
HoneyHive is an AI developer platform that provides essential tools for teams to safely deploy and continuously improve Language and Learning Models (LLMs) in production.
It offers a wide range of functionalities that can work with any model, framework, or environment.The platform includes mission-critical monitoring and evaluation tools, ensuring the quality and performance of LLM agents.
It also enables teams to confidently deploy LLM-powered products to their users. Developers can access evaluation test suites for offline evaluation and utilize monitoring capabilities for observability and analytics.
The platform's collaborative prompt engineering toolkit aids in prompt engineering with project managers and domain experts in a version-controlled workspace.Additionally, HoneyHive facilitates debugging of complex chains, agents, and RAG pipelines, leveraging AI-assisted root cause analysis.
It provides evaluation metrics and guardrails, as well as a model registry and version management system. The platform allows data scientists to track experiments and analyze performance, providing self-serve data and insights to application teams.HoneyHive is designed to seamlessly integrate with any LLM stack, supporting any model, framework, or external plugin.
It adopts a pipeline-centric approach, specifically built for complex chains, agents, and retrieval pipelines. Notably, HoneyHive's non-intrusive SDK ensures that requests are not proxied through their servers.With a focus on enterprise-grade security and scale, HoneyHive offers end-to-end encryption, role-based access controls, and data privacy measures.
The platform can be deployed on the HoneyHive Cloud or a company's own Virtual Private Cloud (VPC), providing secure data ownership. Dedicated customer success managers (CSMs) and 24/7 founder-led support are available to assist users at all stages of their AI development journey.
Releases
Pricing
Prompts & Results
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33,86623Released 21d agoFree + from $19.99/mo
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832,432635v1.6 released 14d agoFree + from $12/moReducing manual efforts in first-pass during code-review process helps speed up the "final check" before merging PRs
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