Datafold Data Diff Alternative: Options, Cost, and What to Use After the Open Source Sunset

Short answer: Datafold archived the open source data-diff project on May 17, 2024, so the tool a lot of CI pipelines were built on has had no maintenance for over two years. The closest drop-in replacement is Reladiff, an actively maintained MIT fork by the original data-diff author. If you are on dbt, dbt-audit-helper and Recce cover most pre-merge comparison work without a contract. The commercial upgrade path is Datafold Cloud, which no longer publishes a price and starts at 15,000 dollars a year for five developer seats on AWS Marketplace. None of these watch production, which is a different purchase.

This query exists for a specific reason, and it is worth being clear about it. Datafold published a post titled Sunsetting open source data-diff and archived the repository on May 17, 2024. It is read-only now: roughly 3,000 stars, 1,932 commits, MIT licensed, and frozen. Datafold's stated reason was that maintaining two products with overlapping functionality and separate codebases was pulling effort away from Datafold Cloud, which is a defensible call for the company and an annoying one if your nightly migration check imports data_diff. Here is what you can actually move to, with the costs checked at the source on September 16, 2026.

Datafold data diff alternatives compared

Reladiff is the closest thing to a drop-in replacement

If you want the same shape of tool, this is the one to look at first. Reladiff describes itself as a fork of an archived project called data-diff, and it is maintained by erezsh, who wrote the original. It keeps the design decision that made data-diff fast: the diff is calculated inside the database rather than by pulling both tables across the network, so comparing large tables does not mean moving them. At the time of writing it carries 543 stars and 1,080 commits, with issues and pull requests still moving, and it stays under the MIT license.

Five hundred stars against the original's three thousand is worth reading correctly rather than as a verdict. Most of those three thousand stars were collected while data-diff was being actively promoted by a funded company; the fork inherited the code and the author but not the marketing budget. What matters more for a production decision is whether commits are recent and whether your database is supported, both of which you can check in an afternoon. The honest risk is bus factor: this is a small project, and you are trading a vendor's support contract for a maintainer's goodwill.

If you are a dbt shop, the dbt-native options are probably enough

A lot of teams reaching for data-diff were doing one specific thing with it: proving that a model refactor did not change the numbers. dbt-audit-helper does that natively. It is a dbt Labs package whose compare_relations macro generates the SQL for a row-by-row validation of two relations, with compare_queries for the same job against arbitrary queries and further macros for row counts and column values. It has been the standard answer for dbt migration audits for years, and dbt Labs documents it for exactly that use case. Our GSC data shows people searching for audit helper dbt land on this site regularly, so the pairing is not obscure.

Recce covers the other half. It is built for pull request review: you point it at two dbt environments and it surfaces what a change did to the data, so a reviewer can assess impact rather than read a diff of SQL and guess. Between the two you get most of what data-diff was doing inside a dbt project, in tools that are maintained and that your team already has the context to run. The trade is that both are dbt-scoped. Neither will compare a Postgres table against its replicated copy in Snowflake, which was data-diff's other main job, and for that you want Reladiff or the commercial product.

What Datafold Cloud costs now

Datafold Cloud starts at 15,000 dollars for a 12 month contract covering five developer seats. That figure is not on Datafold's website, because datafold.com/pricing no longer exists as a pricing page; it serves a short stub that forwards to the contact form. The number comes from Datafold's AWS Marketplace listing, which publishes two purchasable dimensions: five provisioned developer seats at 15,000 dollars for twelve months, and ten seats at 30,000 dollars. Both divide out to 3,000 dollars per developer per year, so there is no volume break between the two published rungs. We have broken the whole listing down, including the overage rate and the refund terms, on our Datafold pricing page.

The detail that changes budgeting is the unit. Datafold bills for seats, not for tables, rows or volume, which makes it unusually generous if you are a small team running a very large estate and unusually expensive if you are a small team running a small one. Five seats is the floor on the public listing, so a two person team pays the same 15,000 dollars as a five person team. It is also worth knowing that the widely quoted 799 dollars a month figure comes from a Datafold blog post published on June 2, 2022 that announced a free tier and a Cloud tier. Neither the free tier nor that pricing page exists today, so budgeting against that number will leave you roughly 5,000 dollars short before the first call.

The thing none of these tools do

This is the part worth pausing on, because it is where most of the wasted money in this category goes. Every option above answers the same question: what will this change do to the data before I merge it. That is genuinely valuable, and if your incidents mostly start with a refactor or a warehouse migration, a diffing tool is the right purchase. But it is a pre-merge question, and a large share of data incidents have nothing to do with code changing.

A table that did not load at 4am because an upstream API was down, a row count that halved because a partner changed their export, a column that silently started arriving null, a schema change made by someone who never opened your repository: no diff tool catches any of those, because nothing was merged. That is continuous monitoring, and it needs a baseline learned per table rather than a comparison between two versions of your code. The two jobs get conflated constantly, and teams end up buying change-safety software to solve a monitoring problem, then wondering why production still breaks. It is also worth separating the validation from the movement itself, since keeping systems in sync is a different job from proving they match, and a dedicated integration layer between your apps, APIs and databases handles the replication that a diff tool only checks after the fact.

If production monitoring is what you actually need, that is what we built, and the pricing is published rather than quoted: 99 dollars a month for 50 monitored tables, 299 for 250 and 799 for 1,500, billed yearly, with freshness, volume, schema, distribution and lineage across Snowflake, BigQuery, Databricks and Redshift, a read-only connection and a 14 day trial with no card. You can see how that sits against the rest of the category on our data observability pricing comparison.

Is open source data-diff still safe to use?

It still runs, and the MIT license means you can fork it and keep it running indefinitely. What you no longer get is maintenance. The repository has been read-only since May 17, 2024, which means no bug fixes, no support for database versions released since then, and no security patches for it or its dependency tree. For a tool that holds credentials to two production databases at once, an unpatched dependency chain is a real consideration rather than a theoretical one. Treat a fork as code you now own.

Why did Datafold archive data-diff?

Datafold said it directly: supporting the open source tool meant maintaining two distinct products with different codebases and significantly overlapping functionality, and it chose to put its resources behind Datafold Cloud instead. Read alongside the removal of its published pricing tiers and a repositioning around AI-powered migrations and data engineering automation, the pattern is a company moving upmarket and closing its self-serve on-ramp. That is a normal commercial decision, and it does mean there is no longer a free path into the product.

What is the best free alternative to Datafold?

Reladiff for cross-database diffing, dbt-audit-helper for dbt model comparisons. Both are MIT or open source and both are maintained. Be honest with yourself about the real cost, though: free tools mean your team owns the upgrades, the database driver compatibility, the CI wiring and the on-call when the comparison job itself breaks. For a team of two or three that is often a fair trade. For a team already behind on its roadmap, a few engineering days a quarter is not cheaper than a license.

Can I use data diff and data observability together?

Yes, and mature teams usually do, because they cover opposite ends of the same pipeline. A diff tool gates the merge: it tells you what your change will do to production output before it ships. Observability watches what happens afterwards: freshness, volume, schema drift and distribution against a learned baseline, with lineage so you can trace a bad number to its source. Running both is not redundant. Running only the first and calling it data quality is the common and expensive mistake, which is the same gap we cover on our Datafold alternative page and, for dbt-native teams weighing a package against a platform, on our Elementary alternative comparison.

Does Datafold still offer a free tier?

No. Datafold announced a free tier on June 2, 2022 in a post called Datafold for Everyone, alongside a Cloud tier at 799 dollars a month billed annually. Checked on September 16, 2026, the pricing page that listed them forwards to a contact form and neither tier is offered. The cheapest Datafold contract we can verify anywhere today is the 15,000 dollar five seat listing on AWS Marketplace, and the listing states that all fees are non-cancellable and non-refundable except as required by law.