Today, we’re excited to introduce HoneyHive, a platform that helps developers turn their LLM prototypes into production-ready AI products. It offers a unified workflow and an enterprise-grade suite of evaluation and observability tools designed for teams to test, measure, and iteratively improve LLM application performance.
Dhruv and I started HoneyHive last October with the vision that all software will eventually incorporate large generative models as general-purpose reasoning and data transformation engines. We expected this shift to take years, but much to our amazement, this vision started to materialize much sooner than we anticipated. Just a little over a month later, ChatGPT swept the world – countless developers, knowledge workers, and consumers adopted LLMs in their personal lives and day-to-day workflows. Mainstream developers started building LLM apps and autonomous agents. Companies of all sizes are now rushing in to integrate LLMs into their products and internal processes.
However, if you're someone deep in the weeds of building LLM-powered products, you may have a less rosy view. Most companies today are struggling to get these applications live outside of a small beta. The reason? Enterprise leaders find it hard to trust LLMs in mission-critical workflows. Models hallucinate, agents loop, and RAG pipelines constantly fail. Even with a working prototype in hand, teams struggle to gain confidence in the safety and reliability of their LLM applications.
While it's easier than ever to build prototypes with LLMs, thanks to a growing ecosystem of open-source frameworks like LlamaIndex and AutoGPT, it's still challenging to build enterprise-grade AI applications that are safe, robust and reliable at scale.
Building production-ready LLM apps requires constant iteration and testing, much like traditional software, yet most teams fail to systematically test, iterate and improve their products. Here's why:
LLMs fail in ways that are hard to predict and detect. Unlike traditional ML, a failure doesn't appear as a drop in KL divergence – quality is subjective and hard to judge, especially in the absence of ground truth labels. In response, large companies spend hundreds of hours getting domain experts to manually curate “golden datasets” and grade model responses, while others skip testing and validation altogether. This lack of automated and scalable evaluation techniques is holding back most companies from gaining confidence in their LLM products, and others from making any changes to their LLM apps in production.
Measuring performance is the key to being able to improve it. However, currently there is no scalable way to measure performance in production other than asking for feedback from your users. Most teams end up using product analytics tools to do this, which are designed to track clicks and funnels, not analyze unstructured text or detect inconspicuous failures like hallucination or toxicity. This lack of visibility and guardrails leads to an unclear picture of how the model is truly performing, where it fails, and how it can be improved.
While plenty of tools have emerged to help with prompt engineering and prompt evaluation, many LLM applications have evolved well beyond basic “GPT-wrapper” interfaces to complex chains, agents, and RAG pipelines, which most tools do not fully support. Issues in these pipelines often stem from not just the prompt or the model, but also how the pipeline is being orchestrated, the context provided to the model and the retrieval mechanism used (like chunking sizes, search mechanism, etc.), all of which further add to the complexity of measuring and improving performance.
Today, most LLM applications are built by product engineering teams and generalist software engineers, rather than traditional ML practitioners. These teams have no choice but to stitch together various disconnected tools like spreadsheets, internal dashboards and product analytics software to work with LLMs, resulting in wasted developer resources and slower iteration velocity. More so, with the pace at which LLM pipelines are evolving (from prompts to chains, now agents and multimodal in the near future), internally built tools constantly fall behind the market and maintaining them takes away time from valuable product development work.
Our mission is simple – helping developers and product-engineering teams get AI to production safely, reliably and robustly. We aim to achieve this with a unified suite of tools and workflows designed to make the process of evaluating, monitoring and iterating on your LLM applications as scalable and automated as possible.
Getting to production requires rapid iteration, scalable testing and close collaboration with domain experts for feedback. HoneyHive makes this easy with a streamlined workflow for teams to rapidly iterate on prompts and evaluate their LLM applications, all within a collaborative, developer-friendly workspace.
Testing performance pre-production is only half the challenge. Measuring performance and finding sources of errors in production is the key to being able to improve performance, whether via prompt engineering or fine-tuning. HoneyHive makes this easy with powerful observability, self-serve analytics and trace debugging tools.
Our early customers and partners have successfully leveraged HoneyHive to optimize writing assistants, browser agents, documentation bots, code generation pipelines and more. Teams have come together to run detailed evaluations comparing different models, collect fine-tuning datasets, experiment with various performance metrics, set custom guardrails & track interesting user trends. What was previously a disconnected set of spreadsheets, dashboards and bespoke tools has been replaced with a unified, collaborative and customizable workflow in HoneyHive, all accessible via our SDK. Our customers have been able to get AI to production faster and have more confidence in their product's reliability.
MultiOn, a leading personal-assistant AI company, has used HoneyHive to safely deploy its AI agents to thousands of users and set up automated processes using our SDK to improve performance by fine-tuning open-source models, leveraging HoneyHive's monitoring, evaluation, and data filtering and labeling capabilities. "It's critical to ensure quality and performance across our LLM agents," says Divyansh Garg, CEO of MultiOn. "With HoneyHive's state-of-the-art monitoring and evaluation tools, we've not only improved the capabilities of our agents but also seamlessly deployed them to thousands of users, all while enjoying peace of mind."
If you're facing similar challenges in getting AI to production or improving model performance in production, we'd love to help! Sign up to join our public beta, or join our Discord community.
While we've spent the past year deeply understanding our customers' pain points and iterating on the first version of our platform, our team is just getting started. We will continue to partner with leading organizations, scale our team of world-class engineers and researchers, and tackle frontier challenges like multimodality over the next year.
If you find this interesting and would like to join us on this journey, don't hesitate to get in touch with us at firstname.lastname@example.org.