The Neptune Shutdown:
We Have a Solution for You.

Neptune just announced the shutdown of its services.

Let us tell you why this might actually be a good thing. We have a very good deal for you.

We will help you save lots of time and money.

Drop-in Replacement: 1 Line of Code

We have a plug-in replacement for you. Same APIs. Just change 1 line and you're done.

Start today.

import neptune
import neptune_scale

import minfx.neptune_v2 as neptune
import minfx.neptune_v3 as neptune_scale

Data Migration

We will automatically migrate the data for you.

You don't have to do anything. Use all the features you're used to.

Minfx is Your Ideal Solution if ...

You are using machine learning in:

• Leadearboard-style situation, trying to make the best model, and/or
• You are doing continuous production retraining.

We are particularly aiming to optimize UI to make these as fast and easy as possible.

Expertise

We are experts in low-level optimization, both on backend and frontend, and care very much about performance and great experience. We have been building backend for a Neptune alternative, for a few months prior to Neptune's shutdown. And now we have a push when we want to make it available to you soon.

Frequently Asked Questions:

What will we get extra compared to Neptune?

We have the same API as Neptune. So you can use your existing code with Minfx.

Today, we already offer:

• Multi-backend support for extra reliability and redundancy. You can use Minfx at the same time with Neptune to try it out.
• Very high performance UI, running at >60 FPS. Check out the demo to see for yourself.
• Sophisticated system for experiment comparisons. Easily compare between multiple sets of baseline and candidate experiments.
• Share easily anything you see in the app. We track full state of UI in the URL.
• Besides logging, you can also download the data for any metric, run, etc. using the API.

Additionally, we will have many extra features.

When will be Minfx ready?

Our service is ready! You can start using it today.

Why is the UI ugly?

At the moment, we're prioritizing core features, performance, and reliability over visual polish. A beautiful interface doesn't mean much if the underlying system is slow or unreliable. We fully intend to deliver a refined, high-quality UI, but it's a secondary focus for now. Our current expectation is to reach that stage by mid-April.

We welcome your feedback! Please reach out to us via e-mail or our Discord (you can find the link in the demo footer).

The reason why it's a bit harder for us to make great UI is because we are using technologies where it's not as easy to make it pretty as just styling in CSS. We are actually sponsoring ongoing open-source development!

What about other providers?

Well, there's not really much out there that can ingest data at the scale and speed needed for ML experiments we know of:

WandB: In our direct experience, long time ago they used to be a good choice, but now just don't care anymore. They were acquired by Coreweave, primarily in a move to get more customers for their cloud services. They recently redesigned their UI and somehow made it worse. Their servers now often return internal 500 errors. One word: decay.

Neptune, but that's not a choice anymore.

Then there's bunch of open source stuff, that you have to self-host to get any reasonable performance and deal with all the headaches. Such as: MLFlow, AIM, LabML, etc. And of course deal with migrations, rewriting your code, etc., all in the span of 3 months.

Why not just do it internally ourselves? LLMs are good these days.

Yes, they are for starting greenfield projects. But the complexity of the spaghetti code and lack of team's understanding of the code will bite you later on. There are "fun" things to deal with, such as:

• Setting up new infrastructure
• Corrupted data or data loss
• Hitting cloud rate limits
• Monitoring, alerting and detecting failures
• Cascading failures: What good is your ML training if your tracking layer fails?
• New inter-team coordination overhead
• and so on...

When these happen, what will you do? Do you have the people who are domain experts who can then dive in and fix the mess? This decision will cost you best-case tens of thousands of dollars.

Do you have SOC2 certification?

No, we don't have SOC2 certification yet, but we will be working on it soon.

Who Are We?

Our team includes experienced ML engineers from Tower Research Capital (algo-trading), processing petabytes of data at scale, as well as one of the founders of DataChine (real-time data processing), with deep expertise in building high-performance data systems and a strong focus on low-level optimization.

We also bring strong academic and research credentials, including a PhD in Machine Learning and a research internship at DeepMind.

Progress

We are hiring!

Even though we have enough engineering resources, we are happy to talk with talented people to join our team.

If you stumbled upon this page and you ❤️ what we're building, reach out to us. Find the right e-mail address 🧩 to send your application. In the subject line write "Crate: <your-crate-published-on-crates.io-here>". This is a strong requirement, without it we won't consider your application.