Evaluate any LLM, dedicated endpoint, or inference routing policy on DigitalOcean Inference Engine. Compare quality and performance in a single LLM-as-a-Judge workflow.
Generic benchmark scores show how a model performs on public datasets, not on your prompts, use case, or production endpoints. Without validation on real workloads, teams deploy on intuition and discover quality regressions only after they impact users.
Base models, fine-tuned models, imported models, and routing policies often live in separate systems. Without a consistent evaluation framework, every comparison requires custom tooling, making results harder to trust and repeat.
As models evolve, evaluation often remains a manual, one-time exercise. Without repeatable workflows, teams struggle to detect regressions, track progress across versions, or build evaluation into their deployment process.
Evaluate any model in the DigitalOcean Model Catalog against your own datasets.
Test models deployed on reserved GPU capacity using the same endpoints you run in production.
Evaluate custom or fine-tuned models imported from Hugging Face or DigitalOcean Spaces.
Evaluate any router configuration as a candidate alongside single models, compare quality, and latency against your dataset, and catch misconfigurations before they reach production traffic.
Score outputs against criteria that match your domain, not a generic rubric someone else defined.
Save your full evaluation configuration and re-run it identically as models evolve.
Stop stitching quality scores and serving metrics from separate tools.
Wire evaluation into your development workflow, not just before launch.
Manage the datasets your evaluations depend on—versioned, reusable, and traceable.
Rehearse a change as a full conversation, not a single response, before a real user sees it.
Go from one-off testing to repeatable, production-aligned evaluation workflows across your inference stack.
Save evaluation configurations and re-run them consistently as systems evolve. Same judge, same rubric, same dataset—so results are comparable across versions.
Upload datasets, configure judges, run evaluations, and analyze results in a single platform across your inference stack. No custom pipelines or disconnected tools required.
Evaluate against the same inference endpoints used in production. What you measure matches what your users actually experience.
How to run your first evaluation
How to run your first evaluationEvaluations: Prove Your Routing Policy Actually Works
Evaluations: Prove Your Routing Policy Actually WorksAPI Reference: Evaluations
API Reference: EvaluationsEvaluations overview
Evaluations overviewRun your first evaluation in minutes. Compare quality and performance on real workloads—without managing infrastructure.
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