Arize vs Valohai

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

The short answer

Valohai is the stronger AI pick in MLOps Platforms: it has a visibility score of 1.9 versus 1.5 for Arize, and ranks ahead in 100% of the answers that name both.

Choose Arize if…

You want a free plan to start
You need ISO 27001 or PCI DSS or HIPAA or GDPR coverage
You prefer a larger, established vendor (51–200 employees)
Flat tiers pricing suits your budget better than per seat

Choose Valohai if…

You want the brand AI engines rank higher (1.9 vs 1.5)
Per seat pricing suits your budget better than flat tiers
14-day free trial

AI visibility side by side

Latest weekly run · Nov 2

Metric	Arize	Valohai
Visibility score	1.5%	1.9%
Change vs last week	New	New
Rank in MLOps Platforms	#34	#30
Share of voice	0.7%	0.7%
Mentions this run	5	5
Positive in AI answers	20%	20%

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

Visibility score trend

Weekly runs over the last 60 days

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

Engine by engine

Valohai leads on 1 of 3 engines

ChatGPT

2.8%

2.2%

AI Mode

0.9%

Perplexity

0.9%

3.5%

ArizeValohai

Head to head in AI answers

1 engine answers name both brands

0Arize ranked higher

1Valohai ranked higher

Prompt	Engine	Arize	Valohai
Alternatives to the most popular MLOps platform	Perplexity	#20	#13

Features, pricing & fit

Dimension	Arize	Valohai
Pricing model	Flat tiers	Per seat
Entry price	Free; paid from $50 / mo	Contact sales
Free plan	Yes	No · 14-day trial
Deployment	SaaS, with self-hosted deployment on the Enterprise plan; Phoenix runs locally or self-hosted.	Managed cloud, on-premises, private cloud or air-gapped environments, on the customer's own cloud infrastructure
Integrations	40+	—
Compliance	SOC 2 Type II, ISO 27001, PCI DSS, HIPAA, GDPR	SOC 2 Type II
Support	Community, Email, Dedicated support on Enterprise	Technical support from the Customer Engineering team, Valohai Training academy, Training and onboarding resources
Founded	2020	2016
Headquarters	—	San Francisco, California, US
Employees	51–200	11–50
Funding	Series B-1 extension · $131M raised	Seed · $2M raised
Best for	AI engineering teams building and running production AI agents and LLM applications who need observability and evaluations.	AI product and ML teams that combine LLMs with specialized models and need to run the work on their own cloud or on-premises infrastructure