Galileo.AI
Headquarters
San Francisco, CAFounded
2022CEO / Founder
Vikram ChatterjiEmployees
11-50Funding
Series B — $68MProducts
1 product listedAvg. AI Score
7.7/10Website
www.galileo.ai
Profile updated
About Galileo.AI
Galileo is an AI observability and evaluation platform founded in 2022 and headquartered in San Francisco, California. The company was co-founded by Vikram Chatterji, Yash Sheth, and Atindriyo Sanyal, with a focus on helping engineering and data science teams build and maintain reliable large language model (LLM) applications.
Galileo's core product provides tools for evaluating LLM outputs, detecting hallucinations, monitoring production performance, and applying guardrails to generative AI systems. The platform supports workflows across the full LLM development lifecycle, from prompt experimentation and fine-tuning evaluation to real-time production monitoring. It is used by teams building applications on top of models from providers such as OpenAI, Anthropic, and others.
The company raised a $15 million Series A round in 2023, bringing its total disclosed funding to approximately $17 million. Galileo operates with a team in the 11–50 employee range and has positioned itself within the emerging LLM operations and observability market, competing with tools such as Weights & Biases, Arize AI, and LangSmith.
Specialties
Financials
Dated, source-cited figures. Estimates are marked.
Products by Galileo.AI
All products by Galileo.AI reviewed by our AI panel of experts.
How Our Review Panel Scores Galileo.AI
Six independent reviewer personalities assessed Galileo.AI's product on their own terms. They disagreed by 1.0 points — The Skeptic was the hardest to convince, The Domain Strategist the most positive.
Averaged across 1 reviewed product in our catalog. Scores are out of 10.
Competitive Landscape
Who Galileo.AI competes with, and how they differ.
Arize is Galileo's most direct rival in LLM observability, offering the same production tracing/eval loop (Arize AX / open-source Phoenix) but leans open-source-first where Galileo sells a proprietary Luna evaluation-model layer.
1 product reviewed on TopReviewed
Braintrust targets AI engineers who write evals in code as part of their development loop, competing with Galileo's no-eval-writing pitch of foundation models that judge output automatically.
1 product reviewed on TopReviewed
Datadog bundles LLM/agent observability into the APM suite enterprises already pay for, competing on convenience and existing procurement relationships rather than Galileo's eval-model accuracy claims -- named directly by analysts as a rival threatened by the Cisco-Galileo deal.
1 product reviewed on TopReviewedLike Datadog, Dynatrace extends its existing enterprise observability platform into AI/agent monitoring rather than building a dedicated evaluation product, competing on bundled procurement rather than evaluation depth.
1 product reviewed on TopReviewedCompany Timeline
Key milestones in Galileo.AI's history, with sources.
- Acquisition
Acquired by Cisco to extend Splunk Observability Cloud's AI agent monitoring
Announced April 9, 2026 and closed May 22, 2026; Galileo becomes part of Cisco's Splunk Observability business, which had already partnered with Galileo a year earlier on the open-source AGNTCY agent-trust consortium.
blogs.cisco.com - Funding
Raises $45M Series B led by Scale Venture Partners
Brought total funding to $68M on the back of 834% revenue growth since the start of 2024, a quadrupled enterprise customer count, and six new Fortune 50 customers including Comcast and Twilio.
prnewswire.com - Launch
Launches Luna, evaluation foundation models for detecting LLM hallucinations
Luna models were fine-tuned for specific evaluation tasks (hallucination, context quality, data leakage, malicious prompts), pitched as cheaper and faster than using GPT-3.5 as a judge.
datanami.com - Founded
Founded by Vikram Chatterji, Yash Sheth and Atindriyo Sanyal
The Google, Uber AI and Apple alumni set out to solve what they called AI's "measurement problem" -- evaluating LLM outputs at scale without relying on slow, expensive human or LLM-as-judge review.
networkworld.com
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