Flyte 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 0.9 for Flyte, and ranks ahead in 50% of the answers that name both.

Choose Flyte if…

AI answers describe it more positively (33% vs 20% positive)
You need SOC 2 Type I coverage
Recommended as an open-source, portable MLOps foundation
Named among specialized tools in vendor comparison lists

Choose Valohai if…

You want the brand AI engines rank higher (1.9 vs 0.9)
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AI visibility side by side

Latest weekly run · Nov 2

Metric	Flyte	Valohai
Visibility score	0.9%	1.9%
Change vs last week	New	New
Rank in MLOps Platforms	#39	#30
Share of voice	0.4%	0.7%
Mentions this run	3	5
Positive in AI answers	33%	20%

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

Visibility score trend

Weekly runs over the last 60 days

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

Engine by engine

Valohai leads on 3 of 3 engines

ChatGPT

0.9%

2.2%

AI Mode

Perplexity

1.9%

3.5%

FlyteValohai

Head to head in AI answers

2 engine answers name both brands

1Flyte ranked higher

1Valohai ranked higher

Prompt	Engine	Flyte	Valohai
Which MLOps Platforms vendors do analysts recommend?	Perplexity	#14	#15
Official website of the top MLOps platform vendors	ChatGPT	#20	#13

Features, pricing & fit

Dimension	Flyte	Valohai
Pricing model	—	Per seat
Entry price	—	Contact sales
Free plan	—	No · 14-day trial
Deployment	Open source, self-hosted by the user on their own Kubernetes cluster, or managed Enterprise Flyte on Union.ai, with the data plane in the customer's cloud.	Managed cloud, on-premises, private cloud or air-gapped environments, on the customer's own cloud infrastructure
Compliance	SOC 2 Type II, SOC 2 Type I	SOC 2 Type II
Support	—	Technical support from the Customer Engineering team, Valohai Training academy, Training and onboarding resources
Founded	—	2016
Headquarters	Bellevue, Washington	San Francisco, California, US
Employees	—	11–50
Funding	Series A extension (completed Feb 2026, $38.1M total) · $67M raised	Seed · $2M raised
Best for	Teams running training, serving and agent workloads that need automatic recovery from failures.	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