ArizeThe agent observability and evaluation platform.
vs
Ray ServeScalable model serving library for building online inference APIs
Comparison · MLOps Platforms · weekly run Nov 2
Arize vs Ray Serve
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
Arize is the stronger AI pick in MLOps Platforms: it has a visibility score of 1.5 versus 0 for Ray Serve.
Choose Arize if…
- You want the brand AI engines rank higher (1.5 vs 0)
- You need a self-hosted or on-premises option
- You need ISO 27001 or PCI DSS or HIPAA or GDPR coverage
Choose Ray Serve if…
- You need ISO/IEC 27001 coverage
AI visibility side by side
Latest weekly run · Nov 2
| Metric | Arize | Ray Serve |
|---|---|---|
| Visibility score | 1.5% | 0% |
| Change vs last week | New | New |
| Rank in MLOps Platforms | #34 | #61 |
| Share of voice | 0.7% | 0% |
| Mentions this run | 5 | 0 |
| Positive in AI answers | 20% | — |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Arize 0pp · Ray Serve 0pp over 0 weekly runs.
Engine by engine
Arize leads on 3 of 3 engines
ChatGPT
AI Mode
Perplexity
ArizeRay Serve
Head to head in AI answers
0 engine answers name both brands
0Arize ranked higher
0Ray Serve ranked higher
Features, pricing & fit
| Dimension | Arize | Ray Serve |
|---|---|---|
| Pricing model | Flat tiers | — |
| Entry price | Free; paid from $50 / mo | — |
| Free plan | Yes | — |
| Deployment | SaaS, with self-hosted deployment on the Enterprise plan; Phoenix runs locally or self-hosted. | Self-managed on a laptop, in a Docker container, on a managed Ray cluster, or on Kubernetes via KubeRay |
| Integrations | 40+ | — |
| Compliance | SOC 2 Type II, ISO 27001, PCI DSS, HIPAA, GDPR | SOC 2 Type II, ISO/IEC 27001 |
| Support | Community, Email, Dedicated support on Enterprise | Ray Community Slack, Hands-on training and expert support from the Anyscale team that created Ray |
| Founded | 2020 | — |
| Headquarters | — | San Francisco, California |
| Employees | 51–200 | — |
| Funding | Series B-1 extension · $131M raised | Series C |
| Best for | AI engineering teams building and running production AI agents and LLM applications who need observability and evaluations. | Python teams serving machine learning models, including LLMs, as online inference APIs that need independent scaling |
Plans side by side
Ray Serve
Pricing not available.
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Arize vs Ray Serve”, https://slateindex.ai/compare/arize-vs-ray-serve, data of Nov 2, 2026. Licence: CC BY 4.0.