H2O.ai vs Valohai

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

The short answer

H2O.ai is the stronger AI pick in MLOps Platforms: it has a visibility score of 2.2 versus 1.9 for Valohai, and ranks ahead in 100% of the answers that name both.

Choose H2O.ai if…

You want the brand AI engines rank higher (2.2 vs 1.9)
AI answers describe it more positively (29% vs 20% positive)
You need HIPAA or FedRAMP High coverage
You prefer a larger, established vendor (201–500 employees)

Choose Valohai if…

You need a self-hosted or on-premises option
14-day free trial

AI visibility side by side

Latest weekly run · Nov 2

Metric	H2O.ai	Valohai
Visibility score	2.2%	1.9%
Change vs last week	New	New
Rank in MLOps Platforms	#27	#30
Share of voice	1%	0.7%
Mentions this run	7	5
Positive in AI answers	29%	20%

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

Visibility score trend

Weekly runs over the last 60 days

H2O.ai 0pp · Valohai 0pp over 0 weekly runs.

Engine by engine

H2O.ai leads on 2 of 3 engines

ChatGPT

2.8%

2.2%

AI Mode

1.9%

Perplexity

1.9%

3.5%

H2O.aiValohai

Head to head in AI answers

1 engine answers name both brands

1H2O.ai ranked higher

0Valohai ranked higher

Prompt	Engine	H2O.ai	Valohai
Official website of the top MLOps platform vendors	ChatGPT	#11	#13

Features, pricing & fit

Dimension	H2O.ai	Valohai
Pricing model	—	Per seat
Entry price	—	Contact sales
Free plan	—	No · 14-day trial
Deployment	On-premises, fully air-gapped, private cloud, or public cloud	Managed cloud, on-premises, private cloud or air-gapped environments, on the customer's own cloud infrastructure
Compliance	SOC 2 Type II, HIPAA, FedRAMP High	SOC 2 Type II
Support	Forward Deployed Engineers embedded on-site	Technical support from the Customer Engineering team, Valohai Training academy, Training and onboarding resources
Founded	2012	2016
Headquarters	Mountain View, California, United States	San Francisco, California, US
Employees	201–500	11–50
Funding	Series E · $250M raised	Seed · $2M raised
Best for	Regulated enterprises and public-sector agencies that need predictive, generative and agentic AI on their own infrastructure, including air-gapped deployments.	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