LLM Evaluation API

The challenge

LLM quality is difficult to compare when models, prompts, deployment targets, and evaluation criteria all change independently. Teams need a consistent way to run evaluations without coupling every experiment to a single provider or hosting model.

The approach

This Python API provides a common evaluation layer for cloud and self-hosted LLM endpoints. An evaluation can combine a target deployment, a prompt or fine-tuning candidate, and a configured set of metrics so that results remain comparable across experiments.

The workflow supports:

• evaluating prompts, models, deployments, and fine-tunings;
• selecting metrics appropriate to the use case;
• working across cloud-hosted and self-hosted endpoints; and
• retaining evaluation context for later comparison and analysis.

Engineering focus

The API is designed around replaceable endpoint and metric integrations rather than one fixed evaluation recipe. Cloud Run supplies the service boundary, while Qdrant supports workflows that benefit from vector-based retrieval or comparison. The result is evaluation infrastructure that can evolve alongside the models it measures.