Why did Arize choose to open source AI observability tooling instead of building a closed SaaS product?
Answered at 16:01 in Ep. #44, Beyond the Model: Observing AI Agents with Mikyo King · Heavybit Podcasts
Why did Arize choose to open source AI observability tooling instead of building a closed SaaS product?
The decision to open source predates the LLM boom. At the time, if you were building AI or LLMs, every developer's tech stack was open source using tools like Keras and MLflow. If you had a SaaS platform, it had to be exponentially better than open source to even be considered. Beyond developer adoption, there's a mission-driven approach: everyone is learning about building AI and LLMs, and you trust the vendor that open sources everything where you can read the code. Additionally, in this new market, open sourcing shapes the industry and vertical by defining what evals and telemetry mean, and it enables collaboration and community contributions.
Full answer and detailsRead the whole answer, key facts and every other question from this episode
In short
The decision to open source predates the LLM boom.
Mikyo King, Head of Open Source at Arize · Ep. #44, Beyond the Model: Observing AI Agents with Mikyo King · Heavybit Podcasts · answer at 16:01
Key takeaways
- The decision to open source predates the LLM boom.
Quote to remember
“It's one thing to capture the information. It's another thing to kind of make that the fossil fuel for improving your system as the next step.”
— Mikyo King
The full answer · about 4 min
The decision to open source predates the LLM boom. At the time, if you were building AI or LLMs, every developer's tech stack was open source using tools like Keras and MLflow. If you had a SaaS platform, it had to be exponentially better than open source to even be considered. Beyond developer adoption, there's a mission-driven approach: everyone is learning about building AI and LLMs, and you trust the vendor that open sources everything where you can read the code. Additionally, in this new market, open sourcing shapes the industry and vertical by defining what evals and telemetry mean, and it enables collaboration and community contributions.
Key facts
- Mikyo King has been a software developer for more than 20 years
- Last three years specifically focused on open source at Arize
- Arize started open source initiative around three years ago for observability and evaluation tooling
- Three years ago questions centered on LLM hallucinations, now focus shifted to agents as a service with higher levels of abstraction
People, companies and topics mentioned
More answers from this episode
- 3:26How does Arize use OpenTelemetry for AI agent observability?
- 5:27What is the difference between Open Telemetry semantics and OpenInference semantics for AI observability?
- 13:44How should teams approach starting with evaluations for AI agents?
- 19:05What security challenges does Arize face when open sourcing AI agent observability?
- 23:44How should open source maintainers handle pull requests from AI coding agents?