LLMOps platform for prompt engineering, evaluation, and optimization with collaborative workflows for AI product development teams.
Helps teams collaborate on AI prompts and models — test, evaluate, and improve AI quality with your whole team.
Humanloop is a comprehensive LLMOps platform that streamlines the development, evaluation, and optimization of LLM-powered applications. Unlike general-purpose ML platforms, Humanloop is purpose-built for the unique challenges of working with large language models, providing specialized tools for prompt engineering, evaluation, and human-in-the-loop workflows.
The platform's prompt engineering environment supports versioning, A/B testing, and collaborative development of prompts across teams. Humanloop automatically tracks performance metrics, costs, and quality indicators across different prompt versions, enabling data-driven optimization of AI applications. The system supports complex prompt templates with variables, conditional logic, and multi-step workflows.
Humanloop's evaluation framework combines automated metrics with human evaluation workflows. Teams can set up custom evaluation criteria, recruit human evaluators, and create feedback loops that continuously improve model performance. The platform integrates with popular LLM providers and supports fine-tuning workflows for domain-specific optimization.
For production deployments, Humanloop provides monitoring, logging, and analytics specifically designed for LLM applications. The platform tracks token usage, latency, failure rates, and quality metrics in real-time, with alerting and automated optimization capabilities.
Humanloop excels in scenarios where teams need to iterate rapidly on LLM applications while maintaining quality and cost control. Product teams use it to optimize customer-facing AI features, while enterprise developers leverage it for building reliable AI agents and automation systems.
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LLMOps platform for prompt engineering, evaluation, and optimization with collaborative workflows for AI product development teams.
Version-controlled prompt development with team collaboration, branching, merging, and deployment workflows similar to software development practices.
Use Case:
Product and engineering teams collaboratively developing and testing different prompt variations for a customer support chatbot, with staged rollouts and performance tracking.
Built-in evaluation metrics plus custom test suites that automatically assess prompt performance across quality, safety, and business metrics with every change.
Use Case:
Automatically testing new prompt versions against a golden dataset of customer inquiries to ensure quality doesn't regress when optimizing for cost or speed.
Workflows for recruiting human evaluators, creating evaluation tasks, and collecting feedback to improve model performance through reinforcement learning from human feedback.
Use Case:
Having domain experts evaluate legal document summaries generated by AI to train reward models that improve accuracy and reduce hallucinations in specialized contexts.
A/B testing and comparison across different LLM providers, models, and configurations with cost-performance optimization and automatic model switching.
Use Case:
Testing GPT-4, Claude, and Gemini for different agent tasks to optimize cost-performance trade-offs, automatically routing simple tasks to cheaper models.
Real-time monitoring of LLM applications with cost tracking, performance metrics, user feedback collection, and automated alerting on quality degradation.
Use Case:
Monitoring a sales assistant agent for response quality, cost per conversation, conversion rates, and automatically alerting when performance drops below thresholds.
End-to-end fine-tuning workflows from data preparation through model training, evaluation, and deployment with support for multiple fine-tuning approaches.
Use Case:
Fine-tuning a model on company-specific product information and customer interaction patterns to improve accuracy for domain-specific agent applications.
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View Pricing Options →Product teams iterating on customer-facing AI features with quality and safety requirements
Enterprise applications requiring systematic prompt optimization and performance monitoring
Cross-functional teams needing collaboration tools for AI product development
Applications requiring human-in-the-loop evaluation and continuous improvement workflows
Humanloop works with these platforms and services:
We believe in transparent reviews. Here's what Humanloop doesn't handle well:
Humanloop provides systematic evaluation, A/B testing, and performance tracking that's difficult to implement with ad-hoc prompt management. It also enables non-technical team members to contribute to prompt development through collaborative interfaces.
Yes. Humanloop supports any model accessible via API, including custom models, fine-tuned models, and local deployments. The platform provides tools for fine-tuning workflows and custom model evaluation.
Humanloop includes automated metrics like perplexity, similarity scoring, and safety checks, plus frameworks for custom business metrics. It also supports human evaluation workflows for subjective quality assessment.
Yes. Humanloop provides production APIs with low latency, monitoring, and reliability features. However, for latency-critical applications, you may want to cache optimized prompts locally rather than calling through Humanloop's API.
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