Soda Data Quality Alternative: Soda Core, Contracts and Lineage

What Soda does well

Soda describes itself in its own documentation as "a data quality platform", built on three pillars that are worth separating because they are sold together. Data testing is the proactive half: you declare expectations in YAML using SodaCL, its check language, and run them in development or in CI so bad data never reaches production. Data observability is the reactive half, using machine learning to flag anomalies in freshness, row counts and null rates without you defining every rule up front. Data contracts sit on top, making the expectations an explicit agreement between producer and consumer.

The testing half is genuinely good, and it is the part Datatrail does not do. If your problem is that a specific business rule must never be violated, that revenue can never be negative, that a status column only ever holds five values, that a reconciliation must balance to the cent, then a declarative check in your pipeline is exactly the right tool and no amount of lineage replaces it. Soda's YAML is more readable than writing the same assertions in Python, which is the main reason teams pick it over Great Expectations.

Credit where it is due on pricing too. Almost nobody in this category publishes a number. Collibra, Alation, Atlan, Monte Carlo, Informatica, Acceldata and IBM all make you ask. Soda puts its tiers on the page, and so do we.

Soda Core is no longer open source

This is the finding most 2026 buyer's guides have not caught up with, and it matters if you chose Soda because it was open source.

Soda Core changed license with the v4 release. The LICENSE file in sodadata/soda-core was replaced on 28 January 2026 in a commit titled "V4 release", and it now contains the Elastic License 2.0 rather than Apache-2.0. The package metadata tells the same story from the other direction: soda-core 3.5.5 on PyPI carries the classifier "License :: OSI Approved :: Apache Software License", while soda-core 4.21.0 declares its license as "Proprietary" and carries no OSI classifier at all.

The Elastic License is source-available, not open source, and the Open Source Initiative does not recognize it. Two of its limitations are worth reading before you build on it. You may not "provide the software to third parties as a hosted or managed service, where the service provides users with access to any substantial set of the features or functionality of the software." And you may not "move, change, disable, or circumvent the license key functionality in the software", which tells you plainly that there is license key functionality in there.

To be fair about what this does and does not mean: you can still read the code, self-host it and use it in your own pipelines, and the older 3.x releases keep the Apache license they shipped under. Soda is far from the first company to make this move. But if your procurement process or your legal team treats "open source" as a requirement, soda-core no longer qualifies, and a listicle telling you otherwise is out of date. The project itself is healthy and busy, with 4.21.0 published on 13 August 2026 and commits landing daily.

What Soda costs

Verified from soda.io/pricing in August 2026. Soda meters usage in Soda Processing Units (SPUs), so the tier gets you a bucket of processing plus a feature set.

Annual billing and volume discounts are offered. Two details are worth pulling out of that table because they change evaluations. Collaborative data contracts are an Enterprise feature, which is a little surprising given that the soda-core repository describes itself as a "Data Contracts engine for the modern data stack". And SSO and RBAC are Enterprise-only, which for many companies means the tier decision is made by their security policy rather than by data volume.

For comparison, Datatrail publishes Starter at $99, Team at $299 and Scale at $799 per month, with two months free on annual billing. We do not have a permanent free tier, and Soda does. If your budget for this is zero and your need is a handful of checks, Soda Free is a genuinely better answer than anything we sell.

Why teams look for a Soda alternative

Four reasons come up, and they are all structural rather than complaints about quality.

• Coverage equals the checks somebody wrote. This is the fundamental limit of every declarative testing tool. A YAML check protects the columns it names. The table added last quarter, the field an analyst quietly started depending on, the raw landing table nobody modeled: none are covered until a human writes them in. Most real incidents happen in that uncovered space.
• There is no lineage graph. Soda does not build one. It integrates with the catalogs and lineage tools you already own, pushing quality results into them so the lineage view shows a quality badge. That is a sensible design, but it means the lineage has to come from somewhere else, and it means a failed check cannot tell you what it breaks.
• Column-level checks are not column-level lineage. These get conflated constantly. Soda can absolutely check a column. It cannot tell you which eleven downstream models and which finance dashboard read that column.
• The license and tier changes. Teams who adopted Soda Core as an Apache-2.0 project, or who assumed data contracts were in the product they were already paying for, sometimes reopen the evaluation.

Checks you write versus a graph you get

The honest framing is that these tools answer different questions, and the sharpest teams run both.

Soda answers "is this data what I said it should be?" You supply the definition of correct, and it enforces it precisely, early, in your pipeline. Datatrail answers "what is actually connected to what, and what breaks if I change it?" You supply nothing; it reads what your warehouse already did.

Concretely, Datatrail connects read-only and parses your query history and dbt manifest into column-level lineage. Because that graph is derived rather than declared, it covers every table in the warehouse from day one, including the ones nobody would have written a check for, and it follows data past the dbt boundary into ad hoc SQL and scheduled jobs. From the same graph it computes downstream impact, naming every model, exposure and dashboard that reads a column you are about to change, before you merge. Freshness and schema change monitoring are derived from each table's own history, so an alert arrives already ranked by what it breaks rather than as one more red check.

A sensible split: keep Soda for the business invariants only your team can express, the reconciliations and domain rules, and let lineage-derived monitoring cover the several hundred tables where nobody is going to write YAML. If you want the wider field, see our comparisons of data quality tools and data lineage tools, plus the data contracts pillar for how the standards actually work and data validation tools for the testing category specifically.