Feast vs ZenML

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

The short answer

ZenML is the stronger AI pick in MLOps Platforms: it has a visibility score of 6 versus 2 for Feast, and ranks ahead in 100% of the answers that name both.

Choose Feast if…

Connects with data lakes and warehouses for consistent data retrieval
Open-source, portable foundation option

Choose ZenML if…

You want the brand AI engines rank higher (6 vs 2)
AI answers describe it more positively (55% vs 50% positive)
You need SOC 2 Type II or ISO 27001 coverage
Free and fully transparent open-source option

AI visibility side by side

Latest weekly run · Nov 2

Metric	Feast	ZenML
Visibility score	2%	6%
Change vs last week	New	New
Rank in MLOps Platforms	#29	#14
Share of voice	0.6%	1.5%
Mentions this run	4	11
Positive in AI answers	50%	55%

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

Visibility score trend

Weekly runs over the last 60 days

Feast 0pp · ZenML 0pp over 0 weekly runs.

Engine by engine

ZenML leads on 2 of 3 engines

ChatGPT

1.9%

11.7%

AI Mode

3.1%

6.3%

Perplexity

0.9%

FeastZenML

Head to head in AI answers

1 engine answers name both brands

0Feast ranked higher

1ZenML ranked higher

Prompt	Engine	Feast	ZenML
What are the best MLOps Platforms tools in 2026?	ChatGPT	#13	#9

Features, pricing & fit

Dimension	Feast	ZenML
Pricing model	—	Flat tiers
Entry price	—	Free; paid from $39 / mo
Free plan	—	Yes
Deployment	Self-hosted; Kubernetes, and AWS Lambda (alpha)	Self-hosted open source, or ZenML's cloud. Enterprise runs on-prem or air-gapped in the customer's cloud, with data and compute staying in the customer's VPC.
Integrations	—	66+
Compliance	—	SOC 2 Type II, ISO 27001
Support	Community Slack, Documentation	Community Slack, Email, Dedicated support with SLA on Enterprise, Workshops and architecture reviews on Enterprise
Founded	2018	2021
Headquarters	—	Munich, Germany
Employees	—	11–50
Funding	—	Seed extension · $6M raised
Best for	Machine learning and AI teams that need the same feature definitions for training and inference.	Data science and ML engineering teams that want pipelines and AI agents run on their own infrastructure, and agent teams that want to find the evals they are missing.