Start free. Scale per seat.
The data quality foundation for your AI: free to begin, pay per seat when your team is ready, and full enterprise infrastructure when you need to run it in production.
Everything a small team needs to put a quality foundation under its first AI project.
Start free- 1 user
- 1 workspace
- 2 data sources
- Up to 25 rules
- AI rule suggestions
- Manual & scheduled validation
- Community support
- Anomaly detection, Ask AI, explainable failures
- MCP server
- SSO & RBAC
- Privacy Agent
The full AI quality platform for teams shipping real AI on real data.
Contact sales- Unlimited data sources & rules
- Full AI toolkit: AI rule suggestions, anomaly detection, Ask AI, explainable failures
- MCP server: connect your own LLMs & agents
- Unlimited connectors & lakehouse
- Rule groups, scheduling, alerts & incidents
- Catalog integrations (DataHub, Atlan, Alation, Data.world)
- Organization & teams, standard RBAC
- REST API + BI export (PowerBI, Tableau)
- Self-hosting / Privacy Agent
- SSO/SAML & advanced RBAC
- SLA, dedicated & chat support
Self-host, Agent, SSO, SLA. Run Measured as production infrastructure, your cloud or ours.
Contact sales- Everything in Team
- Self-host or dedicated managed cloud
- Privacy Agent: data & credentials never leave your network
- On-prem / bring-your-own LLM for all AI features
- SSO/SAML (Okta, Entra ID, Google) + 6-role RBAC
- Restricted PII/PHI/PCI assets & tenant isolation
- Audit log, SIEM export & SOC 2 on request
- 99.9% SLA, dedicated & chat support
Every paid plan ships the MCP server as part of the AI toolkit, so your own copilots and agents can read data quality, author rules, and triage failures directly. On Enterprise, run all of it against an on-prem or self-hosted LLM via the Privacy Agent, so nothing leaves your network.
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Every capability, side by side.
| Feature | Developer | Team | Enterprise |
|---|---|---|---|
| Users | 1 | Per seat | Unlimited / volume |
| Data sources | 2 | Unlimited | Unlimited |
| Rules | Up to 25 | Unlimited | Unlimited |
| Connectors (Snowflake, BigQuery, Oracle, …) | Core | Unlimited | Unlimited |
| AI rule suggestions | |||
| Anomaly detection | |||
| Ask AI + explainable failures | |||
| MCP server (your own LLMs/agents) | |||
| Bring-your-own / on-prem LLM | Cloud LLMs | On-prem via Privacy Agent | |
| Rule groups, scheduling, alerts, incidents | Basic | ||
| Catalog integrations | |||
| BI export (PowerBI / Tableau) | |||
| Self-hosting | |||
| Privacy Agent | |||
| SSO/SAML + 6-role RBAC | Standard RBAC | ||
| Restricted PII/PHI/PCI assets | |||
| Audit log & SIEM export | |||
| Support | Community | Dedicated + chat, SLA |
Questions, answered
What can I do on the Developer plan?
Connect up to 2 data sources, author up to 25 rules in one workspace, and run validations manually or on a schedule, free for a single developer. It includes AI rule suggestions, so you can stand up a quality check in front of your first AI project in minutes.
How does the Team plan work?
Team is billed per active seat. Every seat gets the full AI toolkit (AI rule suggestions, anomaly detection, Ask AI, explainable failures), the MCP server, unlimited connectors, scheduling, alerts, incidents, and catalog integrations. Contact sales to set your team up.
When do I need Enterprise?
When you need to run Measured as production infrastructure: self-hosting or a dedicated managed cloud, the Privacy Agent so data and credentials never leave your network, on-prem LLMs, SSO/SAML with 6-role RBAC, restricted PII/PHI/PCI assets, audit/SIEM, dedicated and chat support, and an SLA.
What is the MCP server?
Model Context Protocol is how your own LLMs and agents talk to Measured. With the MCP server (part of the AI toolkit), tools like Claude or your in-house copilots can query data-quality status, draft and edit rules, and investigate failures in natural language.
How does Measured relate to Great Expectations Core?
Measured runs on Great Expectations Core, the open-source data validation engine. Your existing checks work unchanged, and existing pipelines can point at Measured with a single environment variable. You get the open standard your team already trusts, plus AI, scheduling, RBAC, and observability, with no proprietary lock-in.
Does my data leave my network?
On Enterprise with the Privacy Agent, no. The agent sits between your data sources and the cloud, runs validation on-prem, and sends back only run metadata, never the data, credentials, or results. AI can run against an LLM you control.