DagsHub vs Neptune.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 2.6 for Neptune.ai, and ranks ahead in 0% of the answers that name both.

Choose DagsHub if…

You want the brand AI engines rank higher (3.5 vs 2.6)
Structured annual pricing with yearly billing options on team tiers
Recommended as a starting option for MLOps lifecycle coverage

Choose Neptune.ai if…

You need a self-hosted or on-premises option
You prefer a larger, established vendor (51–200 employees)
Collaboration-first dashboard for tracking experiments
Lightweight metadata store built for experiment tracking

AI visibility side by side

Latest weekly run · Nov 2

Metric	DagsHub	Neptune.ai
Visibility score	3.5%	2.6%
Change vs last week	New	New
Rank in MLOps Platforms	#23	#24
Share of voice	0.6%	0.6%
Mentions this run	4	4
Positive in AI answers	—	25%

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

Visibility score trend

Weekly runs over the last 60 days

DagsHub 0pp · Neptune.ai 0pp over 0 weekly runs.

Engine by engine

DagsHub leads on 2 of 3 engines

ChatGPT

3.1%

0.9%

AI Mode

3.1%

6.8%

Perplexity

4.3%

DagsHubNeptune.ai

Head to head in AI answers

1 engine answers name both brands

0DagsHub ranked higher

1Neptune.ai ranked higher

Prompt	Engine	DagsHub	Neptune.ai
Which MLOps Platforms tools offer annual discounts?	AI Mode	#2	#1

Features, pricing & fit

Dimension	DagsHub	Neptune.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.	Hosted service by default, with a self-hosted option on-premises or on AWS, Google Cloud or Azure as a Kubernetes cluster, deployed as microservices via a Helm chart.
Integrations	—	25+
Support	Community chat and email, Priority support on the Team plan, Enterprise SLA and support	—
Founded	2019	2017
Headquarters	San Francisco, California, United States	Palo Alto, California, United States
Employees	11–50	51–200
Funding	Accelerator/incubator deal	Series A · $18M 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.	Teams training foundation models and other large-scale machine learning models that need to monitor GPT-scale training runs.