Driftcut compares your current model against a candidate on a small, stratified slice of production prompts — and tells you early whether to stop, continue, or proceed to full evaluation.
Teams run a candidate model across the entire prompt corpus before discovering that critical categories break. Wasted spend, slow feedback, and less willingness to test alternatives.
Hundreds of API calls before learning the candidate was never viable for the cases that matter most.
A candidate can look acceptable overall while breaking structured outputs, high-criticality prompts, or latency-sensitive paths.
Before a full evaluation, teams need a fast filter: is this migration promising enough to keep testing?
Driftcut samples representative batches, compares baseline versus candidate across quality, latency and cost, and makes a decision with evidence.
Real prompts with category, criticality, and expected output type. CSV or JSON.
Stratified batches cover the categories that matter. Test 10–20%, not 100%.
Deterministic checks first, judge models only when the signal is ambiguous.
Stop now, continue, proceed to full eval, or proceed only for low-risk categories.
Not for everyone building with LLMs. For teams that already feel the cost of migration testing, quality risk, and slow evaluation loops.
Faster pre-eval loop before running expensive comparisons across providers or model versions.
A repeatable gate before rolling a new model into shared infrastructure or customer-facing flows.
Reduce evaluation waste and catch migration risk before it reaches the full review cycle.
Driftcut classifies what went wrong so you decide the next action: adapt prompts, reject the candidate, or isolate safe categories.
Invalid JSON, missing fields, structure that breaks downstream systems.
Output exists but not in the format or contract your product expects.
Partial response — candidate misses info the baseline captured.
Weaker judgments, missed edge cases, wrong conclusions on complex prompts.
Candidate refuses or hedges more than baseline for the same use case.
Slower where it matters, even when average quality seems fine.
Eval tools measure quality. Driftcut makes a migration decision. If you already use an eval framework, Driftcut is the step before it.
| Eval frameworks | Driftcut / | |
|---|---|---|
| Core question | How good is this model? | Should I keep testing this candidate? |
| Early stopping | — | Decision engine with configurable thresholds |
| Coverage | 100% corpus | 10–20% stratified sampling |
| Failure detail | Score or pass/fail | 8 failure archetypes with examples |
| Budget awareness | — | Cost tracking + spend avoided |
| Output | Metrics to interpret | Stop · Continue · Proceed — with evidence |
No. It's a pre-evaluation filter. Driftcut tells you early whether a candidate is worth a full run — or whether you should stop and save the budget.
No. You need a structured prompt corpus with categories and criticality. No ground-truth labels required — value comes from testing the prompts that already matter in your product.
CLI tool, CSV/JSON corpus, baseline vs candidate comparison, early-stop decision logic, failure archetypes, latency and cost tracking, terminal report, JSON and HTML export.
Eval frameworks answer "how good is this model?". Driftcut answers "should I continue this migration, or stop now?" Use both. Driftcut runs first.
A typical run (120 prompts, 20% tested) costs $0.50–$2.00 in judge calls, plus whatever the candidate model charges. Total spend and spend avoided are tracked in every report.
CLI-first, open source, built for teams already evaluating migrations between LLM providers or model versions. One email at launch — no spam.