# Soda vs Bigeye: 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; one named Bigeye. Page: https://it-ai-index.com/it-data/data-quality-and-observability/soda-vs-bigeye/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Soda | 9% | #2 of 9 | 7% | 42 | 14 of 14 |
| Bigeye | 3% | #4 of 9 | 23% | 35 | 14 of 14 |

## The direct prompt, model by model

- GPT-5.4 mini: soda first (first choices: Monte Carlo, Soda) (alternatives: Acceldata, Bigeye)
- Muse Glimmer 30B: bigeye first (first choices: Bigeye, Metaplane)
- Claude Haiku 4.5: neither first, one named (first choices: Monte Carlo) (alternatives: Bigeye, New Relic)
- Gemini 3.5 Flash: neither first, one named (first choices: Metaplane) (alternatives: Elementary, Soda)
- Perplexity Sonar: neither first, one named (first choices: Metaplane) (alternatives: Bigeye)
- 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)
- GLM 4.7 FlashX: neither first, one named (first choices: Metaplane) (alternatives: Anomalo, Bigeye, Monte Carlo)
- MiniMax M2.5: neither first, one named (first choices: Metaplane) (alternatives: Bigeye, Monte Carlo)
- GPT-6 Luna: neither first, one named (first choices: Metaplane) (alternatives: Monte Carlo, Soda)
- 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)

## 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 Bigeye

- "Why be cautious: integration gaps for non-standard stacks, workspace management overhead, and enterprise-only quote pricing." (Muse Glimmer 30B, negative prompt, soft negative)
- "users note tuning needs; consolidation (e.g., via acquisitions like Datadog/Metaplane) may disrupt roadmaps" (Grok 4.1 Fast, negative prompt, soft negative)
- "Monte Carlo, Anomalo, Bigeye: While popular, these ML-based platforms are best for specific use cases" (Mistral Small, negative prompt, soft negative)
- "Bigeye is positioned as the Enterprise AI Trust Platform combining data observability, end-to-end lineage and agentic AI governance. It is recommended for large enterprises 500+ employees" (Muse Glimmer 30B, scale prompt, first choice)
- "Bigeye is the platform most guides explicitly call out for mid-market ease of use and quick implementation" (Muse Glimmer 30B, direct prompt, first choice)
- "If you want production observability with automated anomaly detection: Monte Carlo or Bigeye" (GPT-5.4 mini, comparative 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.