Helicone: A Revolutionary Observability Platform for Generative AI Companies

Artificial intelligence is rapidly transforming the business landscape, and generative AI is one of the most exciting areas of innovation. However, as more companies adopt generative AI, they face several challenges, such as cost management, rate limits, and opaque errors. This is where Helicone comes in. In this article, we will explore Helicone, an open-source observability platform for generative AI companies, and its unique features.

What is Helicone, and how does it work?

Helicone is an open-source observability platform that helps companies building with generative AI to manage costs, result quality, and latency. With one line of code, companies can log requests made to providers like OpenAI and monitor them. Helicone sits on top of wherever inference requests are called and adds negligible latency while logging the full state of the request and response for any endpoint. Users can also add application-related metadata to these requests for analytics.

Helicone's web-based application provides insights into key metrics, such as which users are disproportionately driving costs and what is the token usage broken down by prompts. Users can filter and navigate this data based on their custom logic and export it to the rest of their data science workflow.

Integrating Helicone with a single line of code is straightforward. It is a proxy service that executes and logs requests, secured by Cloudflare workers around the globe to add less than a scratch to the overall latency. Once integrated, users can customize requests with properties like the user, conversation, or session ID to group requests and get metrics like the total latency, the users disproportionately driving OpenAI costs, or the average cost of a user session.

One of the unique features of Helicone is that it caches completions to save on bills and helps overcome rate limits with intelligent retry techniques. Users can easily cache their completions, customize their cache for their application's unique requirements, and configure retry rules when they run into rate limits.

Who are the founders of Helicone, and what experience do they bring?

The founders of Helicone are Scott Nguyen, Barak Oshri, and Justin Torre. Scott brings UX and finance expertise, with four years of experience across Tesla, Bain Capital, and DraftKings. Barak brings machine learning expertise, with seven years doing research, teaching, and engineering across the Stanford AI Lab and Sisu Data. Justin brings platform and full-stack expertise, with seven years across Apple, Intel, and Sisu Data.

Scott, Barak, and Justin are on a mission to make it extremely straightforward to observe and manage the use of language models. They understand the challenges that companies building with generative AI face, and they have designed Helicone to address those challenges.

What problem does Helicone solve?

Companies building with generative AI face several challenges, such as cost management, rate limits, and opaque errors. Helicone solves these problems by providing an analytics interface for understanding metrics broken down by users, models, and prompts with visual cards. It helps users manage costs, track the metadata of their requests, and overcome rate limits with intelligent retry techniques.

For instance, if a company is running into rate limits with its provider, Helicone's retry rules can help route requests to another provider or retry them later. Helicone also helps companies fine-tune their models and achieve cost savings by providing insights into key metrics such as which users are disproportionately driving costs and what is the token usage broken down by prompts.

What are the benefits of using Helicone?

Helicone provides several benefits to companies building with generative AI. Here are some of the key benefits:

Improved observability: Helicone provides a simple and efficient way to monitor and track the use of generative AI models. It allows companies to easily log requests made to providers like OpenAI and monitor their costs, result quality, and latency.

Better cost management: Helicone helps companies to better understand the unit economics of their product, such as the average cost of a user or conversation. With Helicone, companies can easily track their usage and identify users who are disproportionately driving costs.

Intelligent retries and caching: Helicone caches completions to save on bills and helps overcome rate limits with intelligent retry techniques. This can significantly reduce costs and improve the performance of generative AI models.

Customizable requests: Helicone allows companies to append custom information to group requests, such as the user, conversation, or session ID. This makes it easier to analyze and visualize metrics like total latency, the users disproportionately driving costs, or the average cost of a user session.

Simple integration: Integrating Helicone with a company's existing system is easy and requires only a single line of code.

Overall, Helicone offers a comprehensive solution for monitoring, tracking, and managing the use of generative AI models. It provides companies with valuable insights into the performance and cost of their models, helping them to make better decisions and optimize their resources.

How does Helicone compare to other observability platforms?

There are several observability platforms on the market, but Helicone stands out in a few key ways.

First, Helicone is specifically designed for companies building with generative AI. This means that it provides unique features and metrics that are tailored to the needs of these companies. Other observability platforms may not have these features, or they may require additional customization.

Second, Helicone is open-source, which means that companies can customize and extend the platform to suit their specific needs. This can be a significant advantage for companies that require more advanced or specialized features.

Finally, Helicone is easy to integrate with existing systems, requiring only a single line of code. Other observability platforms may require more complex integration processes, which can be time-consuming and expensive.

Overall, Helicone offers a unique combination of features, flexibility, and ease of integration that make it a compelling option for companies building with generative AI.

Conclusion

Helicone is an open-source observability platform for companies building with generative AI. With one line of code, companies can easily log requests made to providers like OpenAI and monitor their costs, result quality, and latency. Helicone provides valuable insights into the performance and cost of generative AI models, helping companies to make better decisions and optimize their resources.

With its unique features, flexibility, and ease of integration, Helicone is a compelling option for companies that want to improve their observability of generative AI models. As the use of generative AI continues to grow, Helicone is well-positioned to become a key tool for companies in this space.