Cut AI spend without guessing
Engineering leads and founders watching spend
Token costs grow before anyone knows which workflow, agent or customer caused it. Provider invoices arrive too late and too aggregated to act on.
Cost per run, not per month
Spend is recorded on each model call with input, output and cache tokens, so you can find loops, oversized prompts and model choices that cost more than they return.
Measure spend per call, group it the way you run the business, fix the biggest sources, and watch quality while you do.
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
What you get
- 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.