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Platform & Infra Engineers

Reliable experiment tracker for foundation model scale

With Neptune, researchers get the features they ask for — real-time monitoring, fine-grained logging, instant debugging, and shareable reports — while platform teams get high availability, 1M+ data points/sec throughput, flexible APIs, and dedicated SREs.

Used by top teams training foundation models
icon The challenge

Researchers want snappy, tailored UI. You need uptime and throughput. Delivering both is hard when researchers log at scale.

You need to turn complex needs into self-serve, tailored tools researchers actually use. Or they’ll ignore them.
You have to support logging 1000s of metrics from day- to months-long jobs without infra slowing down under load.
You need logs to survive failures & downtimes. Reliability isn’t an option & neither is self-hosting for many teams.
icon The solution

Neptune bridges what researchers expect and what infra requires through a flexible tracker built for large models

Researchers first

Built for the way AI researchers work on foundation models

From quick ablations to 100+ GPU pretraining jobs, researchers can iterate and debug fast, without fighting the tracker. No lagging performance and unnecessary complexity.

Neptune provides researchers with all the functionality they care about:

  • Live monitoring for long, multi-week experiments
  • Fine-grained logging of metrics, parameters, and model internals
  • Easy experiment comparison
  • Fast logs search for context and debugging
  • Forking and versioning to track branches and baselines
  • Flexible Python API and integrations
Send them Neptune demo
Scales with you

Ready to handle the scale of foundation model training

Add jobs, GPUs, and teams. Neptune keeps ingest latency low and performance steady even as everything else scales. No slowdowns, no bottlenecks, no schema rewrites when your org grows.

Neptune is architected for high-throughput, distributed workloads:

  • Horizontally scalable architecture — scale ingest, storage, and processing independently
  • Kafka-based ingest pipeline handles over 1 million data points per second
  • Sharded ClickHouse enables low-latency queries across massive datasets
  • Tiered storage keeps recent data fast on SSDs and archives historical data on HDDs
Review speed on 100M+ points example
High availability by design

Resilient, observable, and deployable on your terms

When researchers are running multi-week training across hundreds of nodes, reliability is your safety net. With Neptune, you have fewer 3 a.m. incidents, and no trade-offs when choosing how you deploy.

Self-hosted version

for secure environments

Multi-AZ deployment

for high availability

RBAC and SSO

for access control

24/7 support

with on-call SREs and a dedicated
support Slack channel

Automated backups

with point-in-time restore

Monitoring & observability

with pre-configured Grafana
dashboards and alerts

Our customers

See how companies like yours extend their platform capabilities with Neptune

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Clément Giron
Clément Giron Tech Lead & R&D Data Scientist at Kayrros
Speed, accuracy and reliability are of the essence. That’s what we like about Neptune. Its lightweight SDK seamlessly integrates with our machine learning workflows, enabling us to effortlessly track artifacts and monitor model performance metrics and empowering our team to iterate rapidly, ensuring repeatable and reliable results.
Olivier Lammas
Olivier Lammas Founding Engineer at Navier AI
A tracker is deeply integrated into all of our everyday workflows. It’s being used almost every hour of the day. Neptune hasn’t been down on any of those checks. We’re not fighting it. It works great, it’s fast, it’s reliable, and it is designed for foundation model training. We’re very happy we made the switch.

Give your researchers a tracker they’ll love and infra you can trust