Definite

What it is

Definite is an AI-native data platform. It is not a dashboard tool that sits on top of a stack you assemble yourself; the company's pitch is that ingestion, storage, transformation, dashboards and an AI analyst live in one product with one login and one bill. The homepage headline is "From zero to AI analytics by Monday," and the supporting line reads "Centralize your data, ask questions in plain English, and ship full data apps." It is written for teams that want BI without wiring up an ETL tool, a warehouse and a transformation layer first. The solutions menu names founders and CEOs, finance, RevOps, marketing, product, customer success, startups and enterprise, and offers a Data Team as a Service option.

What you build and how

The homepage lays the platform out as a set of layers, and what you build is a data stack plus the apps on top of it.

Ingestion. "500+ native connectors. If it has an API, Definite already speaks to it." The integrations block groups them in 3 sets. Customer and sales systems include Salesforce, HubSpot, Pipedrive, Attio, Intercom, Zendesk, Freshdesk, Slack, Gong, Salesloft, Linear, Asana, ClickUp, Jira, GitHub, Workday and Okta. Revenue, billing and marketing sources include Stripe, Chargebee, NetSuite, QuickBooks, Xero, PayPal, Shopify, WooCommerce, Square, Google Ads, Facebook Ads, LinkedIn Ads, TikTok Ads, Klaviyo, Marketo, Mailchimp and SendGrid. Databases, warehouses and analytics tools include Postgres, MySQL, SQL Server, MongoDB, Snowflake, BigQuery, Redshift, DuckDB, Amazon S3, Google Sheets, a REST API, GA4, Mixpanel, Amplitude, Segment, PostHog and Datadog. The architecture list mentions "native CDC," change data capture, for keeping synced tables current.

Storage. A "data lake" described as "columnar, cheap, fast," shown on the page as a "Live Lakehouse." You do not provision a separate warehouse; the company's blog says the lakehouse is built on DuckDB and DuckLake.

Transformations. "SQL + versioned models." The logic layer is SQL you write or have the AI write, and the models are versioned.

Semantic layer. "Metrics defined once, used everywhere." You define a metric such as revenue one time and every dashboard, app and agent uses that definition.

Apps and dashboards. This is the part closest to a visual editor. The homepage shows "custom viz, fully custom to the use case," including an interactive revenue map with drill-down. A second example shows component-level editing: "Edit one chart with the agent. Leave the rest alone." You ask the AI to change one chart in a dashboard and the rest stays as it was, which avoids the usual risk of a prompt rewriting a whole page.

Fi, the AI analyst. "Ask a question. Get a data app. Not a dashboard. Not a chart. A full application: sidebar, filters, KPIs, tables, and an inspector that explains every number." Fi "writes queries, builds charts," and the answers are meant to be kept as apps, not read once.

Agents and MCP. "Definite speaks MCP natively. Point Claude, Cursor, or any agent at your warehouse and they'll get schema, lineage, and semantic context automatically. No glue code, no scraping, no guesswork." The page marks Claude, ChatGPT and Cursor as ready, shows a custom agent via an SDK, and says "role-based access control permissions travel with the data."

Deployment. The homepage says "Definite runs as a private deployment in your own cloud (AWS, GCP, Azure, or anywhere with Kubernetes); you keep control of data, compute, and networking." The company's blog adds that Definite "self-hosts the full stack, including the Fi AI analyst, via a single Helm chart, with AI calls routed to a model endpoint you control." For an IT team, that means the whole platform, AI analyst included, can run inside its own infrastructure.

A reader who arrives from the dashboards category should know the dashboards are a part of this product, not the whole of it. If you only want charts over a database you already run, this is more platform than you need.

Plans, pricing and claims

The homepage headline is "Pay for answers. Not seats, rows, or cores." Pricing is credit-based: "You pay for queries run, connectors used, and agent time," and nothing else, and it "Starts free; scales with you." The homepage lists 3 plans.

• Free: $0, with 2 users, 2 connectors and the Fi AI assistant.
• Standard: $250 a month, with unlimited users, 500+ connectors and API access.
• Enterprise: custom pricing, with SSO, SOC 2, dedicated support and an SLA.

A comparison post on the company's blog uses a different label for the entry plan, "Free (Starter)," and describes Standard as "one platform that replaces your ETL, warehouse, and BI tool in a single bill." SOC 2 is listed as an Enterprise plan feature.

The homepage frames the cost case with a before-and-after picture: a "patchwork stack" of separate ingestion, warehouse, transformation and BI contracts at "~$182k/yr · 5 contracts · 3 engineers" with a timeline of "T+ 6 months," against an "after" column that reads "Starts free, 1 contract, 0 extra hires." These are the company's illustrative numbers, not a customer result. Its blog comparisons are vendor-written too, and one says so: "Definite is our product."

That same blog is candid about a limit: "Definite prioritizes speed-to-answer over chart customization," so a team whose main output is pixel-perfect charts for board decks gets less control over each chart here. Support is described as "dedicated support & SLA" on the Enterprise plan, and the page offers a demo video and a sign-up flow.

Who it suits and who it does not

Definite suits a startup or mid-size company that is about to buy a connector tool, a warehouse and a BI tool separately and would rather start with one product. A founder or operations lead can connect Stripe, HubSpot and a Postgres database, define metrics once, and ask Fi questions in English. It also suits a team already using Claude or Cursor that wants those agents to read governed, defined metrics instead of raw tables. An IT team with a rule that data stays in its own AWS, GCP or Azure account gets a private deployment option from the same vendor.

It is a weaker fit if the main deliverable is a polished, pixel-controlled presentation chart, which the company says outright. It is also more than a small team needs if the data already sits in a single database and a simple chart tool would do. And because the pricing is credit-based, a heavy-query team should model usage on the Standard plan's $250 a month before assuming it stays flat.