SayQL vs DataGrip
Updated
DataGrip is JetBrains' database IDE, with AI Assistant, coding agents and an MCP server built in. SayQL is a desktop SQL client focused on governing what AI sees and does on your databases.
Choose DataGrip for deep SQL editing, refactoring and schema comparison. Choose SayQL to decide per connection what AI sees of results, keep chosen columns from it entirely, and keep every AI query read-only.
Checked on 3 November 2026 against each product's own docs, code and pricing; every cell links to its source. Spotted something out of date? Email hello@azenvoc.com and we'll fix it.
Side by side
| SayQL | DataGrip | |
|---|---|---|
| What it is | Desktop SQL client (macOS, Windows, Linux) with AI, plus an optional self-hosted team server source | A database IDE by JetBrains, with JetBrains AI Assistant and coding agents built in source |
| Status | Maintained; 0.6.0 released 2026-11-03 source | Maintained; 2026.2.6.1 released 30 October 2026 source |
| Licence | App: open source, Apache 2.0. Team server: paid licence source | Proprietary. Free for non-commercial use (with anonymous usage statistics); paid for commercial use source |
| Price | App free. Team server $149 a month for up to 10 people, 30-day trial source | Organisations $259 per user a year; individuals from $109 a year. AI: a small free quota, AI Pro $100 a year, higher tiers above source |
| Databases | PostgreSQL, MySQL, MariaDB, SQLite, SQL Server, Azure SQL source | PostgreSQL, MySQL, MariaDB, Oracle, SQL Server, SQLite, Snowflake, BigQuery, Redshift, ClickHouse and more, plus MongoDB, Redis, Cassandra and DynamoDB source |
| AI models | Claude, OpenAI, Gemini, OpenRouter, Mistral, Groq, DeepSeek, any OpenAI-compatible API; local models with Ollama or LM Studio source | JetBrains AI (cloud models), your own key (Anthropic, OpenAI, Gemini, OpenAI-compatible), or local models in Ollama or LM Studio (not for agents) source |
| Plain English to SQL | Yes: SQL appears in the editor as you type, from your schema and approved notes source | Yes: generate SQL with AI in the editor, and a text-to-SQL agent skill that reads the real schema source |
| Multi-step questions | Yes (beta): plans, runs read-only steps, checks its work, cites each query source | Yes: agent mode and coding agents (Junie, Claude Agent, Codex and others) can run queries when allowed source |
| MCP server for AI agents | Yes: a local MCP server for Claude Code, Cursor and others, and an HTTP gateway on the team server source | Yes: a built-in MCP server (on by default since 2025.2) with database tools including running SQL; it can also connect to MCP servers source |
| What the AI sees of your data | Schema by default. Per connection you choose what AI sees of results: nothing, counts and summaries (default), masked rows, or all rows source | Schema (with consent) and query results: agents get a sample of rows, and more on request. Four global switches allow reading or changing data and schemas source |
| Keep chosen columns from the AI | Yes: columns marked never send are refused in any form and withheld from results source | Not documented: the AI switches apply to all databases, with no column-level setting source |
| Read-only for AI | Read-only by default: statements are checked before they run and run in a read-only transaction; AI apps can't change data source | A read-only option per data source, which may not stop changes from a console if the driver lacks read-only support; the MCP docs recommend a read-only database user source |
| Record of AI queries | Yes: a local log of what each AI app ran; on the team server, one log of every query from people and agents, whose rows can't be edited source | Not documented in DataGrip itself; AI Enterprise lists organisation-level AI audit logs source |
| Per-agent or per-user rules | Block or allow each AI app. Team server: each agent its own key, databases, tables and rows source | Organisations can choose which agents and AI providers people use and cap spending; per-agent database rules not documented source |
| Where it runs, where data goes | Runs on your computer; the team server runs on your servers and your data never reaches us source | Desktop app; cloud AI goes through JetBrains' AI service by default. AI Enterprise offers on-premises models and zero data retention source |
| Interface | Desktop app source | Desktop app (Windows, macOS, Linux) source |
| Learns about your database | Shared notes about the database that people approve and SayQL keeps true as the schema changes; no training on question–SQL pairs source | Not documented source |
What DataGrip does better
- SQL refactoring: rename objects with their usages, extract subqueries to CTEs, and more. source
- Schema comparison and migration scripts between databases. source
- Schema-aware code completion and inspections with quick fixes across many SQL dialects. source
- Far more databases, including NoSQL stores and cloud warehouses. source
- Git and local history for SQL scripts, and a mature data editor with many import and export formats. source
How SayQL handles AI access
- Counts, not rows, by default. For each connection you choose what AI apps see of results: nothing, counts and summaries, masked rows or all rows. See what AI apps may see.
- Columns that never reach the AI. Mark a column never send and any query that could read it is refused, and it's withheld from results. See never-send columns.
- Read-only, enforced. Every statement is checked before it runs and runs in a read-only transaction; AI apps can't change data. See how queries run safely.
- A record of what AI ran. See every query each AI app ran, and block an app in one click. On the team server, one log covers every person and agent. See agent activity.
- Per-agent keys on your own server. The self-hosted team server gives each agent its own key, databases, tables and rows. See SayQL for teams.
Questions
Does DataGrip send query results to the AI?
Schema (with consent) and query results: agents get a sample of rows, and more on request. Four global switches allow reading or changing data and schemas. source In SayQL: schema by default. Per connection you choose what AI sees of results: nothing, counts and summaries (default), masked rows, or all rows. source
Can DataGrip keep a column away from the AI?
Not documented: the AI switches apply to all databases, with no column-level setting. source In SayQL, yes: columns marked never send are refused in any form and withheld from results. source
Is DataGrip read-only for AI?
A read-only option per data source, which may not stop changes from a console if the driver lacks read-only support; the MCP docs recommend a read-only database user. source In SayQL: read-only by default: statements are checked before they run and run in a read-only transaction; AI apps can't change data. source
Does DataGrip have an MCP server for AI agents?
Yes: a built-in MCP server (on by default since 2025.2) with database tools including running SQL; it can also connect to MCP servers. source In SayQL, yes: a local MCP server for Claude Code, Cursor and others, and an HTTP gateway on the team server. source
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Download SayQL: the app is free and open source.
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