A lot of data problems don't look like bugs at first — a column that used to never be empty suddenly has nulls, or a number that should stay between 0 and 100 jumps to 4,000, and three dashboards downstream quietly go wrong before anyone notices. "Data quality" tools exist to catch that automatically, by checking your data against rules every time a pipeline runs instead of waiting for someone to spot a weird chart. Great Expectations and Soda are two of the most established ways to do this, but they come from different philosophies: one is a free, code-first open-source framework, the other a commercial platform built around collaborative agreements and AI.
Great Expectations — the open-source framework data engineers already know
Great Expectations grew out of something data engineers were already doing by hand: writing custom Python scripts to check that a column isn't null or that a number falls in an expected range. It turned that instinct into a proper open-source framework — you define what your data should look like as "expectations" in code, and it validates every pipeline run against them automatically, with native integrations into Airflow, Databricks and Snowflake.
For who: data engineers and analytics engineers already working in Python, Jupyter and standard data orchestrators.
Price: GX Core is free (Apache 2.0); GX Cloud Developer tier is also free; Team and Enterprise pricing is on quote.
Strengths: the core product is 100% free and open source, it has one of the largest and most established communities in the data quality space, and it integrates natively with the orchestrators and warehouses most Python data teams already use.
Limits: you have to actually write Python code to define checks — there's no AI-driven anomaly detection to skip that step — and Team/Enterprise pricing on GX Cloud isn't public.
Soda — data contracts, with AI to explain and fix what breaks
Soda starts from a different problem: a lot of data quality issues come down to a mismatch between what a data producer thinks they're shipping and what a downstream consumer actually needs — a schema changes upstream, and dashboards break silently. Soda's core idea is to make that agreement explicit through "data contracts": collaborative, checkable definitions of what a dataset should look like, with AI layered on top to detect, explain, and in some cases automatically fix anomalies when a contract is broken.
For who: data engineering and data governance teams at mid-size to large companies who want a shared, non-code-only definition of data quality across teams.
Price: free tier with limited SPUs (Soda's usage unit); Team plan is $750/mo; Enterprise on quote.
Strengths: a real, usable free tier to start with, a published Team price so you're not stuck requesting a quote just to compare, and data contracts that let non-engineers participate in defining quality instead of it living only in someone's Python code.
Limits: the jump from the free tier to $750/mo is significant, Enterprise pricing still requires a quote, and letting AI auto-fix anomalies is a feature you have to actually trust before turning on.
Side-by-side
| Great Expectations | Soda | |
|---|---|---|
| Core model | Code-first, expectations defined in Python | Collaborative data contracts + AI |
| Free tier | Core is 100% free, open source | Free tier with limited usage (SPUs) |
| Paid pricing | Team/Enterprise on quote | Team $750/mo, Enterprise on quote |
| Non-engineers can define checks | No — requires writing Python | Yes — contracts are meant to be collaborative |
| AI-driven anomaly detection | Not built in | Yes, including proposed auto-fixes |
| Ecosystem | Large, established open-source community | Smaller, commercial-first |
Verdict
Pick Great Expectations if your team is comfortable in Python, you want to start at zero cost with no usage caps, and you're fine with quality checks living in code alongside the rest of your pipeline.
Pick Soda if you want non-engineers (analysts, data governance folks) to be able to define and see data quality rules without reading Python, you want a published price to plan around instead of always requesting a quote, and you're open to letting AI flag — or even propose fixes for — anomalies.
They're not mutually exclusive, either: some teams run Great Expectations as their free, code-level safety net and adopt Soda's contracts once they need cross-team buy-in on what "good data" actually means.