Revefi Extends Its AI DBA to Autonomously Optimize Databricks Ecosystem

FinOps, Observability and Optimization
for your Data and AI

Our AI agents autonomously manage cost, performance, and quality across your data warehouses, LLMs and agentic workloads

AI & Agents · Token Economics

For every model call you make

Observability and FinOps for LLMs and agents, with full user agent model attribution and cost to the cent across OpenAI, Anthropic & Google.

AI FinOps Observability Token Economics Prompt Optimization

To the cent cost attribution

Explore AI Token Economics

AI DBA · for Data Platforms

For every database you manage

An autonomous DBA that tunes warehouses, fixes slow queries, and reclaims idle spend across Snowflake, Databricks, BigQuery & Redshift.

FinOps Performance Operations Quality

30–70% cost reduction

Case Study

A Fortune 500 Insurer using Snowflake

Snowflake warehouses, 711

Snowflake Warehouses

50% spend reduced

Spend Reduction

Results in under 48 hours

Results in < 48 hours

"With Revefi, the value of FinOps and Data Observability became clear within just a few months. They identified savings, improved quality and more than paid for themselves in no time."

Louis DiModugno

Global CDO Verisk

Verisk

Fortune 1000 Company

Read Case Study

5 Minutes

Average time to first insight.

60%

Reduction in Snowflake and cloud data costs.

665K+ Automated Monitors

Overall automated observability without overheads.

100%

Projected annual ROI.

27%

Less Snowflake spend

5×

Beyond the 5–6% expected

3 Weeks

To deliver, start to finish

Expected vs. Actual Savings

30

20

10

0

5-6%

Expected Savings

27%

Actual Savings

~$1M

Saved during the free trial

25%

Of production covered so far

>$3M

Projected across all production

Savings Unlocked

$3M

$2M

$1M

$0M

$1.0M

Free Trial (25% env)

$3.0M

Projected savings

100% Unified

All Data-SLAs and RCA

3x Increased

Interoperability between systems & tools

4 Platforms

In one interface: BigQuery, Looker, Composer & Gemini

Revefi connected to BigQuery, Looker, Composer and Gemini

Used by Innovative Data Teams at Global Brands

Maximize the ROI of your AI, Agents and Data.

Cloud Data Platforms

Observe, optimize cost, quality and performance

LLMs and AI Agents

Observe, measure, optimize cost, usage and performance

Data Platforms

You are experiencing

Blown Budget

Unpredictable compute costs.

Poor Data Quality

Inaccurate data & lack of trust.

Fire-Fighting

Constant operational issues.

Revefi Impact

Reduce Data Cost

by 60%

Increase Efficiency

by 10x

Get Results

in 5 mins

LLMs / AI Agents

You are experiencing

Unknown Budget

Uncontrolled token usage.

Low Adoption

Trust issues with AI output.

Unpredictable Quality

Hallucinations & drift.

Revefi Impact

AI Observability

Full Visibility

AI Metering

Cost Control

AI Quality

Verified Truth

AI Agent for Data Platforms

Observe, optimize cost, quality and performance

LLMs & AI Agents

Observe, measure, optimize cost, usage and performance

Data Platforms

You are experiencing

Blown Budget

Unpredictable compute costs.

Poor Data Quality

Inaccurate data & lack of trust.

Fire-Fighting

Constant operational issues.

Revefi Impact

Reduce Data Cost

by 60%

Increase Efficiency

by 10x

Get Results

in 5 mins

LLMs / AI Agents

You are experiencing

Unknown Budget

Uncontrolled token usage.

Low Adoption

Trust issues with AI output.

Unpredictable Quality

Hallucinations & drift.

Revefi Impact

AI Observability

Full Visibility

AI Metering

Cost Control

AI Quality

Verified Truth

Agentic AI is redefining the data landscape

Checkout Revefi's AI Data Agent, MCP Server with Claude Code in action.

⌘ Claude Code — ~/revefi/shasta-databricks

◀ ▶ ↻

Projects / TEST / TEST-416

TEST-416

Optimize ETLMedJob cluster configuration with Photon runtime

Description

Updated ETLMedJob cluster configuration based on Revefi's cluster optimization experiment recommendations.

Configuration Changes:

Node type: m5d.large → m7gd.large (Graviton3)
Runtime: STANDARD → PHOTON (vectorized engine)
Added driver_node_type_id and spark_conf for single-node

Benefits:

Better performance and cost efficiency for Auto Loader streaming workloads.

Activity

C

Claude created this issue

Just now

C

Claude changed status to In Progress

Just now

Status

IN PROGRESS

Type

📈 Improvement

Priority

▲ Medium

Assignee

C

Claude (Bot)

Reporter

KR

Kanhaiya R.

Labels

cost-optimization databricks

Sprint

Sprint 24.3

[TEST-416][Part 1] Optimize ETLMedJob cluster config with Photon runtime and m7gd.large #1

● Open Claude wants to merge into main from kr_optimize-etlmed-cluster

Conversation

Files changed

Checks

▾ dbc_bundle/resources/etl_med_job.yml

+6 −2 · YAML

⋯ 20 unchanged lines

21 first_on_demand: 1

22 zone_id: us-west-2d

23 spot_bid_price_percent: 100

24 − node_type_id: m5d.large

24 + node_type_id: m7gd.large

25 + driver_node_type_id: m7gd.large

26 cluster_log_conf:

27 volumes:

28 destination: /Volumes/revefi/default/logs

29 spark_env_vars:

30 PYSPARK_PYTHON: /databricks/python3/bin/python3

31 + spark_conf:

32 + spark.master: local[*, 4]

33 + spark.databricks.cluster.profile: singleNode

34 enable_elastic_disk: false

35 data_security_mode: DATA_SECURITY_MODE_DEDICATED

32 − runtime_engine: STANDARD

36 + runtime_engine: PHOTON

37 kind: CLASSIC_PREVIEW

38 is_single_node: true

⋯ 4 unchanged lines

✓ All checks passed

Key Use Cases

FinOps

Automated insights for financial governance, budgeting, cost allocation, and forecasting.

Automated Data Observability

Automated Data Observability

Automates and optimizes data engineering workflows, fixing data quality.

Automated Data Observability

AI Observability

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.

Automated Data Observability

Automated DataOps

Automates data operations to improve efficiency, accelerate data delivery, and reduce operational overhead.

Automated Data Observability

Revefi has been recognized as a
2025 Gartner Cool Vendor

gartner-cool-vendor-2025

What makes Revefi different?

Self-Tuning

Automatically adapts to changing workloads and usage patterns to optimize spend.

Fine-tunes resource allocation and query performance.

Proactively identifies inefficiencies and suggests precise adjustments.

Self-Driving

Automates complex cost optimization, eliminating manual effort and expertise required.

Operate at peak cost efficiency without constant human intervention.

Proactive, autonomous process that continuously drives down spend and improves performance.

Agentic AI

Dedicated, autonomous agent constantly assessing your Snowflake, Databricks, BigQuery, and Redshift environments.

Actionable insights and recommendations, guiding you to significant savings.

Learn, anticipates future cost trends and suggests preventative measures.

Zero-Touch

Requires minimal setup, integrates in a few minutes.

No disruption to your current workflows or performance.

Delivers immediate savings without any time from your team.

How does Revefi compare?

The only solution that unifies FinOps, DataOps, Performance Optimization and Data Observability.

Agentic AI

FinOps and Data Cost Optimization

Performance Optimization

Usage Observability

Data Observability and Quality

DataOps

Configuration

Autonomous Remediation

ROI

Setup Time

Time to Value

Scale

Self-Tuning

Self-Driving

Revefi

✅ Autonomous

✅ Automated

✅ Automated

✅ Automated

✅ Automated

✅ Automated

✅ Automated

✅ Yes

✅ Greater than 100%

✅ 5 minutes

✅ Hours or Days

Enterprise grade

✅ Yes

✅ Yes

❌ On-premises or Managed SaaS

❌ Basic spend alerting only

❌ Incident detection only

❌ No

⚠️ Manual

❌ No

❌ Manual

❌ No

⚠️ Limited

⚠️ Weeks-Months

2 weeks or Months

Limited to small environment

❌ No

❌ No

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