Dataiku vs Ray Serve

Which one do AI engines recommend, and how do they differ on price, fit and features?

The short answer

Dataiku is the stronger AI pick in MLOps Platforms: it has a visibility score of 8.5 versus 0 for Ray Serve.

You want the brand AI engines rank higher (8.5 vs 0)
You need ISO 27001:2022 or ISO 27701:2019 or SOC 1 Type II or HIPAA or GxP or GDPR coverage
Bridges data scientists and business teams for collaboration
End-to-end governance and operationalization

Latest weekly run · Nov 2

The stronger value in each row is marked with a ✓.

Weekly runs over the last 60 days

Dataiku 0pp · Ray Serve 0pp over 0 weekly runs.

Dataiku leads on 3 of 3 engines

ChatGPT

12.2%

AI Mode

9.3%

Perplexity

3.9%

DataikuRay Serve

0 engine answers name both brands

0Dataiku ranked higher

0Ray Serve ranked higher

Dimension	Dataiku	Ray Serve
Deployment	Runs on the clouds, data platforms and models a customer already has	Self-managed on a laptop, in a Docker container, on a managed Ray cluster, or on Kubernetes via KubeRay
Compliance	ISO 27001:2022, ISO 27701:2019, SOC 1 Type II, SOC 2 Type II, HIPAA, GxP, GDPR	SOC 2 Type II, ISO/IEC 27001
Support	—	Ray Community Slack, Hands-on training and expert support from the Anyscale team that created Ray
Founded	2013	—
Headquarters	New York City, NY	San Francisco, California
Employees	1,001–5,000	—
Funding	Series E · $600M raised	Series C
Best for	Large enterprises, including highly regulated ones, where business experts and IT need to build AI together under central governance	Python teams serving machine learning models, including LLMs, as online inference APIs that need independent scaling