Glean vs Grapevine

Glean is an enterprise AI platform that connects an organization's knowledge across more than 35 applications, including Slack, Google Drive, Jira, Confluence, SharePoint, GitHub, and Salesforce, to power permission-aware search and answers. Beyond search, Glean lets teams build custom AI agents without code on top of that connected knowledge layer, positioning itself as a horizontal workplace productivity platform rather than a single point tool.

Grapevine is an open-source, self-hostable unified knowledge store designed to ingest information from a company's scattered tools and turn it into one searchable index. It connects to popular services like Slack, Notion, Linear, GitHub, Google Drive, HubSpot, Salesforce, and more, then uses AI embeddings to enable semantic search across all of them. The platform is built around an MCP (Model Context Protocol) server, which lets AI assistants such as Claude query and retrieve company knowledge directly. Beyond search, Grapevine provides an optional Slack bot for AI-powered question answering and a web-based admin UI for configuring data sources and onboarding organizations. Its architecture includes core services like an ingest gatekeeper, tenant steward, and background workers that process webhook data, generate embeddings, and maintain search indices. This makes it a practical choice for teams that want a private, self-hosted knowledge layer rather than relying on a closed SaaS product. Because Grapevine is self-hostable via Docker Compose or Kubernetes, it appeals to developers and enterprises that need control over their data and infrastructure. It supports a wide range of connectors across communication, project management, documentation, code, CRM, design, analytics, meetings, and support. However, it is not a plug-and-play product: it requires PostgreSQL with pgvector, OpenSearch, Redis, WorkOS, and an OpenAI API key, and the repository is provided as-is without active maintenance.

• Connects a very wide range of enterprise apps rather than a narrow set
• Permission-aware search reduces the risk of surfacing content a user shouldn't see
• No-code agent builder extends beyond search into workflow automation
• Strong compliance certifications suitable for regulated enterprises

• Unified semantic search across 25+ business tools from one self-hosted platform.
• MCP server makes company knowledge accessible to AI assistants like Claude.
• Open source under MIT, with Docker Compose and Kubernetes deployment options.
• Optional Slack bot provides natural language Q&A directly in Slack.

• No public pricing; requires a sales demo to get a quote
• Built for large organizations with many connected systems, likely overkill for small teams
• Value depends heavily on how much of the org's knowledge actually lives in supported integrations

• Repository is provided as-is without active maintenance, so updates and support are limited.
• Requires substantial infrastructure: PostgreSQL, OpenSearch, Redis, WorkOS, and an OpenAI API key.
• Setup complexity is high for non-technical teams due to Docker, environment variables, and multiple services.
• No hosted or managed version is mentioned, so users must handle deployment and scaling themselves.