Alternative
Soda Alternative Without Writing All the Checks
Soda is a flexible, code-first data quality framework where you define checks yourself in SodaCL, and it is one of the very few vendors in this category that publishes a price. The real difference is not pricing, it is who writes the checks. Dataobservability auto-generates monitors across all five pillars from your warehouse and dbt project, so coverage does not depend on somebody remembering to write a rule.
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Short answer
Soda is a code-first data quality tool: you declare checks in SodaCL (a YAML-style check language), version them in Git, and run them in your pipeline. Soda publishes its pricing, with a free plan and a Team plan at 750 dollars a month. Dataobservability takes the opposite approach: it profiles every table you connect and generates freshness, volume, schema, and distribution monitors automatically, adds column-level lineage, and starts at 99 dollars a month.
Last updated August 2026
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Dataobservability vs Soda
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| Capability | Dataobservability | Soda |
|---|---|---|
| Auto-generated monitors | Partial | |
| ML anomaly detection | Partial | |
| End-to-end column-level lineage | ||
| Incident tracking | Partial | |
| Coverage without hand-writing checks | ||
| Checks as code in Git | Partial | |
| Publishes pricing | ||
| Entry price | 99 dollars a month | Free plan, then 750 a month |
| How usage is metered | Flat tier, no per table or per monitor meter | Processing units, with pay as you go for additional SPUs |
Comparison reflects general product positioning and is provided in good faith. Verify current capabilities with each vendor.
What a check language actually costs to maintain, and why that is the whole comparison
SodaCL is a good piece of design. Checks read close to English, they live in Git, they get reviewed in pull requests, and a data engineer can tell exactly what will be evaluated before it runs. That is a real advantage over monitoring you cannot inspect, and it is the reason Soda has a loyal following among teams who treat data like software. The cost is not the syntax, it is the authorship. Every check that exists is a check somebody decided to write, and every table without one is invisible. That property is fine when the surface is small and stable. It stops being fine at the point where the warehouse grows faster than the review queue, which on most teams is somewhere between one and two years in. The number worth calculating before an evaluation is the ratio of tables in your warehouse to tables carrying at least one check today. On most projects that number surprises the people who built it, and it is a better predictor of whether a code-first approach will hold than any feature comparison. The honest framing is that a check language and a learned baseline solve different halves of the problem: one encodes what you already know can break, the other notices what you did not think to ask about.
The processing unit meter, and what it prices
Soda is one of the few vendors in this category that publishes a number, and that deserves credit. The structure is a free plan at zero dollars with unlimited users, a Team plan at 750 dollars a month, and a custom Enterprise tier. Underneath the tier sits a consumption meter: plans include a quantity of processing units, and Team and Enterprise customers pay as they go for additional units beyond that. That is a more honest meter than per monitor pricing, because it tracks work performed rather than taxing coverage directly. It still means the bill responds to how often you check and how much data each check reads, so a decision to move from nightly to hourly evaluation on a large table is a budget decision as well as a detection-latency decision. Model that before signing rather than after, because the natural direction of travel for any monitoring program is to check more things more often. A flat tier removes the question entirely, which is worth something specifically to teams who do not want detection frequency negotiated against cost every quarter.
Where Soda is the better answer, stated plainly
There are cases where a check language wins and it would be dishonest to pretend otherwise. If your quality requirements are contractual rather than statistical, Soda expresses them better than any baseline can. Revenue must never be negative. A customer id must be unique. A regulatory extract must contain exactly the seventeen columns the recipient expects. Those are assertions, they are knowable in advance, and writing them down explicitly is the correct engineering choice. A learned baseline will never assert them, because statistics describe what usually happens rather than what must always happen. Soda is also the better fit when the reviewers of data quality rules are analysts rather than engineers, because SodaCL is readable by people who will not read Python, and when your governance process genuinely requires that every check be visible in a pull request with an approver attached. If those describe your situation, buy Soda and do not let a monitoring vendor talk you out of it.
Running both, which is what most mature teams actually do
The framing of Soda against a monitoring platform as an either-or decision is mostly a sales artifact. In practice the teams with the fewest incidents run both, and they divide the work along a clean line. Explicit checks cover the invariants that must hold and the contractual windows that carry consequences, and there are far fewer of these than a rules-first approach implies, usually tens rather than thousands. Automated monitoring covers everything else: the freshness, volume, schema and distribution of every table in the warehouse, scored against what that table normally does, including the tables nobody has gotten around to thinking about. The second layer is what catches the incident nobody predicted, and the first layer is what catches the incident that would be embarrassing to explain. If you already run Soda, adding metadata-driven monitoring does not require removing a single check or changing a pipeline, because a read only connection observes the warehouse rather than intercepting the work. That is the cheapest way to find out whether your current coverage has holes: turn on the observation layer and see what it flags in the first two weeks that nothing else did.
Honest verdict
Which one should you buy?
Pick Soda when
Choose Soda if your team wants data quality expressed as code, reviewed in pull requests, and run inside the pipeline, and if you have the engineering appetite to write and maintain those checks. SodaCL is genuinely good, the free plan is real, and Soda is one of the few vendors here that will tell you the price without a call.
Pick Dataobservability when
Choose Dataobservability if you want coverage on every table without writing a rule for each one, you want column-level lineage and incident tracking included, and you would rather spend engineering time fixing pipelines than maintaining check files. It starts at 99 dollars a month.
Questions buyers ask
Soda alternative FAQ
How much does Soda cost?
Soda publishes its pricing, which is rare in this category. There is a free plan at 0 dollars a month for small projects, a Team plan at 750 dollars a month for data engineering teams, and custom Enterprise pricing. Dataobservability starts at 99 dollars a month for Starter, 299 for Team, and 799 for Scale. Checked August 2026.
What is the difference between Soda and data observability?
Soda is a testing framework: it checks the rules you write. Data observability platforms monitor continuously and generate their own baselines, so they catch problems nobody thought to assert, like a table that stopped refreshing or a row count that halved. Soda tells you a known rule broke. Observability tells you something changed.
Is Soda open source?
Soda Core is open source and free to run yourself, and Soda Cloud is the paid hosted product with a free plan and a paid Team plan. The open-source path costs no license fee and the maintenance lands on your engineers, which is the usual trade in this category.
Can I use Soda and a data observability platform together?
Yes, and plenty of teams do. Hand-written checks are the right tool for business rules only a human knows (an order total must equal the sum of its lines). Automated monitors are the right tool for the failures nobody predicted. Using both means your rules cover intent and your monitors cover surprise.
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