ClearMLThe Infrastructure Platform For AI Builders
vs
Ray ServeScalable model serving library for building online inference APIs
Comparison · MLOps Platforms · weekly run Nov 2
ClearML vs Ray Serve
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
ClearML is the stronger AI pick in MLOps Platforms: it has a visibility score of 20.6 versus 0 for Ray Serve.
Choose ClearML if…
- You want the brand AI engines rank higher (20.6 vs 0)
- You need a self-hosted or on-premises option
- open-source flexibility
- transparent, clear pricing with a free self-hosted tier
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 | ClearML | Ray Serve |
|---|---|---|
| Visibility score | 20.6% | 0% |
| Change vs last week | New | New |
| Rank in MLOps Platforms | #8 | #61 |
| Share of voice | 4.5% | 0% |
| Mentions this run | 30 | 0 |
| Positive in AI answers | 43% | — |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
ClearML 0pp · Ray Serve 0pp over 0 weekly runs.
Engine by engine
ClearML leads on 3 of 3 engines
ChatGPT
AI Mode
Perplexity
ClearMLRay Serve
Head to head in AI answers
0 engine answers name both brands
0ClearML ranked higher
0Ray Serve ranked higher
Features, pricing & fit
| Dimension | ClearML | Ray Serve |
|---|---|---|
| Pricing model | Per seat | — |
| Entry price | Free; paid from $15 per seat / mo | — |
| Free plan | Yes | — |
| Deployment | Hosted servers, self-hosted, or a managed service in a VPC, on-premises (including air-gapped) or hybrid. | Self-managed on a laptop, in a Docker container, on a managed Ray cluster, or on Kubernetes via KubeRay |
| Compliance | — | SOC 2 Type II, ISO/IEC 27001 |
| Support | Dedicated Slack channel (Scale), Standard SLA (Scale), White-glove support with custom SLA (Enterprise), Professional services (Enterprise) | Ray Community Slack, Hands-on training and expert support from the Anyscale team that created Ray |
| Founded | 2016 | — |
| Headquarters | Tel Aviv, Israel | San Francisco, California |
| Employees | 11–50 | — |
| Funding | Series B (reported for parent Allegro AI, unverified, led by New Enterprise Associates) · $16M raised | Series C |
| Best for | Individuals, researchers and small teams on the free plan, growing AI teams on Pro, and enterprises running GPU clusters for multiple large projects. | Python teams serving machine learning models, including LLMs, as online inference APIs that need independent scaling |
Plans side by side
ClearML
Community$0month
Pro$15user/month + usage
ScaleCustomcustom quote
EnterpriseCustomcustom quote
Ray Serve
Pricing not available.
Pros and cons
Themes from the AI answers that mention each brand
ClearML
- open-source flexibility
- transparent, clear pricing with a free self-hosted tier
- end-to-end MLOps coverage in one platform
- GPU orchestration and pipeline automation
- affordable choice for cost-conscious teams
Ray Serve
Profile in progress.
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “ClearML vs Ray Serve”, https://slateindex.ai/compare/clearml-vs-ray-serve, data of Nov 2, 2026. Licence: CC BY 4.0.