Revefi
by Revefi
AI FinOps and observability agents that cut LLM, Snowflake, Databricks and BigQuery spend
Revefi is an AI FinOps and observability platform that tracks what LLMs, agents and cloud data warehouses cost, finds waste and bad data, and recommends or applies fixes. It is aimed at data platform, FinOps and AI engineering leaders who need token and warehouse spend attributed to users, teams and outcomes.
Revefi, founded in 2021 by former ThoughtSpot engineers Sanjay Agrawal (CEO) and Shashank Gupta (CTO) and based in Redmond, Washington, started as a data observability and cost tool for cloud warehouses and has extended into AI spend. Its RADEN agent, launched with a $20M Series A led by Icon Ventures in September 2024, monitors Snowflake, Databricks, Google BigQuery and Redshift for slow queries, idle compute and data quality problems, and acts as an AI DBA that tunes warehouses. On March 11, 2026 Revefi previewed AI Observability, which benchmarks GPT, Claude and Gemini models on throughput, failure rates and cost with searchable prompt and response logs, and Agentic Observability, which traces a request from the user through each agent step to the model response. On June 8, 2026 it announced general availability of FinOps for Agents, AI and Data, adding Token Economics views by outcome, department, waste and optimisation, per-user attribution, spend alerts and ROI tracking per AI action across OpenAI, Anthropic, Gemini and Vertex AI. Setup is read-only and needs no code changes. Revefi says a Fortune 500 customer caught one user's unexpected $76,000 token spend within minutes, and cites customers including AMD, Verisk, Stanley Black & Decker, Ocean Spray, Ingersoll Rand and Cribl. It has raised $30.5M from Mayfield, Icon Ventures, StepStone Group and GTM Capital, and says it is a Pioneer in Gartner's 2026 Emerging Market Quadrant for AI FinOps tools.
Heads of data platform or FinOps at companies spending heavily on Snowflake, Databricks or BigQuery and starting to see LLM token bills grow.
One read-only tool attributes warehouse and token spend to users, teams and outcomes and flags runaway costs within minutes.
At a Glance
- Category
- Data & Analytics
- Pricing
- Freemium, Usage-based, Contact for pricing
- Target Market
- CIOs, CTOs, Data Engineering Leaders, FinOps Teams
- Deployment
- Cloud-only, API-based
- Founded
- 2021
- Headquarters
- Redmond, USA
Key Features
- ✓Token Economics
Breaks AI spend down by outcome, department, waste and optimisation, so leaders see which AI use cases return value.
- ✓Per-user cost attribution
Attributes LLM and agent cost to individual users, agents and models with outlier detection, exposing a single runaway workload quickly.
- ✓Agentic Observability
Traces a request from the user through each agent step to the model response, with latency, volume, prompts and responses for auditing.
- ✓AI DBA for warehouses
Tunes Snowflake, Databricks, BigQuery and Redshift warehouses, fixes slow queries and reclaims idle compute automatically.
- ✓Budgets and spend controls
AI plans add budgeting, forecasting, user and team spend limits, approval workflows and budget enforcement for model usage.
- ✓Data observability and quality
Automated monitors detect bad or late data before it reaches dashboards and downstream applications.
- ✓Claude Code agent and MCP server
Works inside Claude Code to find an over-provisioned job, open a Jira issue and raise a pull request with the fix.
Capabilities
Use Cases
- •Stopping runaway token spend
A Fortune 500 customer used Revefi to spot one user's unexpected $76,000 token spend on a single AI use case within minutes, according to Revefi.
- •Warehouse cost reduction
Data platform teams let the AI DBA right-size Snowflake and Databricks compute and fix expensive queries, with Revefi claiming 30-70% lower spend.
- •AI chargeback by department
FinOps teams allocate model costs to departments and outcomes, then set budgets and approval workflows for teams exceeding their allocation.
- •Model benchmarking for cost and reliability
AI engineering teams compare GPT, Claude and Gemini throughput, failure rates and cost on production traffic before switching models.
Ideal For
Best For
- ✓Attributing LLM token spend by user, team, agent and model across OpenAI, Anthropic and Gemini
- ✓Cutting Snowflake, Databricks and BigQuery costs through automated query and warehouse tuning
- ✓Catching runaway AI spend from a single user or use case before the monthly bill arrives
- ✓Data teams that want quality monitoring and cost optimisation in one product
Not Ideal For
- ✗Teams that need deep LLM tracing and evaluation for application debugging, where dedicated tools such as Langfuse or Arize go further; Revefi's agentic tracing launched in preview in March 2026
- ✗Organisations on warehouses outside Snowflake, Databricks, BigQuery and Redshift
- ✗Buyers wanting flat, predictable pricing, since paid plans are a percentage of data platform spend
Integrations
Deployment
Market Analysis
Pros
- ✓Free tier for smaller data estates and a no-credit-card trial
- ✓Founders with database and data-quality backgrounds at ThoughtSpot, Google, Microsoft and Meta
- ✓Named enterprise customers including AMD, Verisk and Stanley Black & Decker
- ✓Unifies AI FinOps, data observability and warehouse tuning, which otherwise need several tools
Cons
- ✗Savings figures (30-70%, up to 60%, 10x efficiency) are company claims; SiliconANGLE noted the funding coverage had no independent validation
- ✗Paid pricing is a percentage of platform spend, so cost scales with your warehouse bill and is not published
- ✗AI and agentic observability launched in preview in March 2026, so that side of the product is newer than the warehouse tooling
- ✗Last disclosed funding round was in September 2024, and no G2 or Capterra reviews could be retrieved
Pricing
Starter
$0
- ✓Combined annual data platform spend under $50K
- ✓Cost optimisation
- ✓Data observability and quality
- ✓Self-serve support
FinOps / DataOps / All (data)
Contact for pricing
- ✓Priced as a percentage of data platform spend
- ✓No usage limits
- ✓Standard or enterprise support
AI Observability & Operations / AI FinOps
Contact for pricing
- ✓LLM observability with OTEL connector and SDK
- ✓Budgets, spend limits and approval workflows
- ✓Policy creation and enforcement
The free Starter plan covers organisations with under $50K combined annual data platform spend. Paid data plans are billed as an unpublished percentage of Snowflake, Databricks or BigQuery spend, and the AI plans are quote-only; a no-credit-card free trial is offered. Checked on revefi.com/pricing on November 10, 2026.
Security & Compliance
Sources
This page was written from 6 sources, 3 on domains other than revefi.com.
- 1.revefi.com — revefi.comvendor
- 2.revefi.com — pricingvendor
- 3.revefi.com — revefi launches finops observability and token economics forvendor
- 4.siliconangle.com — revefi raises 20m launch worlds first ai data engineer
- 5.techintelpro.com — revefi launches ai observability and agentic observability t
- 6.cbinsights.com — revefi
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