Humanloop
LLM ops platform for prompt management, model evaluation, and fine-tuning - used by AI product teams to iterate fast and monitor production reliability.
Humanloop is an LLM operations platform that helps AI product teams manage the full lifecycle of prompts and models in production applications. Developers connect their app to Humanloop via SDK and all LLM calls are automatically logged with inputs, outputs, latency, cost, and model version. Prompts are versioned in a central registry where teams can A/B test variants, run evaluations against custom test datasets, and push winning versions to production without code deploys. Humanloop supports GPT-4, Claude, Gemini, Llama, and Mistral - allowing side-by-side model comparisons on identical prompts. Founded in 2021 and Y Combinator-backed, Humanloop raised a $10M Series A in 2024. Free tier covers up to 1,000 logged LLM calls per month; Starter plan at $60/month supports 50,000 monthly calls.
Key Features
- Prompt registry with version control, diff comparison, and one-click production deployment
- Automatic logging of all LLM calls with inputs, outputs, latency, token count, and cost tracking
- Evaluation framework for running prompts against custom test datasets with pass/fail scoring
- A/B testing for comparing prompt variants or model swaps on real production traffic
- Human feedback collection for RLHF workflows and fine-tuning dataset construction
- Multi-model support - compare GPT-4, Claude, Gemini, and Llama on identical prompts side-by-side
Use Cases
- AI product teams iterating on prompts across development, staging, and production without redeploying code
- ML engineers setting up evaluation benchmarks and catching model regressions on each prompt update
- Teams collecting human feedback on AI output quality to build fine-tuning datasets for model improvement
- Engineering managers tracking LLM cost, latency, and error rates across a multi-model AI product
Pros
- Prompt versioning and production deployment decouple AI iteration from software release cycles
- Side-by-side model comparison on real prompts makes provider switching a data-driven decision
- Human feedback collection is built in - no separate infrastructure needed for RLHF dataset collection
Cons
- SDK integration requires adding Humanloop to every LLM call path - more invasive than proxy-based tools
- Free tier caps at 1,000 logged calls per month - production apps hit this limit quickly
- Evaluation framework requires writing test cases manually - no automatic test generation
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