DagsHub vs Evidently AI

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

The short answer

DagsHub is the stronger AI pick in MLOps Platforms: it has a visibility score of 3.5 versus 2.4 for Evidently AI, and ranks ahead in 100% of the answers that name both.

Choose DagsHub if…

You want the brand AI engines rank higher (3.5 vs 2.4)
You prefer a larger, established vendor (11–50 employees)
Structured annual pricing with yearly billing options on team tiers
Recommended as a starting option for MLOps lifecycle coverage

Choose Evidently AI if…

Listed as a monitoring option in vendor-neutral ML stacks
Named alongside Prometheus and Grafana in monitoring setups
Grouped with Arize and WhyLabs as a monitoring tool

AI visibility side by side

Latest weekly run · Nov 2

Metric	DagsHub	Evidently AI
Visibility score	3.5%	2.4%
Change vs last week	New	New
Rank in MLOps Platforms	#23	#25
Share of voice	0.6%	0.9%
Mentions this run	4	7
Positive in AI answers	—	50%

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

Visibility score trend

Weekly runs over the last 60 days

DagsHub 0pp · Evidently AI 0pp over 0 weekly runs.

Engine by engine

DagsHub leads on 2 of 3 engines

ChatGPT

3.1%

5.4%

AI Mode

3.1%

Perplexity

4.3%

1.9%

DagsHubEvidently AI

Head to head in AI answers

1 engine answers name both brands

1DagsHub ranked higher

0Evidently AI ranked higher

Prompt	Engine	DagsHub	Evidently AI
Alternatives to the most popular MLOps platform	Perplexity	#5	#19

Features, pricing & fit

Dimension	DagsHub	Evidently AI
Pricing model	Per seat	—
Entry price	Free; paid from $119 per seat / mo	—
Free plan	Yes	—
Deployment	Cloud hosting, with on-premise, VPC, and air-gapped installation on the Enterprise plan.	The open-source version runs on the customer's own infrastructure; Evidently Cloud runs on managed infrastructure.
Support	Community chat and email, Priority support on the Team plan, Enterprise SLA and support	—
Founded	2019	2020
Headquarters	San Francisco, California, United States	San Francisco, CA, United States
Employees	11–50	1–10
Funding	Accelerator/incubator deal	Seed · $1M raised
Best for	Data science and AI teams that manage multimodal datasets, experiments, and models, from individuals building small applications to enterprises running high-scale workloads.	AI and ML teams that want open-source evaluation, testing and monitoring for LLM applications and predictive models.