Domino Data Lab vs Ray Serve
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
Domino Data Lab is the stronger AI pick in MLOps Platforms: it has a visibility score of 5.6 versus 0 for Ray Serve.
Choose Domino Data Lab if…
- You want the brand AI engines rank higher (5.6 vs 0)
- You need a self-hosted or on-premises option
- You need ISO 27001 or ISO 9001 or SOC 2 or HIPAA or GDPR coverage
- Reproducible development environments
Choose Ray Serve if…
- You need SOC 2 Type II or ISO/IEC 27001 coverage
AI visibility side by side
Latest weekly run · Nov 2
| Metric | Domino Data Lab | Ray Serve |
|---|---|---|
| Visibility score | 5.6% | 0% |
| Change vs last week | New | New |
| Rank in MLOps Platforms | #15 | #61 |
| Share of voice | 2.5% | 0% |
| Mentions this run | 17 | 0 |
| Positive in AI answers | 35% | — |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Domino Data Lab 0pp · Ray Serve 0pp over 0 weekly runs.
Engine by engine
Domino Data Lab leads on 3 of 3 engines
Head to head in AI answers
0 engine answers name both brands
Features, pricing & fit
| Dimension | Domino Data Lab | Ray Serve |
|---|---|---|
| Pricing model | Flat tiers | — |
| Entry price | Contact sales | — |
| Free plan | No | — |
| Deployment | Single-tenant managed SaaS (Domino Cloud), or self-managed in the customer's VPC or on-premises, with hybrid and multicloud operation via Domino Nexus. Runs on Kubernetes. | Self-managed on a laptop, in a Docker container, on a managed Ray cluster, or on Kubernetes via KubeRay |
| Compliance | ISO 27001, ISO 9001, SOC 2, HIPAA, GDPR | SOC 2 Type II, ISO/IEC 27001 |
| Support | Premium support with 2 business day response for non-urgent issues and 2 hour response for urgent issues, Enterprise support with 1 business day response for non-urgent issues and 1 hour response for urgent issues, Designated customer success manager on Enterprise plans, Security team available 24/7 for enterprise customers | Ray Community Slack, Hands-on training and expert support from the Anyscale team that created Ray |
| Founded | 2013 | — |
| Headquarters | San Francisco, CA | San Francisco, California |
| Employees | 201–500 | — |
| Funding | Strategic equity investment from UBS · $228M raised | Series C |
| Best for | Large enterprises in regulated industries such as life sciences, financial services and the public sector, especially code-first data science teams. | Python teams serving machine learning models, including LLMs, as online inference APIs that need independent scaling |
Plans side by side
Domino Data Lab
Ray Serve
Pricing not available.
Pros and cons
Themes from the AI answers that mention each brand
Domino Data Lab
- Reproducible development environments
- Centralized governance and lifecycle policy enforcement
- Suited to collaborative, governed enterprise data science at scale
- Analyst recognition, such as a Gartner Visionary placement
Ray Serve
Profile in progress.
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Domino Data Lab vs Ray Serve”, https://slateindex.ai/compare/domino-data-lab-vs-ray-serve, data of Nov 2, 2026. Licence: CC BY 4.0.