Decision comparison
MLflow vs Neptune.ai
Neptune.ai is no longer available. OpenAI announced its acquisition of Neptune in December 2025, and Neptune shut down its hosted service permanently on March 5, 2026. All external user data was deleted, so former users have nothing left to export or migrate and new teams cannot sign up. This page is therefore not a choice between two active products: MLflow is the only one of the two that a team can adopt today, and the Neptune.ai descriptions on this page are historical context. Neptune carved out a specialized niche in foundation model training monitoring, offering deep visibility into months-long training runs with rapid metric comparison across thousands of experiments. MLflow is the most widely adopted open-source AI engineering platform, covering the full lifecycle from experiment tracking through model deployment and LLM observability. MLflow continues to grow as a community-driven, vendor-neutral platform with 30 million monthly downloads and Linux Foundation backing. For teams that need experiment tracking today, MLflow is the clear choice: it is free under Apache 2.0, self-hosted, and it covers run logging, run comparison and experiment search. Neptune's training-monitoring strengths mattered to a narrow audience of frontier research teams, and OpenAI said it would fold that technology into its internal training stack rather than sell it to outside teams.
Neptune.ai is no longer available as an active product
Neptune.ai shut down its hosted service permanently on March 5, 2026 following its acquisition by OpenAI, and all external user data was deleted. There is nothing left to export or migrate. Treat this page as historical context rather than a current buying page.
Active alternatives to evaluate
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are experiment tracking.
Quick Comparison
| Decision factor | MLflow | Neptune.ai |
|---|---|---|
| Primary Focus | Full-lifecycle AI engineering platform covering tracking, evaluation, deployment, and observability | Was an experiment tracking and training monitoring service specialized for foundation model development; discontinued on March 5, 2026 |
| Deployment Model | Open-source, self-hosted; backed by Linux Foundation with 30M+ monthly downloads | Was a managed service. OpenAI announced the acquisition in December 2025 and shut the hosted service down permanently on March 5, 2026; all external user data was deleted |
| Experiment Tracking | General-purpose tracking for ML experiments, LLM traces, and agent workflows | Historically specialized for massive-scale training runs with thousands of metrics and multi-step branches; no longer available |
| LLM/Agent Support | Built-in observability, prompt management, AI Gateway, and Agent Server for production deployment | Was focused on training-phase visibility rather than inference or agent deployment; the service no longer exists |
| Cost and Availability | Free and open-source under Apache 2.0; no paid tiers | No longer sold. Neptune.ai shut down its hosted service permanently on March 5, 2026, after its acquisition by OpenAI, and all external user data was deleted. There is no plan to buy, no account to open and nothing left to export or migrate. Former enterprise prices are not a current option. |
| Best For | Teams needing an end-to-end open-source platform for ML and LLM lifecycle management | No longer available to anyone. Historically it suited research teams training large foundation models that needed deep training run analysis. |
MLflow
- Primary Focus:
- Full-lifecycle AI engineering platform covering tracking, evaluation, deployment, and observability
- Deployment Model:
- Open-source, self-hosted; backed by Linux Foundation with 30M+ monthly downloads
- Experiment Tracking:
- General-purpose tracking for ML experiments, LLM traces, and agent workflows
- LLM/Agent Support:
- Built-in observability, prompt management, AI Gateway, and Agent Server for production deployment
- Cost and Availability:
- Free and open-source under Apache 2.0; no paid tiers
- Best For:
- Teams needing an end-to-end open-source platform for ML and LLM lifecycle management
Neptune.ai
- Primary Focus:
- Was an experiment tracking and training monitoring service specialized for foundation model development; discontinued on March 5, 2026
- Deployment Model:
- Was a managed service. OpenAI announced the acquisition in December 2025 and shut the hosted service down permanently on March 5, 2026; all external user data was deleted
- Experiment Tracking:
- Historically specialized for massive-scale training runs with thousands of metrics and multi-step branches; no longer available
- LLM/Agent Support:
- Was focused on training-phase visibility rather than inference or agent deployment; the service no longer exists
- Cost and Availability:
- No longer sold. Neptune.ai shut down its hosted service permanently on March 5, 2026, after its acquisition by OpenAI, and all external user data was deleted. There is no plan to buy, no account to open and nothing left to export or migrate. Former enterprise prices are not a current option.
- Best For:
- No longer available to anyone. Historically it suited research teams training large foundation models that needed deep training run analysis.
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | MLflow | Neptune.ai |
|---|---|---|
| GitHub commits, 90d(Product adoption) | 1.5k | Not available |
| GitHub stars(Product adoption) | 28,000+ | Not available |
| Search interest(Market interest) | 2 | 0 |
| Hacker News mentions, 90d(Community interest) | 4 | Not available |
| PyPI weekly downloads(Product adoption) | 5.0M | Not available |
| Stack Overflow questions(Community interest) | 771 | 20 |
| GitHub commits, 90d(Developer adoption) | Not available | 0 |
| GitHub stars(Developer adoption) | Not available | 16 |
| Product Hunt comments(Community interest) | Not available | 0 |
| Product Hunt reviews(Community interest) | Not available | 0 |
| Product Hunt votes(Community interest) | Not available | 6 |
| PyPI weekly downloads(Developer adoption) | Not available | 22.5k |
As of November 9, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
MLflow
November 9, 2026Package vulnerabilities
PyPI · mlflow@3.18.0
0 vulnerabilities
across 1 package
Repository security score
Not available
Neptune.ai
November 9, 2026Package vulnerabilities
PyPI · neptune@1.14.0.post2
0 vulnerabilities
across 1 package
Repository security score
Not available
Interface Preview
MLflow
Neptune.ai
Feature Comparison
| Feature | MLflow | Neptune.ai |
|---|---|---|
| Experiment Tracking | ||
| Run Logging | Logs parameters, metrics, artifacts, and models with autologging support for 100+ frameworks | Logged parameters, metrics, and artifacts with a focus on large-scale training workflows |
| Training Run Visualization | Built-in UI for comparing runs, viewing metrics, and inspecting artifacts | Offered specialized visualization for comparing thousands of metrics across months-long training runs |
| Search and Filtering | Query-based search across experiments with tag and parameter filtering | Offered high-speed filtering and search designed for massive training datasets |
| LLM and Agent Capabilities | ||
| LLM Observability | Full trace capture for LLM applications and agents built on OpenTelemetry | Was not a core capability; Neptune focused on training-phase monitoring rather than inference |
| Prompt Management | Prompt versioning, testing, deployment, and automated optimization | Was not offered as a standalone feature |
| Agent Deployment | Agent Server with FastAPI hosting, streaming support, and built-in tracing | Was not offered; Neptune focused on experiment tracking, not production serving |
| Model Management | ||
| Model Registry | Central model registry with versioning, stage transitions, and deployment workflows | Tracked model metadata within experiment runs; had no dedicated registry |
| Model Evaluation | 50+ built-in metrics and LLM judges with systematic regression detection | Compared metrics across training runs; evaluation focused on training quality |
| Model Deployment | Built-in deployment tools for packaging and serving models to production | Was not a core capability; Neptune tracked experiments but did not handle deployment |
| Infrastructure and Integration | ||
| Framework Integrations | 100+ integrations including LangChain, OpenAI, PyTorch, and supports Python, TypeScript, Java, and R | Had integrations with major ML frameworks for training experiment logging |
| API Gateway | Unified AI Gateway for LLM providers with routing, rate limits, fallbacks, and cost controls | Was not offered; Neptune did not include API gateway or routing capabilities |
| Open Source | Fully open-source under Apache 2.0 with 20K+ GitHub stars and 900+ contributors | Was a proprietary managed service, not open-source; the hosted service shut down on March 5, 2026 |
| Scale and Performance | ||
| Large-Scale Training Monitoring | Handles enterprise-scale tracking; battle-tested by Fortune 500 companies | Was purpose-built for months-long foundation model training with multi-step branching |
| Metric Throughput | Scales with self-hosted infrastructure; performance depends on deployment setup | Was optimized for visualizing and comparing thousands of metrics in seconds |
| Community and Support | 20K+ GitHub stars, 900+ contributors, active Slack community, Linux Foundation backing | No support or roadmap: the hosted service shut down permanently on March 5, 2026, after the OpenAI acquisition |
Experiment Tracking
Run Logging
Training Run Visualization
Search and Filtering
LLM and Agent Capabilities
LLM Observability
Prompt Management
Agent Deployment
Model Management
Model Registry
Model Evaluation
Model Deployment
Infrastructure and Integration
Framework Integrations
API Gateway
Open Source
Scale and Performance
Large-Scale Training Monitoring
Metric Throughput
Community and Support
Which to choose
Neptune.ai is no longer available. OpenAI announced its acquisition of Neptune in December 2025, and Neptune shut down its hosted service permanently on March 5, 2026. All external user data was deleted, so former users have nothing left to export or migrate and new teams cannot sign up. This page is therefore not a choice between two active products: MLflow is the only one of the two that a team can adopt today, and the Neptune.ai descriptions on this page are historical context. Neptune carved out a specialized niche in foundation model training monitoring, offering deep visibility into months-long training runs with rapid metric comparison across thousands of experiments. MLflow is the most widely adopted open-source AI engineering platform, covering the full lifecycle from experiment tracking through model deployment and LLM observability. MLflow continues to grow as a community-driven, vendor-neutral platform with 30 million monthly downloads and Linux Foundation backing. For teams that need experiment tracking today, MLflow is the clear choice: it is free under Apache 2.0, self-hosted, and it covers run logging, run comparison and experiment search. Neptune's training-monitoring strengths mattered to a narrow audience of frontier research teams, and OpenAI said it would fold that technology into its internal training stack rather than sell it to outside teams.
Best-fit scenarios
Choose MLflow if:
Choose MLflow for virtually any ML or LLM workflow. It delivers experiment tracking, a model registry, production deployment tools, LLM observability, prompt management, an AI Gateway, and an Agent Server in a single open-source platform. With 30 million monthly downloads, 100+ framework integrations, and zero licensing cost under Apache 2.0, MLflow is the default choice for teams of all sizes. Its active community of 900+ contributors and Linux Foundation backing ensure long-term stability and rapid feature development.
Neptune.ai (no longer available):
Neptune.ai cannot be chosen today. Its hosted service shut down permanently on March 5, 2026, after OpenAI acquired the company, and all external user data was deleted, so there is no account to open and no project history to export or migrate. Historically, Neptune was the stronger option for research teams training large foundation models: it monitored months-long runs with multi-step branching and compared thousands of metrics in seconds. Teams with that need should choose MLflow instead. MLflow logs parameters, metrics, artifacts and models with autologging for 100+ frameworks, compares runs in its built-in UI, and searches experiments by tag and parameter. It is free under Apache 2.0 and self-hosted, so its performance at very large metric volumes depends on the infrastructure you run it on; size that deployment for your own run counts before you commit a long training program to it. What you give up is Neptune's purpose-built speed for comparing thousands of metrics; what you gain is a maintained open-source platform whose Apache 2.0 code you host yourself.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
What is the main difference between MLflow and Neptune.ai?
MLflow is a full-lifecycle open-source AI engineering platform that covers experiment tracking, model registry, deployment, LLM observability, prompt management, and an agent server. Neptune.ai was a specialized experiment tracker built specifically for monitoring foundation model training. MLflow gives you breadth across the entire ML and LLM lifecycle, while Neptune was designed for depth in the training phase of large-scale model development. Today the deciding difference is availability: MLflow is free and self-hosted under Apache 2.0, while Neptune was an enterprise managed service that shut down permanently on March 5, 2026, after its acquisition by OpenAI.
Is Neptune.ai still available after the OpenAI acquisition?
No. OpenAI announced its acquisition of Neptune.ai in December 2025, and Neptune shut down its hosted service permanently on March 5, 2026. All external user data was deleted, so former customers have nothing left to export or migrate, and new teams cannot sign up. OpenAI's Chief Scientist Jakub Pachocki stated plans to integrate Neptune's tools deep into OpenAI's training stack to expand visibility into how models learn; that work serves OpenAI's own research, and the product is not offered to outside teams. Teams that need experiment tracking now should evaluate MLflow, which covers run logging, run comparison and experiment search under the Apache 2.0 license.
Can MLflow handle the same scale of training run monitoring as Neptune.ai?
MLflow handles enterprise-scale experiment tracking and is battle-tested by Fortune 500 companies with over 30 million monthly package downloads. Neptune.ai was purpose-built for months-long foundation model training runs where researchers compared thousands of runs, analyzed metrics across layers, and monitored complex branched training workflows. That specialization no longer decides anything: Neptune's hosted service shut down on March 5, 2026, and its user data was deleted. For standard ML and LLM experiment tracking, MLflow provides more than sufficient scale. For frontier-scale training, MLflow's throughput depends on the self-hosted infrastructure behind it, so load-test your MLflow deployment with your own metric volume before a long training run.
What LLM and agent capabilities does MLflow offer that Neptune.ai does not?
MLflow provides a comprehensive LLM and agent toolkit that Neptune.ai did not match. This includes OpenTelemetry-based observability for capturing full traces of LLM applications, prompt versioning and automated optimization, an AI Gateway for unified API routing across LLM providers with rate limits and cost controls, and an Agent Server for deploying agents to production with a single command. MLflow also offers an evaluation framework with 50+ built-in metrics and LLM judges. Neptune.ai was focused exclusively on the training phase and did not provide inference-time observability, prompt management, or production deployment tools.
How much does MLflow cost, and can Neptune.ai still be bought?
Neptune.ai can no longer be bought. Neptune.ai shut down its hosted service permanently on March 5, 2026, after its acquisition by OpenAI, and all external user data was deleted. There is no current Neptune price, and former enterprise prices are not a buying option. MLflow has no license cost: it is open source under Apache 2.0 and self-hosted for free, with no paid tiers. The real cost of MLflow is the infrastructure that runs its tracking server and stores artifacts, plus the engineering time to operate and upgrade it.