Compared
Soda vs Great Expectations: Data Quality Frameworks Compared
The short answer
Soda and Great Expectations are both open-source data quality frameworks where you declare the checks and they run them. Soda uses SodaCL, a compact YAML check language, and pairs Soda Core (free, Apache-2.0) with Soda Cloud, which publishes pricing: Free at 0 dollars, Team at 750 dollars a month, Enterprise custom. Great Expectations uses Python Expectation Suites; GX Core stays free and Apache-2.0 (now stewarded by Fivetran), but the hosted GX Cloud was acquired by FICO and retired from public availability on June 1, 2026, so there is no self-serve hosted GX today. Both catch the rules you write. Neither learns a table's normal behavior on its own, which is the gap continuous observability fills. Verified July 2026.
| Dimension | Soda | Great Expectations |
|---|---|---|
| What it is | Check framework (SodaCL, YAML) | Check framework (Expectations, Python) |
| Open-source core | Soda Core, Apache-2.0, free | GX Core, Apache-2.0, free |
| Hosted product | Soda Cloud, pricing published | GX Cloud retired June 2026 (FICO) |
| Published price | Free 0, Team 750 a month, Enterprise custom | None self-serve since GX Cloud retired |
| You must author the checks | Yes | Yes |
| Learns normal without rules | Limited anomaly checks | No, assertions only |
| Column-level lineage | No | No |
| Best for | Teams wanting readable YAML checks | Python teams wanting rich expectations |
Verdict
The bottom line
Choose Soda if you want readable YAML checks and published pricing, and Great Expectations if you want expressive, Python-native expectations and you are comfortable running GX Core yourself now that GX Cloud is gone. Both are frameworks that check what you tell them to. If you also want the failures nobody wrote a rule for, freshness across every table, and lineage-aware alerts, add Dataobservability: five pillars, column-level lineage, self-serve, from 99 dollars a month.
Both are frameworks, not monitoring systems
The most important thing to understand about Soda and Great Expectations is that they are the same kind of tool: declarative data quality frameworks. You write the checks (Soda in SodaCL YAML, GX in Python Expectation Suites), you decide where and when they run, and they pass or fail against the rules you stated. That is genuinely valuable, and for teams that want data quality as versioned code reviewed in pull requests, either one is a good foundation. But a framework only checks what you told it to check. It has no opinion about the table nobody wrote a rule for, and it does not run unless you schedule it. That boundary is what separates both of these from a continuous observability layer.
Where Soda has the edge
Soda is easier to adopt for a mixed team because SodaCL reads like plain configuration rather than code. A check like "row_count > 0" or "missing_count(email) = 0" is legible to an analyst, not just a Python engineer, so checks get written by more people. Soda also publishes its pricing, which is rare in this market: Soda Core is free and Apache-2.0, Soda Cloud has a Free tier, a Team tier at 750 dollars a month, and a custom Enterprise tier. Soda Cloud adds anomaly detection checks, alerting, and collaboration on top of the open-source engine. If you want readable checks and a clear price, Soda is the more approachable of the two.
Where Great Expectations has the edge
Great Expectations is the deeper toolkit for Python-first data engineering teams. The Expectations library is large, the validation results produce rich, shareable Data Docs, and because everything is Python you can express complex, custom logic that would be awkward in YAML. GX Core remains free, Apache-2.0, and actively maintained under Fivetran stewardship (v1.19 shipped in July 2026). The caveat that changed in 2026: the hosted GX Cloud was acquired by FICO and retired from public availability on June 1, 2026, so if you want GX you are running and operating GX Core yourself. For a team that already lives in Python and wants maximum expressive control over its assertions, GX is the stronger framework.
What neither one does, and where that hurts
Both tools require you to author every check, run only when invoked, and cover only the tables you pointed them at. That leaves the same gaps: the column that had never been null until today (no rule caught it), the table that stopped refreshing at 2pm (the check ran at 6am), the distribution drifting a few percent a week (no static threshold trips), and the tables nobody had time to write suites for. Neither computes column-level lineage, so a failed check does not tell you which dashboards it affects. Continuous data observability comes at it differently: it learns each table's normal freshness, volume, and distribution automatically, monitors every table you connect (not just the ones with hand-written checks), and attaches lineage so an alert arrives with its blast radius. Dataobservability is dbt-native, connects read-only in about 15 minutes, and publishes its price at 99 dollars a month, so most teams keep Soda or GX for the business rules they can state and add observability for the failures they cannot predict.
Questions
Frequently asked questions
Is Soda or Great Expectations better?
Neither is universally better. Soda is more approachable because SodaCL checks read like plain configuration and Soda publishes its pricing (Free, Team at 750 dollars a month, Enterprise custom). Great Expectations is more powerful for Python-first teams that want rich, custom expectations and shareable Data Docs. Both are open-source frameworks where you write the checks, so the real question is whether your team prefers readable YAML or expressive Python.
Is Great Expectations still free in 2026?
Yes. GX Core remains free and Apache-2.0, now stewarded by Fivetran, with active releases (v1.19 in July 2026). What changed is the hosted product: GX Cloud was acquired by FICO and retired from public availability on June 1, 2026, so there is no self-serve hosted Great Expectations anymore. If you use GX today, you run GX Core yourself.
How much does Soda cost?
Soda publishes its pricing, which is unusual in this market. Soda Core is free and Apache-2.0. Soda Cloud has a Free tier at 0 dollars, a Team tier at 750 dollars a month, and a custom Enterprise tier. That transparency is one reason teams shortlist it. For comparison, a self-serve observability platform like Dataobservability starts at 99 dollars a month.
Do I still need data observability if I use Soda or Great Expectations?
Often yes, because they cover different failures. Soda and GX check the rules you write, when you run them, on the tables you pointed at. They cannot catch the anomaly nobody wrote a rule for, a freshness failure hours after the check ran, or slow distribution drift, and neither maps lineage. Data observability learns each table's normal behavior, watches every table continuously, and attaches lineage, so teams keep their checks and add observability as the safety net.