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Neptune.ai costs $150 to $250 per month as of July 2026, with 3 plans available. Plans: Startup at $150/month, and Lab at $250/month. Enterprise pricing is available on request. Pricing depends on your chosen tier, contract length, and negotiated discounts.

Use the interactive pricing calculator to estimate your exact cost based on team size and requirements.

  • Free tier: No free tier available

Neptune.ai offers 3 pricing tiers: Startup, Lab, Self-Hosted. Paid plans include Startup at $150/per user/month, Lab at $250/per user/month. The Lab plan is large ml research labs training large-scale foundation models.

Last verified: | Sources: 1 pricing pages and reviews
+70% above list price
Know Before You Buy — See Neptune.ai Plans

Neptune.ai true cost runs 70% above the listed $150-$250/month price as of July 2026. For a 25-person team, expect ~$127,500 in year-one costs vs the $75,000 base license. Key hidden costs: data preparation, integration engineering, human capital/talent premiums. Verified from 1 sources by CostBench.

Hidden Costs Breakdown

1

Data Preparation

critical implementation

This involves cleaning, normalizing, labeling, annotating, ensuring privacy compliance, and constructing data pipelines.

industry

It involves cleaning, normalizing, labeling, annotating, and ensuring privacy compliance for data, as well as constructing data pipelines.

2

Integration Engineering

high implementation

Connecting MLOps platforms with existing workflows, data sources, and output channels often requires significant custom development.

industry

Integration Engineering: Connecting MLOps platforms with existing workflows, data sources, and output channels often requires significant custom development, accounting for 15-25% of hidden costs.

3

Human Capital/Talent Premiums

critical support

Salaries for specialized ML engineers, DevOps specialists, and MLOps experts needed to set up and maintain an MLOps platform can be the largest and most underestimated expense.

industry

Human Capital/Talent Premiums: The salaries for specialized ML engineers, DevOps specialists, and MLOps experts needed to set up and maintain an MLOps platform can be the largest and most underestimated expense, often exceeding hardware costs.

4

Ongoing Maintenance and Retraining

high support

ML models degrade over time due to data and concept drift, requiring manual monitoring, retraining, and data validation.

industry

The accumulated hidden costs for manual monitoring, retraining, and data validation can be substantial, with one example showing $120,000 in the first year alone for a system with an initial development cost of $75,000.

5

Operational Overhead

medium overage

This includes expenses like network bandwidth, security, and general maintenance.

industry

Operational Overhead: This includes expenses like network bandwidth, security, and general maintenance.

6

Idle Infrastructure and Resource Waste

high overage

Unnoticed idle AI endpoints and low GPU utilization for AI inference workloads lead to significant waste.

industry

GPU utilization for AI inference workloads often hovers around 20-40%, leading to significant waste.

7

Compliance and Governance

high compliance

Ensuring data privacy, decision traceability, and model unpredictability adds additional costs for regulated industries.

industry

Compliance and Governance: For regulated industries, ensuring data privacy, decision traceability, and model unpredictability adds additional costs, sometimes 10-15% for compliance and safety frameworks.

Example: True Cost for 25 Users

License (25 × $250 × 12) $75,000/yr
Data Preparation +30-40% of the total project effort and 15-35% of project costs
Integration Engineering +15-25% of hidden costs
Ongoing Maintenance and Retraining +15-30% of infrastructure costs annually; $120,000 in the first year alone for a system with an initial development cost of $75,000
Idle Infrastructure and Resource Waste +$500 and $23,000 monthly for idle AI endpoints; GPU utilization often around 20-40%
Compliance and Governance +10-15% for compliance and safety frameworks
Estimated Year 1 Total ~$127,500
That's roughly 1.7× the advertised license price.

Frequently Asked Questions

01 What hidden costs should I budget for with Neptune.ai?

Beyond the license fee, budget for: Data Preparation (30-40% of the total project effort and 15-35% of project costs); Integration Engineering (15-25% of hidden costs); Ongoing Maintenance and Retraining (15-30% of infrastructure costs annually; $120,000 in the first year alone for a system with an initial development cost of $75,000); Idle Infrastructure and Resource Waste ($500 and $23,000 monthly for idle AI endpoints; GPU utilization often around 20-40%); Compliance and Governance (10-15% for compliance and safety frameworks). Total ownership typically runs 70% higher than the listed price.

02 Does Neptune.ai charge for implementation?

Neptune.ai implementation is not included in the license cost. This involves cleaning, normalizing, labeling, annotating, ensuring privacy compliance, and constructing data pipelines.. Estimated impact: 30-40% of the total project effort and 15-35% of project costs.

03 How much does Neptune.ai support cost?

Salaries for specialized ML engineers, DevOps specialists, and MLOps experts needed to set up and maintain an MLOps platform can be the largest and most underestimated expense..

04 Are there overage or storage costs with Neptune.ai?

This includes expenses like network bandwidth, security, and general maintenance..

05 What add-ons cost extra with Neptune.ai?

Add-on pricing for Neptune.ai varies by feature. The sourced cost breakdown above lists any verified add-on costs we have.

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