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Model ML Pricing, API, Implementation, and FAQ

This page covers Model ML's commercial model, API access, implementation, and the questions buyers most often ask.

Pricing

Model ML does not publish list pricing. The buying motion is sales-led: demos and contact flows are the entry points, and commercial terms are scoped to each engagement.

How buying works

The primary calls to action are:

  • Speak with us

  • Schedule a demo

  • Book a demo now

The contact form asks for:

  • full name

  • work email

  • LinkedIn profile

  • industry

  • how the prospect heard about Model ML

  • message

Industries listed directly on the form include:

  • Investment Banking

  • Private Equity

  • Consulting

  • Venture Capital

  • Hedge Fund

  • Other

API access

Model ML has a public API page with a request form and a View API docs entry point; API documentation is provided through the request flow. MCP/headless access is available for programmatic use.

Implementation and onboarding

  • Connectors for email, cloud storage, and calendars.

  • Configurable workflows and source selection.

  • Deployment within Model ML-managed or customer-controlled Azure environments.

  • SSO, JIT provisioning, and MFA.

  • New-customer provisioning in as little as one hour (Azure article).

Onboarding runs as an enterprise process.

Packaging

The platform draws enterprise packaging distinctions: SSO identity provider support is referenced for Enterprise Plan customers (security page).

Frequently asked questions

Is Model ML a self-serve product?

No. Model ML is enterprise sales-led; prospects start with a demo, the contact form, or an API-doc request.

Is pricing public?

No. Pricing is scoped to each engagement and discussed through the sales process.

Are API docs public?

API documentation is available through a request flow. The public API page provides a form and a "View API docs" entry point.

Can Model ML run in our environment?

Yes. Model ML offers dedicated deployment options, single-tenant Azure environments, and deployment inside a customer's own cloud perimeter (security page).

Does Model ML use customer data to train models?

No. Customer data is not used for model training, product improvements, or any other purpose (security page).

What identity controls are supported?

SAML and OIDC SSO, JIT provisioning, domain claim enforcement, IDP-initiated and SP-initiated SSO, and MFA.

What outputs can Model ML create?

PowerPoint decks, Word outputs, Excel outputs, agentic dashboards (dynamic HTML visuals), investment memos, earnings summaries, company profiles, tearsheets, due diligence materials, and other structured deliverables.

Does Model ML support source traceability?

Yes. Outputs can include datapoint-level citations, and external web research is cited and filterable by approved source.

Is there public proof of customer adoption?

Yes. Model ML's clients include HSBC, Deloitte, PwC, EY, Moelis, BDO, Centerview Partners, GCM Grosvenor, FT Partners, CBPE, TresVista, Three Hills, AltamarCAM, LCap, and VSS. Published announcements and case studies cover i5 Invest, Intrepid Growth Partners, West Lane Capital Partners, GCM Grosvenor, and InterAlpen, plus homepage testimonials from finance leaders.

Bottom line

Model ML's public materials give buyers what they need to understand the product category, security posture, and likely fit. The buying motion is enterprise-led through demo and contact flows; commercial terms are scoped to each engagement.