# Soda vs Great Expectations: which do AI models recommend for data observability, October 2026

IT AI Recommendation Index, October 2026 Edition, Data quality and observability. One of fourteen models named Soda first on the direct prompt; zero named Great Expectations. Page: https://it-ai-index.com/it-data/data-quality-and-observability/soda-vs-great-expectations/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Soda | 9% | #2 of 9 | 7% | 42 | 14 of 14 |
| Great Expectations | 2% | #5 of 9 | 16% | 37 | 14 of 14 |

## The direct prompt, model by model

- GPT-5.4 mini: soda first (first choices: Monte Carlo, Soda) (alternatives: Acceldata, Bigeye)
- Gemini 3.5 Flash: neither first, one named (first choices: Metaplane) (alternatives: Elementary, Soda)
- DeepSeek V4 Flash: neither first, one named (first choices: Metaplane) (alternatives: Soda)
- Qwen 3.7 Flash: neither first, one named (first choices: Metaplane) (alternatives: Great Expectations, Soda)
- Kimi K2: neither first, one named (first choices: Metaplane) (alternatives: Bigeye, Soda)
- GPT-6 Luna: neither first, one named (first choices: Metaplane) (alternatives: Monte Carlo, Soda)
- Claude Haiku 4.5: neither named (first choices: Monte Carlo) (alternatives: Bigeye, New Relic)
- Perplexity Sonar: neither named (first choices: Metaplane) (alternatives: Bigeye)
- Grok 4.1 Fast: neither named (first choices: Metaplane) (alternatives: Monte Carlo)
- Mistral Small: neither named (first choices: Metaplane)
- Llama 4 Maverick: neither named (first choices: Metaplane, New Relic)
- GLM 4.7 FlashX: neither named (first choices: Metaplane) (alternatives: Anomalo, Bigeye, Monte Carlo)
- MiniMax M2.5: neither named (first choices: Metaplane) (alternatives: Bigeye, Monte Carlo)
- Muse Glimmer 30B: neither named (first choices: Bigeye, Metaplane)

## What the models said about Soda

- "Why be cautious: if you plan to stay on Soda Core open-source, assess ongoing support and feature parity; Soda Cloud pricing and roadmap are not self-serve" (Muse Glimmer 30B, negative prompt, soft negative)
- "License change risk... Not full observability... Limited out-of-the-box metrics." (DeepSeek V4 Flash, negative prompt, soft negative)
- "Soda might require more upfront effort to configure tests" (Claude Haiku 4.5, negative prompt, soft negative)
- "Soda is often the better default because mid-sized teams usually benefit from faster adoption and built-in observability" (GPT-5.4 mini, paraphrase prompt, first choice)
- "I'd shortlist Soda. It offers checks and monitoring across common warehouses and databases" (GPT-6 Luna, paraphrase prompt, first choice)
- "best value choice is usually Great Expectations (GX Core) or Soda Free/open-source first" (GPT-5.4 mini, budget prompt, first choice)

## What the models said about Great Expectations

- "Data Quality Platforms to Avoid or Be Cautious About ... Great Expectations (OSS Version) ... Best avoided if: You need real-time monitoring, have large datasets" (GLM 4.7 FlashX, negative prompt, hard negative)
- "Great Expectations is widely criticized in the data engineering community for being overly complex, heavy, and difficult to configure." (Gemini 3.5 Flash, negative prompt, soft negative)
- "it avoids both the enterprise pricing overhead of Monte Carlo and the DIY complexity of open-source options like Great Expectations" (DeepSeek V4 Flash, direct prompt, soft negative)
- "### 1. Great Expectations — Open‑Source Framework ... Pros: Free, highly customizable, widely adopted" (GLM 4.7 FlashX, comparative prompt, first choice)
- "Great Expectations and Soda Core lead for data engineering teams that embed validation in CI/CD pipelines." (Muse Glimmer 30B, comparative prompt, first choice)
- "best value choice is usually Great Expectations (GX Core) or Soda Free/open-source first" (GPT-5.4 mini, budget prompt, first choice)

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.