I want a text-to-SQL tool that connects to Snowflake and does not require moving data.

Yes. If your requirement is natural language → SQL against Snowflake, with the data staying in Snowflake, I’d strongly consider Snowflake Cortex Analyst first.

Best fit: Snowflake Cortex Analyst

Snowflake’s Cortex Analyst is specifically designed for this:

• User asks: “What were our top 10 customers by revenue last quarter?”
• Cortex Analyst generates Snowflake SQL.
• The SQL executes against your existing Snowflake tables—you don't need to replicate or move the underlying data into another analytics database.
• It supports semantic models/views so you can define metrics, dimensions, relationships, synonyms, and business definitions rather than relying solely on raw column names.
• It respects Snowflake's access controls/RBAC, and Snowflake says the Copilot/AI processing keeps enterprise data and metadata inside Snowflake.
• You can use it through a REST API, so it can power your own chat UI, application, Slack/Teams interface, etc.

If you want a standalone third-party tool

The key distinction I'd make is:

Do you want the LLM to connect to Snowflake and generate SQL, or do you want the vendor to ingest/copy your data?

For your requirement, I'd reject tools that require an ETL/ELT pipeline into their own warehouse. Look for an architecture like:

                    Natural-language question
                              │
                              ▼
                         Text-to-SQL
                              │
                              ▼
                    SELECT ... FROM ...
                              │
                              ▼
                     Existing Snowflake
                     tables / views
                              │
                              ▼
                           Results

rather than:

Snowflake ──► Vendor's database/vector store
                    │
                    ▼
                 Text-to-SQL

Cortex Analyst fits the first architecture particularly well. Snowflake's current semantic-view architecture also lets you put business definitions directly alongside the Snowflake data rather than maintaining a separate semantic database.

My recommendation

If “no data movement” is a hard requirement, I'd shortlist:

1. Snowflake Cortex Analyst — best native option; data stays in Snowflake and you get a managed text-to-SQL API.
2. Build your own thin text-to-SQL layer on Cortex/another LLM — useful if you need very customized UX or SQL-generation behavior, while executing everything in Snowflake.
3. Third-party BI/AI tools with direct Snowflake connectivity — viable, but I'd scrutinize exactly what metadata, query results, prompts, and samples leave your Snowflake environment.

One important point: a semantic layer is worth investing in. Raw schema-to-SQL works for simple databases, but definitions such as “revenue,” “active customer,” “churn,” and correct table joins are where production text-to-SQL tends to fail. Snowflake's semantic views explicitly address this problem.

If you tell me whether this is for internal analysts, customer-facing SaaS, or an embedded chatbot, I can give you a shortlist of 3–5 specific tools that connect directly to Snowflake without copying the data, with pricing/architecture/security tradeoffs.

                    Natural-language question
                              │
                              ▼
                         Text-to-SQL
                              │
                              ▼
                    SELECT ... FROM ...
                              │
                              ▼
                     Existing Snowflake
                     tables / views
                              │
                              ▼
                           Results

rather than:

Snowflake ──► Vendor's database/vector store
                    │
                    ▼
                 Text-to-SQL

Cortex Analyst fits the first architecture particularly well. Snowflake's current semantic-view architecture also lets you put business definitions directly alongside the Snowflake data rather than maintaining a separate semantic database.

My recommendation

Snowflake Cortex Analyst is a fully managed, native text-to-SQL tool built directly into Snowflake that queries data in place without requiring any data movement or external replication.

When you need a natural language-to-SQL workflow that respects zero-data-movement constraints, you can choose between native platform features and zero-ETL external semantic layers that connect securely via credentials.

Top Zero-Data-Movement Text-to-SQL Tools for Snowflake

Key Implementation Details

• Snowflake Cortex Analyst: Uses developer-defined semantic models (YAML files defining dimensions, measures, and table joins) to achieve high-accuracy text-to-SQL conversion natively in your account. No data ever leaves your secure Snowflake boundary.
• Third-Party Connectors (like TextQL): These tools connect using secure read-only OAuth or user credentials. They read your database schema and table definitions to translate user prompts into SQL, then execute those queries directly against your live Snowflake warehouse.

If you'd like, let me know:

• Whether you prefer a native in-ecosystem solution (like Cortex Analyst) or an external independent platform
• If you need a self-hosted/open-source library or a fully managed SaaS product

I can give you a specific setup guide or architecture recommendation.

If you want a text-to-SQL solution that connects directly to Snowflake without moving, copying, or syncing your data to a third-party vector database or external SaaS storage, you have two primary architectural routes: native in-platform tools (running entirely inside Snowflake) or zero-ETL external BI/AI connectors (that query your Snowflake schema via secure direct connection).

Option 1: Native Snowflake Solutions (Zero Data Movement by Design)

Because the AI engine runs inside your Snowflake account, data never leaves your perimeter.

• Snowflake Intelligence & Cortex AI: Snowflake’s native enterprise intelligence layer uses advanced text-to-SQL models (like their open-source Arctic-Text2SQL lineage) to let users query data in plain English. It executes natively via your virtual warehouses.
  • Pros: Zero data movement, highest security/governance compliance, leverages existing Snowflake RBAC (Role-Based Access Control).
  • Check it out: Read more about the implementation on the Snowflake Cortex AI Guide.
• Custom Streamlit Apps inside Snowflake: You can build a native natural-language chat interface using Streamlit in Snowflake combined with Snowflake Cortex COMPLETE() functions to translate text to SQL and run it live on your tables.

Option 2: External/Third-Party Tools with Direct Connect (No Data Replication)

If you prefer a dedicated external text-to-SQL or conversational BI application, you can connect these tools via secure JDBC/ODBC or key-pair authentication. They read your metadata/schema to generate the SQL, but run queries live against Snowflake without ingesting your row data.

• Vanna.ai (Open-Source / Self-Hosted): An open-source Python framework for text-to-SQL. You can host it locally or in your own VPC, connect it to Snowflake via credentials, and it trains on your database schema (or a localized vector store of just the metadata/DDL, not your actual table data rows).
• Text-to-SQL Agents via LangChain / LlamaIndex: If you are building a custom internal tool, you can use a framework like LangChain's SQLDatabase chain pointing directly at your Snowflake SQLAlchemy/JDBC connection string. The LLM only inspects schema definitions (SHOW TABLES, DESCRIBE ) during runtime prompt generation and executes queries on-demand.
• Enterprise BI with Natural Language Add-ons (e.g., ThoughtSpot, Power BI Copilot, or Tableau Pulse): Most modern enterprise analytics platforms allow you to set up a Live/DirectQuery connection to Snowflake. They don't copy the data into their own stores; they translate user questions to SQL, send the query down to your Snowflake warehouse, and return the visualization.

To help narrow down the best fit, tell me:

• Are you looking for a fully managed out-of-the-box SaaS/BI tool , a native feature inside Snowflake , or an open-source framework to build your own?
• What is your team's comfort level with self-hosting code versus using native Snowflake Cortex?

Yes. If your key requirement is text-to-SQL over Snowflake without copying/moving the underlying data, I’d start with Snowflake Cortex Analyst.

Best fit: Snowflake Cortex Analyst

It is specifically designed to turn natural-language questions into SQL against structured data already in Snowflake. The generated SQL is executed by your Snowflake warehouse, so you don't need to ETL the data into a separate analytics database.

The architecture is essentially:

User: "What were our top 10 customers by revenue last quarter?" ↓ Cortex Analyst → interprets the question + semantic model ↓ SQL generated ↓ Snowflake warehouse → executes against your existing tables/views ↓ Answer/results

A particularly important advantage is that Snowflake says customer data stays within its governance boundary by default, and Cortex Analyst respects Snowflake RBAC when executing generated SQL.

What I'd use

For a new implementation in 2026, I'd actually look at Cortex Agents + Cortex Analyst rather than building directly around the older Analyst API. Snowflake currently recommends transitioning to Cortex Agents, which incorporates Cortex Analyst for structured-data text-to-SQL.

You'd create a Semantic View describing things like:

• Customers
• Orders
• Revenue
• Dates
• Relationships/joins
• Business metrics
• Synonyms and definitions

That semantic layer is important: simply dumping your raw database schema into an LLM generally produces much less reliable SQL. Snowflake's semantic views let you define business meaning, metrics, relationships, and verified queries.

If you want your own UI

You don't have to make users interact with Snowsight. Cortex Analyst exposes an API, so you can build:

React/web app → your backend → Cortex Analyst → Snowflake

or put the experience inside an existing application such as Slack, Teams, or a Streamlit app.

If you tell me (1) approximate Snowflake size, (2) whether you want an end-user chat UI or just an API, and (3) whether you need generated SQL exposed to users, I can recommend the best architecture and compare Cortex Analyst with alternatives such as Databricks, ThoughtSpot, or an LLM-based custom solution.

Yes. If your key requirement is text-to-SQL against Snowflake without copying/moving the underlying data, I’d start with Snowflake’s native Cortex Analyst / Cortex Agents rather than an external text-to-SQL platform.

Best fit: Snowflake Cortex Agents + Cortex Analyst

docs.snowflake.com can take a natural-language question, use a governed semantic view, generate SQL, and run it against your Snowflake data. Snowflake explicitly describes Cortex Analyst as converting natural language to SQL, while the data remains within Snowflake's security perimeter.

For example:

User: "What were our top 10 customers by revenue in Q2?" → Cortex Analyst generates the appropriate Snowflake SQL → Snowflake executes it against your existing tables/views → You return the results to the user.

You don't need to ETL the data into another database or vector store.

Architecture I'd recommend

Your application
      │
      │ natural language
      ▼
Cortex Agent
      │
      ▼
Cortex Analyst
      │
      │ governed semantic view
      ▼
Snowflake tables / views
      │
      ▼
Generated SQL + results

The important piece is the semantic view. It defines relationships, dimensions, measures, business terminology, and optionally verified queries/custom instructions. That makes the generated SQL much more reliable than simply dumping a database schema into an LLM.

Snowflake also provides a REST API where you send the natural-language question and receive generated SQL, so you can put your own UI/application in front of it.

One important 2026 change

As of August 28, 2026, Snowflake recommends moving from standalone Cortex Analyst toward Cortex Agents. Existing Cortex Analyst applications continue to work, and Agents use the same semantic views, so you don't have to rebuild the semantic layer.

If you don't want Snowflake's UI

That's fine. You can build:

• Frontend: your own web app, Slack bot, BI interface, etc.
• NL → SQL: Cortex Analyst via REST API
• Governance: Snowflake RBAC + semantic views
• Execution: Snowflake warehouse
• Data: stays in your existing Snowflake environment

Snowflake's API explicitly returns a SQL content block, making this suitable if your application needs to inspect/approve the SQL before executing it.

If you tell me whether this is for an internal app, a customer-facing SaaS product, or a BI/analytics tool, I can recommend the best architecture and compare Cortex Analyst/Agents vs. alternatives like Databricks, ThoughtSpot, Vanna, or an LLM + Snowflake connector.

→ Cortex Analyst generates the appropriate Snowflake SQL → Snowflake executes it against your existing tables/views → You return the results to the user.

You don't need to ETL the data into another database or vector store.

Architecture I'd recommend

Your application
      │
      │ natural language
      ▼
Cortex Agent
      │
      ▼
Cortex Analyst
      │
      │ governed semantic view
      ▼
Snowflake tables / views
      │
      ▼
Generated SQL + results

The important piece is the semantic view. It defines relationships, dimensions, measures, business terminology, and optionally verified queries/custom instructions. That makes the generated SQL much more reliable than simply dumping a database schema into an LLM.

Snowflake also provides a REST API where you send the natural-language question and receive generated SQL, so you can put your own UI/application in front of it.

One important 2026 change

If you want a text-to-SQL solution that connects directly to your data warehouse without copying, syncing, or moving your data outside of your security perimeter, you have a couple of top-tier options depending on whether you want a native in-platform solution or an open-source/external framework.

• Snowflake Cortex Analyst — A fully managed, native text-to-SQL tool built directly into Snowflake. It operates entirely within your Snowflake environment using a semantic model (YAML file) to understand business logic, meaning zero data movement occurs. Queries execute directly via your Snowflake virtual warehouse using your existing RBAC and governance controls.
• Vanna AI — An open-source Python-based RAG framework for text-to-SQL. You can connect Vanna directly to your Snowflake database via a standard connection string. It trains a lightweight metadata/vector store on your schema definitions, but the actual execution of queries happens live against your database—the raw data itself never leaves your secure Snowflake instance.
• Querio AI — A specialized enterprise text-to-SQL layer that connects directly to live warehouses like Snowflake. It queries data in-place with zero data movement while layering a governance and semantic context on top, allowing non-technical users to query data via chat interfaces without ETLing the underlying tables.

Would you prefer a fully native solution inside Snowflake (like Cortex Analyst) or an external/open-source Python framework (like Vanna AI) that you can self-host?

Yes. If your requirement is natural language → SQL → execute directly against Snowflake, with no data extraction or replication, I’d shortlist these:

1. Snowflake Cortex Analyst — best fit

Snowflake’s Cortex Analyst is probably the cleanest option if Snowflake is your system of record.

• Users ask questions in natural language.
• It generates SQL and executes it inside your Snowflake warehouse.
• Data stays within Snowflake's governance boundary; Snowflake says Cortex Analyst does not train on customer data.
• It supports a REST API, so you can put your own chat/UI in front of it.
• Semantic Views let you define metrics, joins, dimensions, synonyms, and business rules rather than relying on raw table names alone.

Architecture:

User → Your UI/API → Cortex Analyst → SQL → Snowflake → Results

No ETL, reverse ETL, database copy, or vector database is required for the basic structured-data use case.

2. ThoughtSpot — best if you want a polished end-user analytics product

ThoughtSpot can directly query Snowflake rather than requiring you to move the underlying data. ThoughtSpot explicitly describes its Snowflake integration as no data movement, using Snowflake's compute engine for queries.

It is a stronger choice if you want dashboards, search, visualization, embedded analytics, and a polished business-user experience in addition to text-to-SQL.

My recommendation

If you're primarily looking for a text-to-SQL tool/API, start with Cortex Analyst. It is native to Snowflake and avoids introducing another data platform.

If you tell me whether this is for developers building an API, analysts querying Snowflake, or nontechnical users in a chat interface, I can narrow it down to the best 2–3 options and compare pricing, accuracy, security/RBAC, semantic-layer requirements, and setup effort.

If you want a text-to-SQL tool that connects directly to your data warehouse without moving, syncing, or copying any data outside of Snowflake , you have native options built directly into Snowflake as well as external semantic-layer tools that push query execution down to Snowflake.

Zero data movement means the AI tool reads your schema metadata or semantic model, translates your natural language prompt into SQL, and executes it securely inside your Snowflake warehouse.

1. Native Snowflake Solutions (Zero Data Movement)

These options run entirely within the Snowflake security perimeter. Your data never leaves your account.

• Snowflake Cortex Analyst
  • How it works: A fully managed, LLM-powered feature designed specifically for building reliable text-to-SQL applications . It connects to a semantic model (a YAML file defining your tables, relationships, and verified queries) stored in your Snowflake stage.
  • Data movement: None. It leverages Snowflake Cortex AI and executes queries natively.
  • Best for: Developers building custom internal chat apps or conversational interfaces via the Cortex Analyst REST API.[1][2]
• Snowflake Intelligence / AI/BI & Native Semantic Views
  • How it works: Powered by Snowflake’s specialized text-to-SQL models (like the Arctic-Text2SQL engine), this allows conversational analytics directly over native semantic views (SELECT * FROM SEMANTIC_VIEW(...)).
  • Data movement: None. Fully integrated into the Snowflake ecosystem.
  • Best for: Out-of-the-box conversational querying inside Snowflake's ecosystem without managing external middleware. [1][2]
• Snowflake Worksheet AI / GenAI (Ctrl + I)
  • How it works: Built right into Snowsight worksheets, allowing you to highlight or type plain English text and generate executable SQL instantly.
  • Data movement: None.[1]

2. External Semantic & BI Tools (Pushdown Execution)

If you prefer a third-party app interface rather than building it natively in Snowflake, these tools connect via a direct secure driver, read the schema, and push down the SQL execution to Snowflake. No data is replicated or stored in the vendor's cloud.

• ThoughtSpot
  • How it works: Connects natively to Snowflake , allowing users to search data via natural language. It generates the SQL on the fly and pushes execution entirely to your Snowflake warehouse.
  • Data movement: Zero data replication; metadata is indexed, but row data stays in Snowflake.
• DBeaver (AI Assistant / Chat)
  • How it works: If you work in a SQL IDE, DBeaver features an integrated AI chat that connects to your active Snowflake JDBC/ODBC session, writes the SQL from natural language prompts , and runs it right inside your current session.
  • Data movement: None.

If you want to narrow this down, tell me:

• Are you looking for an end-user chat interface (like a BI tool) or an API to build a custom app?
• Do you already use Snowflake Cortex enabled in your account?

If you want text-to-SQL over Snowflake without moving data, look for a tool that connects directly to your Snowflake warehouse, reads metadata/schema information, generates SQL, and executes queries in Snowflake. A few options:

For an enterprise deployment, I would generally evaluate:

1. Snowflake Cortex Analyst if you want the simplest governance story:

• Data stays inside Snowflake.
• Uses Snowflake roles/security.
• Best if your organization is already invested in Snowflake.

2. A self-hosted text-to-SQL layer (such as a Haystack-based system) if you need:

• Your own UI.
• Custom LLM routing.
• More control over prompts, validation, and deployment.

3. A BI-style assistant if the main users are analysts/business teams who want “ask my warehouse a question” rather than build an AI application.

Key capabilities to require:

• Direct Snowflake connector (JDBC/ODBC/Snowflake API)
• Read-only role support
• Schema/metadata grounding
• SQL validation before execution
• Query cost controls (warehouse limits, row limits)
• Audit logs of generated SQL
• Support for Snowflake views, secure views, and semantic layers

If you tell me whether this is for analysts, customers, or an internal developer tool, I can narrow the shortlist.

If you want a text-to-SQL solution that connects directly to your existing data and does not require moving, syncing, or copying your data outward , you have two main architecture paths: fully native solutions (where the AI runs inside Snowflake) and zero-ETL/direct-connect external tools (where the app queries Snowflake in place using metadata only).

The top options available for this use case include:

1. Snowflake Cortex Analyst (Native / In-Snowflake)

• How it works: Snowflake Cortex Analyst is a fully managed, native text-to-SQL service built directly into Snowflake . It pairs a fine-tuned LLM with a YAML-based semantic model that you define over your existing Snowflake tables.
• Data Movement: Zero. The AI models run entirely inside Snowflake's security perimeter against your live data warehouses. No data ever leaves your Snowflake account.
• Best for: Enterprise deployments looking for high accuracy (~90%+) on structured business data, accessible either via Snowflake Intelligence or custom applications via the Cortex Analyst REST API.

2. Vanna.ai (Open-Source / Self-Hosted or Direct Connect)

• How it works: Vanna is an open-source Python framework for text-to-SQL. It works by training an AI (via embeddings/metadata) on your database schema and documentation, then generating the SQL query.
• Data Movement: Zero (if self-hosted). You can run Vanna locally or in your own secure VPC, connecting directly to Snowflake via standard Python drivers (like snowflake-connector-python ). It only reads your schema metadata to train the model and pushes the final SQL query back to Snowflake to execute live. No actual table data is ingested or stored by Vanna.
• Best for: Developers who want to build a custom internal chat interface or Streamlit app embedded tightly into their own infrastructure.

3. DBeaver PRO / Ultimate with AI Assistance

• How it works: If you are looking for a database management GUI rather than a conversational chatbot, DBeaver features built-in AI natural language-to-SQL generation.
• Data Movement: Zero. It connects directly to your live Snowflake instance via standard JDBC drivers . You type what you want in plain English inside the SQL editor, the AI writes the query in place, and you execute it against your live data.
• Best for: Data engineers and analysts who want an AI assistant directly inside their day-to-day SQL workspace.

Which approach should you choose?

• Choose Cortex Analyst if you want a turnkey, highly accurate, enterprise-grade solution that stays 100% native to Snowflake.
• Choose Vanna.ai if you want an open-source, customizable Python stack that you can host internally while keeping your database connection strictly direct.

To help narrow down the ideal setup, let me know:

• Do you prefer a ready-made enterprise API/native feature or an open-source/custom Python framework?
• Will this tool be for internal data analysts or embedded into a customer-facing app?