AI Cost Optimization and Token Spend Intelligence
Engineering leaders, finance-aware product teams, founders
Token and model costs can grow before anyone knows which workflow, agent, user segment, model choice, or tool loop caused the increase. Aggregate billing reports arrive too late to fix the source.
Cost control tied to behavior
AnoSys attributes cost to the operational context that created it: model, agent, tool, workflow, team, user, process, eval result, and business outcome.
Measure spend at the point of execution, then decide what to optimize without degrading quality.
Measure
Capture token usage, model calls, latency, retries, cache use, and tool execution.
Attribute
Tie spend to teams, agents, workflows, users, customer segments, and business KPIs.
Detect
Find cost spikes, retry loops, inefficient prompts, and model routes that are not earning their cost.
Optimize
Prioritize fixes that reduce spend without hurting quality or customer outcomes.
What supports it
Why teams buy it
- Prevent surprise bills by detecting spend anomalies when they start.
- Show which features, customers, and workflows create useful AI value.
- Give engineering and finance a shared source of truth for AI unit economics.
Can AnoSys attribute cost beyond a model name?
Yes. AnoSys links cost to agents, workflows, tools, users, teams, evals, and business outcomes.
Can cost alerts account for quality?
Yes. AnoSys can combine cost signals with eval scores, latency, user behavior, and business KPIs so teams do not optimize spend blindly.