TigerGraph Savanna
TigerGraph Savanna is TigerGraph’s fully managed cloud service, and the successor to TigerGraph Cloud Classic. Savanna runs the infrastructure, so there is nothing to install or maintain: sign in, get a graph database on the latest 4.x engine, then design a schema, load data, write GSQL, and explore results from the browser or over the API.
What sets Savanna apart from Enterprise Server and Cloud Classic is the architecture. Storage and compute are separate, so each scales on its own and you pay for the capacity a workload actually needs. See how the offerings compare.
Every path below gets you to a graph you can query. Choose how you want to work. Build and manage your graph in Savanna, connect from Python with pyTigerGraph, or connect AI tools with MCP and work in natural language.
Get started
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Create and manage graphs, define and edit schemas, load data, and run GSQL queries directly in Savanna. |
Call your Savanna workspace from Python to run queries and work with graph data. |
Use natural language in Cursor, VS Code, Claude Code, or Claude Desktop to create and manage graphs, edit schemas, load data, and run GSQL queries in Savanna. |
Common tasks
Start with the core graph workflow. Model your domain, load data, query it, and then connect applications or automation.
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Design a schema to define the vertices, edges, and attributes that represent your data.
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Load data from local files or connect to S3, GCS, Azure Blob, Snowflake, JDBC, and other sources.
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Write and run GSQL to create, install, and run graph queries in a live workspace.
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Connect with pyTigerGraph to call the workspace from Python with a database secret.
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Query the graph from code with generated curl, Python, or JavaScript examples for the graph data plane.
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Manage Savanna through the REST API to provision workgroups, workspaces, and organization resources.
Where to go next
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Build covers schemas, loading data, writing GSQL, and exploring graph results.
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Connect with pyTigerGraph when your application calls the database from Python.
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Connect AI tools with MCP when you want an AI agent to work against your database.
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Connect via APIs for generated curl, Python, or JavaScript against the graph data plane.
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Workgroups and workspaces handles workgroups, workspaces, access, networking, and routine administration.