Today marks a major milestone for HoneyHive – we're announcing $7.4M in total funding, including a $5.5M Seed round led by Insight Partners and our previously unannounced $1.9M Pre-Seed led by Zero Prime Ventures. This new funding will help us accelerate our core mission — to enable more teams to build reliable AI-powered products that actually work through systematic evaluation and observability.
I'm incredibly grateful for the investors who have joined us on this journey. Our Seed round saw participation from Zero Prime Ventures, 468 Capital, and MVP Ventures, while our Pre-Seed included AIX Ventures, Firestreak Ventures, and notable angel investors like Jordan Tigani (CEO at Motherduck), Savin Goyal (CTO at Outerbounds), and many others. We're particularly excited to welcome George Mathew, Managing Director at Insight Partners, to our board of directors, bringing his extensive experience scaling enterprise software companies.
This funding announcement comes alongside our GA launch today, after a beta period that saw us grow over 50x in requests logged through our platform in 2024 alone. I want to share the story of how we got here, the problems we're solving, and where we're headed next.
The Painful Reality of AI in Production
There's a critical gap between AI prototypes and production-ready AI systems today. After talking with hundreds of teams, from startups to Fortune 500 enterprises, we saw a consistent pattern: they'd build something promising in a very short amount of time, but when they tried to deploy it, everything would break in unexpected ways. Without proper tools to understand what was happening or how to systematically improve their systems, they'd get stuck in an endless cycle of ad-hoc fixes and in many cases, roll back the solution completely.
Here’s why:
Traditional testing fails with AI – Despite recent advances in reasoning capabilities, LLMs today are largely unreliable. While lack of reliability can be circumvented with better testing and faster iterations, LLMs’ inherent non-deterministic means traditional unit and integrations tests don’t work. As a result, most teams today are stuck manually annotating examples and simply relying on “vibe checks” to measure quality.AI engineering is fundamentally iterative – Pre-trained models offer easy access to AI via simple API calls, but sacrifice transparency. Debugging compound systems—where multiple LLMs interact with various APIs—becomes nearly impossible without visibility into their internal workings.AI engineering breaks traditional SDLC – Unlike traditional software where you can plan features months in advance, AI demands continuous experimentation, measurement, and iterative refinement against real-world data from users, which breaks the traditional software development lifecycle and DevTools built around that paradigm.
The traditional Software Development Lifecycle (SDLC) isn’t compatible with iterative AI development.
This fundamental gap between promising prototypes and reliable production-ready systems is a key reason why AI, despite all its promise, has struggled to deliver real ROI for many organizations.
How HoneyHive Solves This
We built HoneyHive on these key principles:
End-to-end evaluation throughout the AI lifecycle - We allow teams to systematically evaluate their AI agents from initial development through production and beyond, using LLMs, code, and human feedback. This means catching edge cases early, understanding performance degradation, and having a clear picture of how your systems behave in the real world.OpenTelemetry-based agent observability - We built our platform on OpenTelemetry standards because we believe in integrating with your existing stack, not replacing it. This gives you unprecedented visibility into your AI systems – not just what they output, but how they reached those conclusions, where they get stuck, and what patterns lead to failures.Closing the loop between development and production - Perhaps most importantly, we close the loop between what happens in production and your development cycle. When failures occur in the wild, we help you capture those scenarios, create test cases from them, and ensure your next iteration doesn't repeat the same mistakesA collaborative system of record - We've built a complete system of record that versions all traces, prompts, tools, datasets, and evaluators throughout your AI development lifecycle. This ensures full auditability and traceability, enabling teams to track system evolution, identify which components were involved during incidents, and maintain compliance in regulated environments.
The new AI Development Lifecycle, Powered by HoneyHive’s iterative workflows
During our beta period, we've seen this approach transform how teams build and deploy AI products. One customer was able to improve their agent accuracy on web-browsing tasks by 340% within a few months of implementing HoneyHive. Another enterprise was able to accelerate their development cycle by 5x across multiple business units because they could confidently ship new AI agents knowing they had proper evals in place.
We've been fortunate to build alongside some incredible early customers ranging from leading AI startups to Fortune 100 enterprises, who have shaped our product as much as we have, confirming that our approach is delivering real value.
Accelerating Forward
With this $7.4M in funding, we're accelerating on multiple fronts. We're enabling enterprise-wide AI deployment through expanded integrations and deployment models, building advanced evaluation tooling for running multi-turn agent simulations, and setting new standards and semantic conventions for agent observability within OpenTelemetry.
Our core mission has only strengthened over time: enable more teams to build reliable AI products that work in the real world. The path from promising AI prototype to reliable production system shouldn't be mysterious or reserved for AI labs with unlimited resources.
We're building the infrastructure that will make effective AI development accessible to every organization. If you're building with AI and struggling with the challenges we've described, we’d love to show you how HoneyHive can help. And if you're passionate about solving hard problems in AI, check out our careers page .
The future of AI isn't just about more powerful models—it's about bridging the gap between what's possible in the lab and what works reliably in the real world. That's the future we're building at HoneyHive, and we're just getting started.
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