You know what AI costs.
Not what it's wasting.
InferOps shows you where the waste is.
InferOps finds the inference waste that's safe to cut, and confirms the saving from your own production data. Direct recommendations carry zero quality risk. Anything with a trade-off comes with the checks to run first.
Analysing production AI spend for early access teams
cost intelligence
patterns found
patterns found
saving confirmed ✓
collecting
Illustrative example
For teams running production AI features where inference spend is growing faster than they can explain it.
~23%
of revenue goes to inference at scaling AI companies, on average
3-5x
output tokens cost more than input tokens. Most teams only optimise one side. For agents, accumulated input context is where most of the bill lives.
< 1 in 3
teams can identify which AI features are generating return on their API costs
Source: ICONIQ State of AI, January 2026.
The product
Two lines in. Waste ranked by saving.
Confirmed from your own data.
Add two lines to your startup code. InferOps starts watching every AI feature in your stack simultaneously. Costs ranked by monthly spend from day one. No configuration.
Direct recommendations carry zero quality risk.
01 · Detect
Two lines. Deploy. Done.
Two lines at startup. InferOps starts seeing your production AI traffic immediately. Costs ranked by monthly spend per feature. No configuration required.
02 · Verify
Waste you did not know was there.
InferOps watches every feature simultaneously and surfaces what is worth investigating. Findings ranked by monthly saving potential. From day one.
03 · Confirm
Actual savings, not estimates.
When you implement a change, InferOps detects it automatically and measures the real saving from your production data over 7 days.
Detect
Waste found automatically
Verify
Zero risk or check first
Confirm
Saving measured from production
No other tool closes this loop automatically.
cost intelligence - last 30 days
investigating
investigating
saving confirmed ✓
All findings from production data
support-bot · prompt caching
Bit-identical responses. Zero quality consideration.
7-day measurement
Detected automatically. InferOps observed the change in your event stream and measured the saving over 7 days. No manual comparison. No invoice guesswork.
Illustrative example
More coming: prompt optimisation tools, shadow model testing, and API cost outcome reporting.
From our benchmark
We measured what's actually safe to cut.
We scored 1,280 responses across four frontier models and four cost levers. One finding stood out: prompt compression looks like free savings and collapses RAG quality — Tier-1 pass rate drops from 80% to 4%. Knowing which levers are safe for which workloads is the whole game.
Read the full benchmark →Early access
Early access now open.
We are onboarding teams with active Anthropic or OpenAI spend.
No payment required. No commitment. We will reach out when your spot opens.