HoneyHive was founded in 2022 in New York by Mohak Sharma and Dhruv Singh, who came from backgrounds building data and AI platforms at companies like Templafy and Microsoft, including work tied to Microsoft's OpenAI innovation efforts. They recognized that as LLM applications and autonomous agents moved toward production, teams lacked the observability and evaluation tooling that traditional software engineering takes for granted, making it dangerously easy to ship unreliable AI.
The platform is built natively for capturing LLM and agent traces. Using OpenTelemetry-based instrumentation, HoneyHive records the full execution of an AI workflow, including prompts, tool calls, retrievals, model responses, latencies, and costs. This gives engineering teams visibility into exactly what their agents are doing in development and production, which is essential for debugging the non-deterministic behavior of multi-step AI systems.
On top of observability, HoneyHive provides a comprehensive evaluation framework. Teams can define automated evaluators, run offline and online evals against curated datasets, compare prompt and model versions through experimentation, and monitor quality metrics continuously in production. By unifying tracing, evaluation, datasets, and experimentation in one place, the platform helps teams quantify reliability and prevent regressions as they iterate.
HoneyHive raised $7.4 million in seed and pre-seed funding led by Insight Partners, with participation from a group of investors including 468 Capital, AIX Ventures, and Firestreak Ventures, among others. The funding supports its push to help enterprises deploy dependable AI agents at scale, a need that has grown sharply as agentic systems move into real workflows.
HoneyHive competes in the crowded LLM observability and evaluation market alongside several well-funded players, but its agent-first, OpenTelemetry-native design and tight coupling of tracing with evaluation make it a strong fit for engineering teams whose primary challenge is making multi-step agents reliable in production.