New capabilities give data, AI, and engineering teams cost attribution, benchmarking, traceability, and integration across LLMs and agents.
Revefi today announced AI Observability and Agentic Observability, new capabilities that extend its platform to give enterprises greater visibility into the performance, cost, and reliability of LLM and AI agent deployments. The announcement coincides with the Gartner 2026 Data & Analytics Summit in Orlando, March 9–11, where Revefi will be exhibiting at Booth 206.
Revefi helps enterprises move AI initiatives from experimentation to production by unifying data and AI operations”
— Sanjay Agrawal
Why This Matters?
The growing complexity of enterprise AI stacks has made observability a top priority for technology leaders. As organizations rapidly deploy AI agents and large language models into production workflows, they face a growing blind spot: the inability to trace what happened, where it went wrong, or what it cost. Revefi’s new capabilities address this directly, providing a unified observability layer across OpenAI, Anthropic’s Claude, Google Gemini, and Google Vertex AI deployments.
Marketing Technology News: MarTech Interview with Omri Shtayer, Vice President of Data Products and DaaS at Similarweb
“Enterprises are running dozens of AI agents and making thousands of model calls a day, but most still lack clear visibility into agent behavior, cost, and failure points,” said Sanjay Agrawal, Co-Founder and CEO of Revefi. “We built AI Observability and Agentic Observability to give data, AI, and engineering teams the visibility and actionable insight they need to manage AI infrastructure with confidence. Revefi helps enterprises move AI initiatives from experimentation to production by unifying data and AI operations.”
Marketing Technology News: Martech Architecture For Small Language Models: Building Governable AI Systems At Scale
Full Observability Across LLMs and Agents
Revefi’s AI Observability delivers benchmarking across models including GPT, Claude, and Gemini, along with throughput metrics in tokens per second and failure rate tracking across providers and time windows. Searchable, filterable activity logs capture prompts and responses, helping teams investigate failures, latency spikes, and cost anomalies.
Revefi’s Agentic Observability provides attribution from user interaction to agent execution to model response, including latency, volume, prompts, and responses across multi-model workflows. This helps teams monitor both simple and complex AI deployments, making each step easier to inspect, troubleshoot, and audit.
