Token Economics for AI agents, LLMs & multi-model workflows

Cost visibility, reliability monitoring, and agent governance across
every model, provider, and user — in one unified platform.

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Unified across

The blind spot

Dozens of agents. Thousands of model calls a day. No visibility.

As enterprises push AI agents and LLMs into production, they hit a growing gap: they can't trace what happened,
where it went wrong, or what it cost.

Can't trace what happened

Multi-step agent workflows fail silently, with no chain of attribution from user to agent to model.

Can't see what it cost

Token spend sprawls across providers, models, agents, and users — with no breakdown to the cent.

Can't govern the stack

No guardrails, no audit trail, and no way to know which prompts drive poor or inconsistent results.

End-to-end attribution

Every call, traced from user to agent to model

Revefi captures the full attribution chain — per-agent latency, request volume, and
complete prompt/response for every step across your AI stack.

Pillar 01

AI Observability

From single LLM calls to multi-step agent workflows, every interaction is visible, traceable, and audit-ready.

Trace everything

See every model call,
action, latency & failure

Insight into every interaction across your AI stack — so nothing
fails silently and nothing is a black box.

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 GPT, Claude, and Gemini; throughput 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.

Pillar 02

AI FinOps

Full cost attribution across your entire AI stack — multiple
providers, models, agents, and users.

Track to the cent

Know exactly what your AI stack
costs and why

Turn token sprawl into a clear, attributable line item across every provider and team.

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, so you see where
consumption is really going.

Cost Outlier Detection

Pinpoint which users, agents, or prompts drive spend — and use cache hit-rate monitoring as a direct optimization lever.

Pillar 03

Prompt Optimization

Understand which prompts are slow, expensive, or producing inconsistent outputs and optimize without guesswork.

Optimize with data

Fix the prompts driving
cost, latency & drift

Connect prompt-level patterns to real outcomes so teams optimize on evidence, not intuition.

Prompt Analysis

Identify the highest-latency prompts across models and agentic workflows.

Hit Rate Tracking

Monitor prompt reuse patterns to cut 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.

One unified layer

Every provider. Every model.

A single observability and FinOps layer spanning your whole AI stack,
with no per-vendor dashboards to stitch together.

OpenAI

GPT family

Anthropic

Claude

Google Gemini

Gemini models

Vertex AI

Google Cloud AI

Proven outcomes

Value in minutes, not quarters

5 min

To first results — zero-touch

Up to 60%

Reduction in cloud data cost

10x

Operational efficiency gain

Get visibility into your
AI agents and LLMs.

See cost, reliability, and governance for your entire AI stack in
one unified platform — live.

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