Intelligence for AI Agents, LLMs, and Multi-Model Workflows

Revefi gives data, AI, and engineering teams cost visibility, reliability monitoring, and agent governance across every model, provider, and user  in one unified platform.
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Automated Data Quality

AI Observability

For Agentic AI and LLMs get insights for every model call, agent action, latency, and failure across your AI stack. From single LLM calls to multi-step agent workflows, every interaction is visible, traceable, and audit ready.
  • Agentic Observability: Full user → agent → model attribution chain with per-agent latency, request volume, and complete prompt/response capture for every step
  • LLM Observability: Latency benchmarking across models (GPT, Claude, Gemini), throughput metrics in tokens/sec, and failure rate tracking across providers and time windows
  • Activity Logs: Searchable, filterable logs of every prompt and response — the slow query log for your AI inference layer
Automated Data Quality
Spend Optimization

AI FinOps

Gain full cost attribution across your entire AI stack,  multiple providers, models, agents, and users.
  • Cost Attribution: Full breakdown by provider (OpenAI, Anthropic, Google), model, agent, and user tracked to the cent.
  • Token Economics: Input vs. output token analytics, with trend analysis.
  • Cost Outlier Detection: Identify which users, agents, or prompts are driving spend and use cache hit rate monitoring as a direct optimization lever.

Prompt Optimization

Understand which prompts are slow, expensive, or producing inconsistent outputs, so that teams can optimize without guesswork.
  • Prompt Analysis: Identify the highest latency prompts across models and agentic workflows.
  • Hit Rate Tracking: Monitor prompt reuse patterns to reduce redundant model calls and lower token costs.
  • Correlation: Connect prompt-level patterns to output quality metrics to identify what's driving poor or inconsistent results.
Performance Optimization
Gain insights into your AI agents and LLMs?
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