Shadow simulation before cutover
Run the candidate model in shadow mode for 30 days to validate cost and quality projections against your real traffic shape.
FinOps Use Case
Shadow simulate the target model for 30 days, validate your projections, and log the migration decision with a full audit trail.
The Problem
Production AI migrations often go wrong because projections from benchmarks don't match real-workload performance. Teams switch, see quality regressions, and roll back — with no record of what happened.
Migrating production AI workloads to a new model is high-stakes. TokenPrice.dev's shadow simulation runs the target model alongside your current one, validating projected savings and quality before you cut over.
What You Get
Run the candidate model in shadow mode for 30 days to validate cost and quality projections against your real traffic shape.
Simulations report a confidence band (high/medium/low) and cost/quality ranges so you plan for variance, not just the ideal case.
Hard governance failures block the migration recommendation automatically — no manual checklist needed.
The full chain — brief → simulation → decision → outcome — is logged and reviewable at any time.
How It Works
Generate a decision brief
Profile your workload and get ranked candidates with projected savings and risk assessment.
Run a shadow simulation
Validate projections over 30 days of shadow traffic before touching production.
Log the migration decision
Record the migrate, test, or hold decision with rationale and track outcomes over time.
FAQ
It models cost delta, quality delta, and reliability delta based on your workload's token shape and volume against the candidate model's live pricing and benchmark data.
Hard governance failures — missing required controls like data residency or audit logging — automatically set the primary action to 'hold' and flag the specific control gaps.
Use Cases
All FinOps use cases →
Model Data
Explore 350+ models →
Price Changes
Track price volatility →