SnowConvert AI alternatives: Why Datafold is the better choice

SnowConvert AI is Snowflake’s newly branded addition to its self-serve migration toolkit, but despite the name, it’s not a major departure from the existing SnowConvert solution. Instead, it layers AI-powered guidance on top of SnowConvert’s core functionality: translating SQL code from legacy systems like Teradata, Oracle, and SQL Server into Snowflake-compatible syntax.

At the 2025 Snowflake Summit, the messaging was clear: Snowflake is doubling down on enabling self-serve, customer-led migrations, but with a narrow focus on code translation and developer experience. The rollout of a VS Code–based AI tool, to be used with the existing Migration Assistant GUI, reflects a strategy centered on helping customers fix translation issues in SQL, not on delivering validated, production-ready migrations.

This signals a shift away from relying solely on partners or SIs, and instead aims to help existing customers consolidate more workloads into Snowflake. But without built-in data validation, orchestration, or support for complex workflows, the tooling falls short of what’s needed for high-stakes or enterprise-scale migrations. It’s a lightweight, syntax-level assistant, but not a full migration system.

We reviewed the documentation and launch materials to understand what SnowConvert AI actually does and where its current capabilities fall short of its marketing claims. 

SnowConvert AI’s limitations include:

• SQL-only conversion: SnowConvert focuses solely on translating static SQL objects. It doesn’t support modern development frameworks like dbt, nor does it account for downstream dependencies across orchestration layers or reporting tools.
• Limited source database support: Although SnowConvert supports a range of source databases, SnowConvert AI is currently only optimized for SQL Server.  
• No granular, value-level data diffing: It checks schema structure but doesn’t verify if actual data values match between source and target.
• No end-to-end automation or validation: SnowConvert AI provides helpful AI suggestions for code-level fixes, but it doesn’t orchestrate migrations, validate ETL pipelines, or verify stored procedure behavior. You still need to handle extraction, loading, testing, and reconciliation yourself.
• No AI-driven feedback loop: While the Migration Assistant offers LLM-powered fixes, there’s no iterative “translate → test → refine” cycle based on data correctness or test coverage.
• Manual and developer-centric: SnowConvert AI is accessible only through VS Code, requiring users to inspect and fix each translation issue one at a time using Cortex-powered suggestions. It offers no data validation or team collaboration features, making it poorly suited for complex or large-scale migrations.
• Lack of collaboration & reporting tools: SnowConvert runs as a standalone desktop application, generating static, hard-to-share reports, which is an impractical setup for most data migrations.

For simple SQL translation, SnowConvert AI may be enough. But in migrations, data accuracy matters just as much as code correctness. Without validation, there’s no way to know if errors were introduced. A migration tool needs to ensure every record is correctly migrated, business logic is preserved, and ETL pipelines remain intact.

That’s where Datafold’s Migration Agent (DMA) comes in. Unlike SnowConvert AI, DMA ensures that every record is migrated correctly, every transformation is validated, and every workflow remains intact. And, DMA is data warehouse agnostic and stands out for its true automation. 

What is SnowConvert AI and how does it work?

SnowConvert AI is a free, AI-powered assistant built into Snowflake’s VS Code extension. It works hand-in-hand with SnowConvert, helping developers manually resolve SQL translation issues that SnowConvert couldn’t automatically fix. 

At its core, SnowConvert is a SQL translator GUI that converts database objects (e.g. tables, views, stored procedures) from legacy systems into Snowflake SQL. It handles schema-level translation, but doesn’t validate that the migration is complete or correct. SnowConvert AI builds on this by using Snowflake Cortex to suggest fixes for problematic code, but it still requires manual review and offers no automated validation or orchestration.

Because SnowConvert and SnowConvert AI are tightly linked—one doesn’t function meaningfully without the other—we’ll discuss both together here. We reviewed product descriptions and documentation to understand what the tooling does well, where it falls short, and how it compares to Datafold’s Migration Agent. (If you’re ready to get into the technical weeds, check out DMA’s docs or this blog that looks into how DMA works).

SnowConvert features and benefits

SnowConvert’s docs describe how it converts code into Snowflake SQL:

• Code parsing: SnowConvert analyzes SQL using abstract syntax trees (ASTs) and symbol tables to extract and convert database objects. This allows it to identify key database objects for conversion; however, it focuses solely on the structural aspects of the code rather than the actual data content.
• Rule-based transformation: It applies predefined rules to convert tables, views, and stored procedures into Snowflake’s SQL dialect. While this automates the basic conversion process, it does not employ iterative refinement to automatically fine-tune the output until full parity is achieved.
• Schema-level validation: It checks for syntax compatibility and structural correctness, flagging issues in a detailed report. This validation does not extend to value-level data comparisons or end-to-end workflow accuracy (e.g., in ETL pipelines).
• Integration: After conversion, SnowConvert outputs converted SQL and static reports to local directories. It does not support orchestration, versioning, or integration with dbt, Airflow, or BI tools. 
• Self-serve model: SnowConvert is offered as a free, self-serve solution, enabling users to run the conversion process on their own. However, the primary interface is a standalone app or CLI,, meaning it does not include a comprehensive, interactive dashboard for full migration management.

SnowConvert AI features and benefits

SnowConvert AI is an AI-powered assistant integrated into Snowflake’s Visual Studio Code extension. It is designed to help users fix post-conversion issues left unresolved by SnowConvert, using Snowflake Cortex to generate explanations and code suggestions.

• Post-conversion focus: SnowConvert AI only activates after you’ve run SnowConvert and generated .sql files with unresolved EWIs (Errors, Warnings, and Issues). It cannot be used independently and is not part of the core SnowConvert CLI or desktop tooling.
• AI-generated suggestions: For each flagged issue, the assistant uses the Cortex API to provide natural-language explanations and recommended code fixes. These suggestions are contextual but require manual review and application by the user.
• Manual workflow: SnowConvert AI runs inside VS Code, requiring users to open a project folder, locate each issue, and click through them one by one to get assistance. There’s no bulk processing, batch testing, or validation automation.
• No data-level validation: Despite its AI features, the assistant does not perform any form of value-level data diffing, reconciliation, or verification that the translated code returns correct results. Test case generation is mentioned in marketing materials, but not evidenced in public docs.
• No team collaboration or orchestration: Outputs and suggestions live entirely within a developer’s local VS Code environment. There is no sharing, audit trail, or integration into CI/CD pipelines or migration tracking systems.

What’s missing?

SnowConvert follows a one-time, rule-based process—if the converted SQL isn’t accurate, it’s your job to fix it. SnowConvert AI adds an assistive layer to help resolve these issues, but it does so one file and one issue at a time, within VS Code, with no automated validation or testing of whether the translated code is actually correct.

Critically, neither tool checks whether the data itself is consistent between source and target. There’s no value-level diffing to confirm that business logic or query results remain unchanged post-migration.

This makes it useful for small-scale conversions or individual object testing, but insufficient for full-scale, production-grade migrations where correctness and trust in the outcome are non-negotiable.

SnowConvert AI migration solution: Pros and cons

SnowConvert AI’s strength lies in its no-licensing-fee model and its ability to convert legacy SQL code into Snowflake-compatible formats using configuration-based transformation and schema-level validation. 

For teams needing SQL code conversion and nothing more, SnowConvert works. But for full-scale migrations at the enterprise level, it leaves too many gaps.

Perhaps the biggest limitation is how SnowConvert operates as a standalone desktop application that generates static reports. This doesn’t scale. In complex migrations, data engineers need to track errors, validate data, and collaborate in real time. Instead of a shared dashboard or workflow engine, SnowConvert AI requires teams to manually manage outputs and findings, which is an impractical setup when reconciling thousands or millions of records.

Comparing Datafold and SnowConvert AI 

While SnowConvert handles SQL conversion, it doesn’t address the broader challenges of data validation, iterative refinement, or non-SQL migration elements.

Datafold’s Migration Agent (DMA) takes a more comprehensive approach—automating not just code conversion but also value-level validation and iterative improvement. DMA translates SQL, stored procedures, and ETL workflows, then systematically verifies that every record in the new system matches the original. Using an AI-powered “translate, diff, repeat” loop, DMA continuously refines its translations, reducing manual debugging and ensuring full parity between source and target.

Here’s the head-to-head comparison:

Why a comprehensive AI-driven approach matters

SnowConvert AI brings AI-assisted SQL conversion into the IDE, but it’s still focused on a narrow slice of the migration problem: generating Snowflake-compatible code. It doesn’t validate the results, test for data parity, or support broader workflows like ETL or orchestration.

Ensuring that every data point is correctly migrated is just as critical as translating SQL syntax. Without value-level validation, there’s no way to know if transformations introduced errors.

Datafold’s Migration Agent (DMA) is designed to solve this problem. Instead of just translating code, DMA ensures that every record, every workflow, and every dependency is validated and accurate.

How Datafold’s approach works

Unlike SnowConvert AI’s piecemeal approach, DMA is built for continuous improvement. Its AI-powered system not only translates legacy SQL but also performs deep validation at every step:

1. Freezing the source data: DMA locks in a snapshot of the input datasets, ensuring consistent inputs across the source and target environments.
2. AI-powered code translation: Unlike static rule-based conversion, DMA iteratively refines SQL translations based on feedback.
3. Value-level validation: DMA compares every record post-migration, verifying accuracy at the most granular level.
4. End-to-end automation: DMA validates ETL workflows, stored procedures, and full pipeline transformations, reducing reliance on manual review.

Once data alignment is complete, Datafold’s AI engine translates legacy SQL, stored procedures, and ETL workflows into the target format. But the process doesn’t stop there.

The “translate, diff, repeat” cycle

DMA follows an iterative “translate, diff, repeat” cycle, powered by advanced AI and large language models:

1. Our AI translates legacy code into the target system
2. DMA performs granular, value-level data diffing, comparing every record in the source and converted output
3. If discrepancies are found, DMA automatically refines the translation and runs the process again
4. This loop continues until perfect data parity is achieved

This continuous refinement process—going beyond basic syntax translation to include non-SQL components like ETL workflows and stored procedures—is what sets Datafold apart from SnowConvert AI.

By automating the entire migration workflow and providing detailed, auditable reports on data lineage, DMA minimizes manual intervention, prevents silent data errors, and speeds up migration timelines by a factor of 6. 

Choosing the right migration tool

The cloud data wars have entered a new phase. With Snowflake releasing SnowConvert AI and Databricks acquiring BladeBridge and launching Lakebridge, cloud vendors are racing to offer free or open-source migration tools to pull workloads into their ecosystems. 

But free doesn’t mean complete, especially if they focus on code conversion, not complete migrations. A migration tool needs to do more than rewrite SQL—it must validate the data itself and ensure that business logic and workflows remain intact. 

SnowConvert AI generates Snowflake-compatible SQL but a real migration requires full data validation. Without it, teams risk:

• Silent data discrepancies that cause downstream issues
• Broken workflows and dependencies
• Time-consuming manual debugging
• Migrations going entirely wrong

This is where Datafold’s AI-driven Migration Agent (DMA) stands out. DMA automates the entire migration workflow with an iterative “translate, diff, repeat” cycle powered by advanced AI. This ensures every record is validated at a granular level, offering full data lineage transparency and significantly reducing manual intervention and error risk.

While free tools lower the entry cost, the risk of incomplete or inaccurate migrations can be far more expensive in the long run. If you’d like to see how DMA can accelerate your migration while ensuring complete accuracy, book time with our team.