Arize vs Feast

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

The short answer

Feast is the stronger AI pick in MLOps Platforms: it has a visibility score of 2 versus 1.5 for Arize, and ranks ahead in 100% of the answers that name both.

Choose Arize if…

You need a self-hosted or on-premises option
You need SOC 2 Type II or ISO 27001 or PCI DSS or HIPAA or GDPR coverage

Choose Feast if…

You want the brand AI engines rank higher (2 vs 1.5)
AI answers describe it more positively (50% vs 20% positive)
Connects with data lakes and warehouses for consistent data retrieval
Open-source, portable foundation option

AI visibility side by side

Latest weekly run · Nov 2

Metric	Arize	Feast
Visibility score	1.5%	2%
Change vs last week	New	New
Rank in MLOps Platforms	#34	#29
Share of voice	0.7%	0.6%
Mentions this run	5	4
Positive in AI answers	20%	50%

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

Visibility score trend

Weekly runs over the last 60 days

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

Engine by engine

Feast leads on 2 of 3 engines

ChatGPT

2.8%

1.9%

AI Mode

0.9%

3.1%

Perplexity

0.9%

ArizeFeast

Head to head in AI answers

3 engine answers name both brands

0Arize ranked higher

3Feast ranked higher

Prompt	Engine	Arize	Feast
What should an MLOps platform integrate with?	ChatGPT	#18	#8
What should an MLOps platform integrate with?	AI Mode	#13	#2
What are the best MLOps Platforms tools in 2026?	ChatGPT	#17	#13

Features, pricing & fit

Dimension	Arize	Feast
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-hosted; Kubernetes, and AWS Lambda (alpha)
Integrations	40+	—
Compliance	SOC 2 Type II, ISO 27001, PCI DSS, HIPAA, GDPR	—
Support	Community, Email, Dedicated support on Enterprise	Community Slack, Documentation
Founded	2020	2018
Employees	51–200	—
Funding	Series B-1 extension · $131M raised	—
Best for	AI engineering teams building and running production AI agents and LLM applications who need observability and evaluations.	Machine learning and AI teams that need the same feature definitions for training and inference.