INTELLIGENCE
Experts Inference Models Pricing Skills Playbooks Articles Resources
Sign in Meet your experts
  1. Home
  2. /Articles
  3. /Development
  4. /AI testing and evals
  5. /Model swaps and gates

Model swaps and quality gates

Golden sets and labelsScoring model outputJudges and calibrationStubbing and replayModel swaps and gatesDrift detection
Development

Model swaps and quality gates

  • A Cost Per Accepted Output: The Metric That Decides a Model Swap
  • A Quality Gate for a Cheaper Model: Scoring Before You Switch
  • A Test Set for Model Swaps: Label Once, Reuse Across Versions
  • Scoring Outputs Before You Switch to a Cheaper Model
  • Thresholds That Fire Too Often: Tuning a Gate
  • When the Judge Rejects a Cheap Model: Distinguishing Faults
  • A Fault-Injection Suite for Model Swaps
  • A Golden Set Where the Cheap Model Should Win
  • A Rejection Taxonomy: Codes, Owners and Thresholds
  • Correlating a Model Swap with a Quality Drop in Traces
  • Estimating Model Swap Costs from Your Test Set Runs
  • Hidden Costs of a Model Swap: Retries, Re-prompts, Review
  • Output Invariants to Check Before Any Model Swap
  • Regression Tests for a Prompt Change Without a Human Judge
  • Shadow Traffic for a Candidate Model: Running Both and Comparing
  • Shadow Traffic for a Cheaper Model: Sampling Rate and Duration
  • The Cost of a False Positive: Pricing Each Gate Mistake
  • Who Signs Off on a Model Change, and on What Evidence
AI testing and evals

Experts · Inference · Models · Docs · Skills · Pricing · Articles · Skills API · About

© 2026 Quanta Intelligence · 8 The Green STE B, Dover, DE 19901