A companion to DataGrip for database work done with your AI agent.
DataGrip is the stronger SQL IDE for hand-written queries, refactoring, and many database engines. Keep it for that. Valv does a different job next to it. You ask a question in plain language. Your own AI agent finds the permitted schema, runs read-only queries, cites them in the answer, and can turn the result into a live dashboard.
Not a replacement for DataGrip. A place for questions you would rather ask than write as SQL.
Use each tool for the job it does best.
These tools do different jobs. The split turns on who writes the query and what should happen after the result comes back.
Use DataGrip if you live in a SQL IDE.
DataGrip has the broader engine list and the deeper editor. It covers context-aware completion, refactoring, code inspections, a query console, a data editor, and version control, plus an optional AI Assistant add-on.
- You manage database engines that Valv does not support.
- You write heavy SQL, refactor schemas, and rely on code inspections.
- The query console is the center of the job.
Check the official DataGrip feature overview.
Use Valv when the agent writes the query.
Valv puts the coding agent inside the database workspace. The agent gets read-only database tools, the app records its tool calls and query results, and useful outputs can become files that survive the chat.
- You ask questions in plain language more often than you start with SQL.
- You need table and column controls for agent access.
- You want live dashboards, flows, exports, and shared definitions in one workspace.
Valv has a read-only table viewer, not a SQL editor. Keep DataGrip for writing SQL.
The same question takes a different path.
DataGrip gives you strong tools to write and run the statement. Valv makes the agent's database work inspectable and reusable.
A DataGrip workflow
- 1
Open the data source and find the relevant schema.
- 2
Write SQL with context-aware completion and code inspections.
- 3
Run it in the query console and read the result grid.
- 4
Refactor, edit data, export, or version the SQL in your repo.
An agent-led Valv workflow
- 1
Ask a question in the workspace chat.
- 2
The agent discovers only the tables and columns allowed for AI.
- 3
Valv validates and runs a structured, read-only query from your machine.
- 4
Inspect the tool call and result, then save a live dashboard or export a CSV when useful.
Valv adds control around the agent, not another chat box.
The important difference appears when the model needs live data. Valv owns that boundary and shows what crossed it.
Read-only database tools
The agent can list resources, inspect the allowed schema, and send structured analytics queries. It cannot call database insert, update, delete, or raw SQL tools.
Table and column controls for AI
Active schemas start visible. Hide a whole table or individual fields before the agent discovers them. The table view marks those columns "Hidden from AI". You still see them there. The agent does not.
AI access
Tool calls and results you can inspect
Valv renders tool calls where they happen in the conversation. A factual answer can link back to the structured query and result behind it instead of leaving you with an unexplained paragraph.
Source: cancellation totals
Files and context that outlive the answer
Save live HTML dashboards, flows, and CSV exports in the workspace folder. In a git-backed workspace, choose which files sync to the team. Confirmed definitions become shared workspace memory.
Both keep the database connection on your device. Chat changes the data path.
DataGrip stores connection details on your machine and runs queries locally. Its AI Assistant is a separate add-on you opt into. That local-first design is a genuine strength, not a gap Valv can claim as its own.
Valv also connects to the database from your machine and encrypts database secrets with the OS keychain. When you use its optional AI chat, the allowed schema, your prompt, and the query results needed for the answer are processed through your chosen agent provider. The database credentials are not.
Source: DataGrip product pages.
What leaves the database client?
DataGrip database work
Connections and queries run locally. The AI Assistant add-on sends context to a model provider only when you enable and use it.
Valv database work with optional chat
Valv runs the query locally. The chosen model provider processes the prompt, permitted schema, and returned data needed to answer. Hide fields before asking if they should not enter that context.
Subscription versus a one-time license.
DataGrip is billed per user per year. Valv sells a one-time seat and is free for personal use. The license terms are the real difference.
DataGrip
Annual per-user subscription
Sold standalone or in the JetBrains All Products Pack. Individual plans cost less than commercial ones. JetBrains also offers free non-commercial licenses for individuals.
See current DataGrip pricingValv Desktop
$99 Pro seat during beta
One-time commercial license with unlimited devices per seat. Personal, non-commercial use is free with every feature included.
See Valv pricingDataGrip alternative questions, answered
Is Valv a drop-in replacement for DataGrip?
No. DataGrip is the stronger choice if you live in a SQL IDE and spend most of your time writing hand-written SQL, refactoring, and managing many database engines. Keep DataGrip for that. Valv covers a different job. You ask questions in plain language, and your own AI agent answers through read-only tools, cites the queries behind each number, and can build a live dashboard. You can run both side by side.
Which databases do DataGrip and Valv support?
DataGrip supports a much longer list, including PostgreSQL, MySQL, SQL Server, Oracle, and many more engines. Valv has stable integrations for PostgreSQL, MySQL and MariaDB, SQLite, and ClickHouse, with MongoDB marked experimental. If your work spans engines Valv does not cover, DataGrip is the safer pick.
Can I still browse tables and write SQL in Valv?
You can browse tables. Valv has a read-only table viewer where you filter, sort, page through rows, and export CSV. There is no SQL editor and no row editing. Keep DataGrip for writing SQL, with its context-aware completion, refactoring, and code inspections. Valv is for the questions your agent answers.
Can a Valv agent change my database?
No. Valv gives agents policy-filtered discovery and structured query tools. The agent never gets insert, update, or delete tools and never runs raw SQL. Every database request goes through Valv's policy layer. Tables in Valv are read-only for you too.
What can the agent see in Valv?
The schemas active in the workspace start visible. You can hide a whole table or individual columns such as email and card number in AI access. Hidden fields stay out of agent discovery and queries. The table view marks them "Hidden from AI", so you can still see them there. The agent cannot.
How do DataGrip and Valv handle privacy?
Both connect to databases from the user's device and keep credentials there. DataGrip stores connection details locally, and its AI Assistant is a separate add-on you opt into. Valv also runs database queries from your machine, but optional chat sends your prompt, the allowed schema, and the query results needed for the answer through the coding-agent account you selected. Valv does not send database credentials to that provider.
How does DataGrip pricing compare with Valv?
DataGrip is a per-user annual subscription, sold standalone or in the JetBrains All Products Pack, with individual plans cheaper than commercial ones. JetBrains also offers free non-commercial licenses for individuals. Valv includes every feature free for personal, non-commercial use. During the beta, commercial Pro is a one-time $99 per seat with unlimited devices for that seat. Check the DataGrip buy page for current subscription prices.
Use Valv next to DataGrip.
Free for personal use, every feature included. Commercial use needs a seat at beta pricing. See pricing.