DataHub

DataHub brings technical metadata, business knowledge and documentation together as a context platform for enterprise data and AI agents. Its discovery tools in DataHub Cloud include natural-language search, an Ask DataHub chat agent, smart ranking and a hosted MCP server for connecting AI tools to its catalog. The platform can automate schema, freshness, volume and custom quality checks, with AI anomaly detection and incident workflows. Cross-platform, column-level lineage follows data from sources through transformations and AI models to downstream assets. DataHub Cloud has more than 100 pre-built connectors, with native integrations including Slack, Microsoft Teams, Chrome and BI tools. Deployment options include self-hosted DataHub Core and managed DataHub Cloud. Core is free to deploy, but users handle installation, configuration, upgrades, uptime and troubleshooting. Cloud pricing depends on data volume, users and selected capabilities. Cloud includes onboarding, adoption support, a dedicated customer success team and private Slack support; Core users get community Slack and self-service documentation. DataHub Cloud is described as SOC 2 compliant, with 99.5% SLA-backed availability.

DataHub combines catalog discovery, quality checks and lineage, with both self-hosted and managed deployment options. The choice hinges on whether a team wants to operate Core itself or use Cloud with scoped pricing and managed support.

DataHub suits enterprise teams organizing metadata and lineage across data systems, as well as teams connecting AI tools to a catalog. Core may fit organizations prepared to manage a self-hosted deployment; Cloud is a managed option.

DataHub is a metadata and context platform for enterprise data teams that need to connect assets with business knowledge and documentation. It is best for organisations that can either run an open-source system themselves or justify a managed service. Its breadth spans discovery, quality checks and lineage, but Cloud’s custom pricing and Core’s operational workload make deployment choice central.

Overview

DataHub brings technical metadata, business context and documentation into one platform, with metadata discovery, a business glossary and lineage analysis. That shared context can help teams find assets and understand their relationships across data and AI systems.

Core is open source and self-hosted; Cloud is managed enterprise SaaS. The founders developed the platform from metadata work at LinkedIn and Airbnb, and the company is headquartered in Palo Alto, California. DataHub is available through an API, on Linux, as self-hosted software and on the web. See Metadata Management Software.

Key features

Discovery and AI connections

DataHub Cloud combines natural-language search, smart ranking and an Ask DataHub chat agent with a hosted MCP server that connects AI tools to the catalog. More than 100 pre-built connectors and native integrations such as Slack, Microsoft Teams, Chrome and BI tools can bring catalog context into existing workflows. MCP-native connections include Cortex, Genie, Cursor, Claude, LangChain, Agent Development Kit, CrewAI and custom agents. This is a strong fit for teams that want catalog context accessible from collaboration and AI tools; the discovery and AI features described are Cloud capabilities, not a reason to assume the same experience in Core.

Observability and lineage

Automated checks cover schema, freshness, volume and custom quality rules, with AI anomaly detection and incident workflows for managing issues. Column-level lineage follows data from sources through transformations and AI models to downstream assets. Together, these capabilities suit teams that need to investigate where data comes from and monitor its condition, rather than maintain a catalog alone.

Security and service

Cloud is described as SOC 2 compliant, with role- and attribute-based access controls and an in-VPC remote execution option for sensitive sources. DataHub says it encrypts customer data at rest and in transit, and conducts third-party penetration tests and static security analysis. Cloud also has SLA-backed 99.5% availability. Core has basic access controls and no SSO or fine-grained permissions out of the box, a meaningful constraint for teams with stricter access requirements. Cloud adds onboarding, adoption support, a dedicated customer success team and private Slack support; Core users get community Slack and self-service documentation.

Pricing

DataHub Core — free

Core is free to deploy and open source. It fits teams willing to manage installation, configuration, upgrades, uptime and troubleshooting themselves. The trade-off is operational responsibility, basic access controls and community-level support rather than Cloud’s managed service and enterprise controls.

DataHub Cloud — custom pricing

Cloud is managed enterprise SaaS, with pricing scoped to data volume, users and selected capabilities. It suits teams that value managed operations, stronger access controls and dedicated support, but the cost depends on the use case rather than a fixed published rate. A Google Cloud offer provides a 21-day trial with a dedicated instance and full platform access.

Platforms

DataHub supports API access, Linux, self-hosted deployment and web use. Core is the self-hosted option; Cloud is the managed option.

Who it's for

DataHub is a good fit for enterprise data teams that need one place to connect metadata, business meaning, quality monitoring and lineage, especially when AI tools need access to catalog context. Choose Core if the team can operate the platform and its access-control limits are acceptable. Choose Cloud if managed availability, enterprise access controls and dedicated support matter enough to warrant custom pricing. Teams seeking fixed, predictable pricing or a low-maintenance free deployment should weigh those trade-offs carefully.

Pros and cons

• Pros: Discovery, glossary, observability and column-level lineage cover complementary catalog needs in one platform.
• Pros: Cloud’s connectors and MCP integrations can connect catalog context with collaboration, BI and AI tools.
• Pros: Cloud combines managed operations, 99.5% SLA-backed availability, enterprise access controls and dedicated support.
• Cons: Core requires the team to handle deployment, upgrades, uptime and troubleshooting.
• Cons: Core lacks SSO and fine-grained permissions out of the box.
• Cons: Cloud pricing varies by data volume, users and capabilities, so it is not a fixed-price choice.

Alternatives

Aurelius Atlas is another free, open-source self-hosted option; consider it when avoiding licensing costs is the priority and optional consulting services suit the team.

Apache Atlas is a free metadata-governance option for teams seeking open software without a stated paid tier or usage limits.

OpenMetadata is worth considering for a self-hosted deployment when the team wants open-source software with no usage limits stated on its page.

Ab Initio Data Platform may suit teams willing to complete a free proof of concept before purchase; its price is custom.

Alation Data Intelligence Platform is an alternative for teams considering AI capabilities through a pool of Alation Consumption Units sized with Alation.

Alex Solutions is another alternative.

Aristotle Metadata Registry is another alternative.

Atlan Lakehouse is another alternative.

Verdict

Choose DataHub when an enterprise data team needs discovery, business context, quality monitoring and lineage together, and can match its deployment model to its operating capacity. Core’s zero-cost entry point is compelling for teams prepared to run it, while Cloud is the stronger fit when managed service, access controls and support take priority. Look elsewhere if a fixed Cloud price or out-of-the-box SSO in the free self-hosted tier is essential.