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Enterprises running hundreds of AI agents and thousands of daily LLM calls lack the visibility to trace execution paths, diagnose failures, and attribute costs. Revefi provides unified AI observability and FinOps across every model request, agent action, and multi-step agentic workflow, helping teams monitor performance, control token spend, and govern production AI from a single platform.
Enforce hard or soft spend caps via policies across your AI infrastructure - OpenAI, Anthropic, and Google in one policy. Your gateway or Revefi gateway stops overspend at runtime with approvals and exceptions not a dashboard reporting what you already paid.
Manage budgeting, forecasting, approvals, limits, and optimization as one lifecycle mapped to FinOps Foundation and token-economics frameworks your organization already trusts. No proprietary model to evangelize before the conversation starts.
Stop burning engineering cycles on custom tools. Get predictable unit economics, understand ROI and agent optimization automatically, so your team ships products.
Trace every interaction from user to agent to model, with granular visibility into token cost, latency, execution paths, and outputs at each stage of the workflow.
Monitor OpenAI, Anthropic, Google Gemini, and Vertex AI from a single AI observability platform, without stitching together separate vendor dashboards.
Detects missing organization, service, model, user, or agent metadata before incomplete telemetry compromises AI cost allocation, showback, chargeback, or reporting accuracy.
Connect Revefi to your AI providers and endpoints to generate actionable cost, performance, and reliability insights in a few minutes.
AI and engineering teams gain visibility into their LLM and agent deployments and operational rigor of FinOps, DataOps making your AI infrastructure fully observable, attributable, and audit-ready.
Monitor per-agent latency, request volume, prompt and response data, and token throughput in real time. Benchmark performance across GPT, Claude, and Gemini to identify slow models, agent bottlenecks, and inefficient AI workflows.
Track input and output token volume alongside AI spend and historical trends to identify efficiency drift, uncover rising inference costs, and token consumption.
Visibility, Traceability and Auditability into Agentic actions from day one.
Pinpoint which users, agents, or prompts are driving up costs and trace any anomaly to its driver.
Monitor every interaction (from individual LLM requests to complex, multi-step agentic AI workflows) with end-to-end tracing, performance insights, and audit-ready records that expose failures before they go undetected.
Attribute AI spend to the teams and business units generating it, measure adoption across users, services, and models, and enforce cost controls before usage translates into budget overruns.
Allocate AI costs across business units, cost centers, organizations, teams, and organizational hierarchies. Track adoption by user, service, model, and workload to understand where AI delivers value and where utilization remains low.
Apply AI usage policies, budget thresholds, and spend controls across models and agentic workflows. Route cost anomaly alerts to the responsible team and detect missing attribution metadata before it compromises showback, chargeback, or financial reporting.
Identify high-latency prompts across models and agentic AI workflows, analyze prompt reuse to reduce redundant model calls and token spend, and correlate prompt-level patterns with output-quality metrics to pinpoint instructions that generate inconsistent, inaccurate, or low-quality results.
Revefi connects to model providers and agents without complex integrations or application code changes. It captures model requests, agent actions, token consumption, latency, failures, and cost events at a granular level, providing real-time AI observability and FinOps visibility across providers, models, users, teams, and multi-step workflows.
Link Revefi to your AI providers and model endpoints without deploying additional agents or modifying application code.
Continuously track every LLM call, agent step, token event, latency spike, failure, and cost signal across the AI stack.
Correlate AI cost, performance, reliability, and usage in one unified view across providers, models, agents, users, teams, and time periods.
Apply actionable recommendations to reduce token spend, improve slow prompts, control runaway agent workflows, eliminate redundant model calls, and improve output consistency and quality.
Unprecedented AI adoption and Spend sprawl
Works Seamlessly with Your Existing Stack
Zero-touch, read-only integration. No agents, no pipeline changes.
