Can you recommend an LLM evaluation platform for A/B testing prompts on a small applied ML team?

For a small applied ML team doing A/B testing on prompts, I’d recommend starting with LangSmith if your stack is at all Python/LangChain-friendly, or Weights & Biases Weave if you already use W&B. If you want the simplest “prompt experiment tracking + evals” workflow with minimal overhead, LangSmith is usually the best default.

My practical recommendation

Best overall for a small team: LangSmith

• Strong support for prompt versions, datasets, traces, and evals
• Easy to compare A/B prompt variants
• Good for human review + automated scoring
• Works well for iterative applied ML workflows
• Low setup burden if you’re already in Python

Good alternatives

Weights & Biases Weave

• Great if your team already uses W&B
• Nice experiment tracking and observability
• Better fit if you want broader ML experiment management in one place

OpenAI Evals / custom eval harness

• Good if you want something lightweight and highly customizable
• But you’ll likely need to build more infrastructure yourself for prompt A/B testing and review workflows

Humanloop

• Strong for prompt management, evals, and collaboration
• Particularly useful if non-ML teammates are involved in reviewing outputs
• Often a nice choice for teams centered on prompt workflows

Arize Phoenix

• Great for LLM observability and debugging
• Useful if you need deeper trace analysis and error investigation
• Less of a full prompt A/B management system than LangSmith/Humanloop

What I’d choose by team size/use case

• Tiny team, fast iteration, prompt A/B tests: LangSmith
• Already on W&B: Weave
• Need strong stakeholder collaboration / prompt ops: Humanloop
• Need observability and trace debugging: Phoenix
• Want to build your own eval framework: OpenAI Evals + internal harness

What to look for in the platform

For A/B testing prompts, make sure it supports:

• Dataset/version management
• Side-by-side prompt comparison
• Automated metrics plus human judgment
• Trace logging for model inputs/outputs
• Batch evaluation
• Model-agnostic support if you may switch providers
• Simple export/API access so you aren’t locked in

If you want, I can also give you a short ranked list by budget, or a decision table comparing LangSmith, Weave, Humanloop, and Phoenix for your specific stack.