Detect the workload change
Class1 identifies changes to models, token limits, context, retries, fallbacks, tools and expected usage.
AI cost estimation
Estimate AI operating cost before the code reaches production.
Class1 reads a pull request, models the workload change and returns P50, P90 and P95 monthly cost deltas while the architecture is still negotiable.
The problem
Most teams discover AI cost after the design decisions are already expensive to reverse.
Model choice is only one cost driver. Context size, output limits, retry policy, fallback paths, tool calls and expected adoption can multiply recurring spend.
Traditional cloud and AI dashboards begin with production telemetry. They explain what was spent, but not whether a proposed pull request should be approved.
Class1 moves that decision into code review, before the change becomes infrastructure, habit and invoice.
How it works
From code diff to a risk-adjusted monthly forecast.
Build the estimate basis
The engine combines quantity assumptions, model rates, uncertainty, contingency and escalation into a documented basis of estimate.
Simulate uncertainty
Paired Monte Carlo draws produce P50, P90 and P95 cost deltas instead of one misleading point estimate.
Approve, revise or block
The pull request receives a report with cost drivers, risk bands and an optional P90 budget gate in CI.
Why percentiles matter
An average is not a budget decision.
P50 is the median modeled monthly outcome. It is useful for planning and comparing alternatives.
P90 is the amount the modeled cost stays below in 90 percent of simulated outcomes. It is better suited to budget approval and policy gates.
P95 exposes a deeper tail and helps teams see whether retries, fallbacks, adoption or context growth can create disproportionate downside.
What Class1 models
The cost architecture behind an AI feature.
Class1 can model input and output tokens, cached tokens, request volume, model routing, fallback rates, retries, context growth, tool use, adoption uncertainty, cloud components, contingency and escalation over time.
The estimate is not presented as certainty. Every result declares its maturity and assumptions, and can be calibrated against post-merge actuals as evidence accumulates.
That creates a closed loop: estimate, approve, deploy, measure, reconcile and improve.
Use cases
Where pre-merge AI cost estimation is most useful.
Model upgrades: compare the quality gain against the expected and tail monthly cost increase.
Agent workflows: estimate repeated steps, retries, tool calls, context replay and escalation to more expensive models.
Budget governance: require review or fail CI when a pull request exceeds an approved P90 monthly delta.
FinOps collaboration: give engineering and finance one traceable basis of estimate before deployment.
FAQ
AI cost estimation questions
What is AI cost estimation?
It is the process of forecasting the operating cost of an AI workload from expected usage, model pricing and uncertainty in the system design.
How is Class1 different from an AI spend dashboard?
A spend dashboard reports production costs after they happen. Class1 estimates the cost impact of a proposed code change before merge.
Does Class1 only estimate token costs?
No. The estimate can include retries, fallbacks, tool calls, context growth, cloud components, contingency, escalation and footprint effects.
Can Class1 block a pull request?
Yes. Teams can configure a P90 monthly cost threshold as a CI policy gate. Advisory mode remains available when blocking is not appropriate.
Can estimates improve after deployment?
Yes. Actual usage and cost data can be reconciled against the estimate to improve future assumptions and estimate maturity.
Next step
Put the cost decision where the architecture decision happens.
Review the worked example to see how a model change, larger output ceiling and retry policy alter the monthly cost distribution.