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Kumo Has Generated Billions in Value for Our Customers.

No feature engineering. No ML pipelines. Just the most accurate predictions.

KumoRFM

Free

The free relational foundation model. Start predicting in minutes with zero infrastructure.

Try Free

What's included

  • Pre-trained on thousands of datasets
  • Instant predictions via API
  • Upload CSV or connect your warehouse
  • Python SDK & MCP server access
  • Unlimited tables and row counts
  • Community support
  • Playground & quickstart tutorial
Recommended

KumoRFM Full Platform

Free Trial

Free for qualified teams

The complete production platform. Fine-tune, research agent, forward-deployed engineer.

Book a Demo

What's included

  • Everything in KumoRFM, plus:
  • Fine-tune on your relational data
  • 30%+ accuracy improvement over base
  • AI Research Agent
  • Forward-deployed engineer support
  • Snowflake & Databricks native apps
  • SOC 2 Type II certified
  • Dedicated onboarding support
  • Private cloud / VPC deployment
  • AWS, Azure, or GCP hosting
  • Custom SLAs & uptime guarantees
  • Dedicated customer success manager
  • Priority support & Slack channel
  • Custom integrations & connectors
  • Enterprise SSO (SAML / OIDC)

ROI Calculator

Calculate Your ROI

Kumo delivers the highest-accuracy predictive models on your relational data — in days instead of months, with no feature engineering. Enter your team's parameters to see the financial impact.

Inputs
Time and Cost Savings
ML models your team builds per year5
150
Average time to build one model (weeks)16 wks
4 wks52 wks
Data scientists on ML5
120
Average DS fully-loaded annual cost$200K
$100K$400K
Annual value of 1% increase in accuracy$1M
500100000000
Customer benchmarks — what Kumo has delivered
—Kumo customers typically see 8-30% improvement over existing models.
—Reddit: 8-10% CTR improvement.
—Databricks: 5.4x lift in lead scoring conversions.
Estimated ROI
+3708%

$9.5M estimated annual benefit based on your inputs.

This is an estimate for illustrative purposes only. Actual results will vary based on your organization, data complexity, and use cases.

Breakdown
Cost and Time Savings
Annual cost of model building
Current
$1.5M
With Kumo
$96K
Annual cost saved
$1.4M
Engineering weeks recovered
75 weeks
5 models × 15 wks saved each.
Annual maintenance savings
$78K
Accuracy impact (conservative 8% scenario)
$8M
Based on your value per 1% \u00d7 8% typical improvement.
Summary
Total ROI
Cost savings (build time)$1.4M
Maintenance savings$78K
Accuracy impact (conservative)$8M
Total annual benefit$9.5M
ROI Summary
Total annual benefit$9.5M
ROI+3708%
+3708%

These figures are estimates for illustrative purposes only. Actual results will vary based on your organization, data complexity, and use cases. Contact Kumo for a tailored assessment.

Note: All figures above are estimates for illustrative purposes only. Actual results will vary based on your organization, data complexity, and use cases. Contact Kumo for a tailored assessment.
Sources and Methodology
1.Current build time (3-6 months): Industry-standard range for enterprise ML model development. Default: 16 weeks.
2.Kumo build time (1 week): OTP Bank built a production model in 3 days. 1 week used as modeled floor.
3.DS salary ($200K default): Glassdoor $154K avg senior DS. Fully-loaded 1.3-1.5x base. $200K is conservative mid-senior.
4.Maintenance ($15,500/model/yr): phData, 2024. Cited range: $12K-$19K. Midpoint used.
5.Accuracy (8%/15%/30%): Reddit 8-10% CTR lift; DoorDash 30% over 8-year model.
6.Kumo pricing: Varies by deployment model, number of use cases, and contract terms. Contact Kumo for a quote.
7.Platform ROI benchmarks: Databricks 482% (Nucleus), DataRobot 514% (Forrester), Dataiku 413% (Forrester). Industry context only.
8.87% never reach production: Gartner AI research / VentureBeat. Directional context.
9.80% time on data prep: CrowdFlower / Forbes survey. Directional context.
10.Accuracy-to-revenue: User-input driven. Applies documented Kumo improvement % to the user's stated value per 1%.

Loved by data scientists, ML engineers & CXOs at

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From a leadership team with proven experience

Vanja Josifovski

Vanja Josifovski

CEO and Co-Founder, ex-CTO Airbnb, ex-CTO Pinterest

Jure Leskovec

Jure Leskovec

Co-Founder & Chief Scientist, Stanford Professor

Hema Raghavan

Hema Raghavan

Co-Founder & Head of Engineering, ex-AI Lead, LinkedIn

Backed by the best in the industry

Sequoia CapitalSV AngelA CapitalSamsung Next

With backing from Frank Slootman and Sridhar Ramaswamy (Snowflake), Ben Silbermann (Pinterest), Matei Zaharia (Databricks), Kevin Hartz (A*), Tristan Handy (dbt Labs), Ron Conway (SV Angel), and others.