server operations, ranked for testing.
- A measured comparison
Accepted results, cost, latency and errors under matched conditions.
- A repeatable implementation
Changes, measurements and rollback steps.