neptune.ai Reviews & Product Details

We use Neptune consistently to track hundreds of different experiments and it is very easy to take advantage of the features in place to corral those results into information that is digestible for other stakeholders, including those that are not in technical roles. It provides a consistent structure that ensures all our data is reliably and intuitively organized. Additionally, Neptune allows us to self-host the application which keeps our data secure and they have provided great support to keep us as up to date as possible. Review collected by and hosted on G2.com.

In some cases, offline logging behavior may diverge from the standard asynchronous behavior. For example, with on-prem servers that require offline logging and then to be synced from the host machine Review collected by and hosted on G2.com.

I’ve found Neptune pretty easy to integrate. Although I don’t use it in the traditional way, I rely on it to track experiment evaluations and monitor the performance of our LLM-based applications. The tool is flexible and adapts to most of all types of tracking needs. Recently, I've had the need to map API call statuses, including error codes, and the process was seamless and fast.

Another super positive aspect is the incredible customer support. We have regular meetings, and the team is always willing to listen to our requests and translate them into practical solutions. They consistently share valuable resources and try to meet our needs. Review collected by and hosted on G2.com.

the only downside for me is the lack of customizability in the front-end. I’d love to have the ability to build tailor-made dashboards that better suit my specific data and provide a more intuitive visualization experience for team members with less technical expertise Review collected by and hosted on G2.com.

Easy to set up, plenty of integration, useful collaboration tool. The team was quick to catch up and start tracking experiments, despite a bit of reservation before the integration. Currently, all experiments need to be in Neptune if they want to go production, allowing us to track and enforce quality. A definite win for the team, and for the company! Review collected by and hosted on G2.com.

Unable to share results with anyone without the license. Helping our partners to understand performance progress is a bit challenging. Review collected by and hosted on G2.com.

I've found Neptune to be a flexible, easy to use platform for tracking everything in our ML development process. The API is simple to set up and requires minimal code to track experiments. I've been loving the runs table, it makes it easy to group and filter experiments for quick comparisons.

Their support team has been great at responding to any questions and showing us new features as they are released. Review collected by and hosted on G2.com.

Dashboard visualizations automatically resize when you adjust the window size, meaning you need to resize and reorder the visualizations at that size. Would love if there was a way to fix the size of dashboards so this wouldn't happen. Review collected by and hosted on G2.com.

- Filter functionality on runs within a project

- Custom dashboards and saveable table schemas

- Reliably working on-prem deployment (GCP)

- Fast responded to questions and feature requests

- Logged content is organized under domains within runs Review collected by and hosted on G2.com.

- Grouping runs within a project is difficult, this makes usage for large teams challenging

- Plots look great already but miss some flexibility

- We can always have more advanced filter criteria :) Review collected by and hosted on G2.com.

Neptune Ai, allows to store practically any necessary data, based on its way of storing data and metadata, it allows complete traceability in a simple way.

It has an easy to use UI, which exposes, in a simple and useful way, practically all the necessary information, allowing some interesting customisations, such as being able to add/remove custom visualisations only with the necessary data/columns.

It also has a Python SDK which is quite easy to use, allowing to adapt to the use case in a quite flexible way, as Neptune provides an accessible data and metadata container, so integration with other tools is quite feasible.

On the support side, they are usually quite quick to respond and provide a lot of help. Another very interesting point is that the documentation is quite extensive and has a lot of examples. Review collected by and hosted on G2.com.

The great flexibility that Neptune AI provides as a container of data and metadata is both an advantage and a disadvantage, as it requires a good level of governance not to record too much information, or organise it in the right way, to be really useful. This is not a ‘problem’ of the tool itself, but it is something to be taken into account when using it in a use case of some complexity. Review collected by and hosted on G2.com.

The ease of use is super, just a couple of lines of code, and you are ready to track and log the metrics you want. Documentation and customer support are super efficient, and you can uploads tons of metrics. After a couple of connection issues (solved easily) we were able to integrate from different pipelines on azureML studio. Review collected by and hosted on G2.com.

We experienced some problems with connection stability and timeouts while uploading metrics, but everything was solved easily after a fast communication with customer support.

Also a slightly higher focus on LLM evaluation would be appreciated especially in these LLMs-centric days. Review collected by and hosted on G2.com.

Neptune just works for tracking experiments: it has nice and not overloaded UI that is quite fast (especially in the new version), you can track metrics and compare different runs. The team also provides great support and open to implementing new features on request. We use it for tracking all of our pretraining, post-training and evaluations. Review collected by and hosted on G2.com.

When you want to compare just final metrics of multiple runs as a table, that's something Neptune can do but the experience can be improved.

Also some features important to us are still missing, like being able to log steps of one run non-monotonically from different processes. Review collected by and hosted on G2.com.

Neptune helps track experiment data with a simple interface. The dashboard shows key metrics, graphs, and lets you compare different model runs side by side. Our team uses it to manage thousands of machine learning experiments, from initial training through fine-tuning and final evaluation. The system handles large amounts of data well and loads results quickly. You can organize experiments into projects, tag important runs, and easily search through past results. It also lets you log both training metrics and evaluation scores in one place. Review collected by and hosted on G2.com.

Overall, there's not much to dislike. Two helpful additions would be synced legends across multiple plots when hovering over a specific x-axis point, and the ability to automatically group related metrics together for better organization. Review collected by and hosted on G2.com.

The fact that I cane keep track of my experiments easily:

- They are all on one place

- I can compare between runs

- I can log nearly everything I want even when tuning with optuna I can see all the nice metadata and visulizations.

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A few minor things:

- The fact that everytime I visit the page of my projects the colors for each run are different. There is an option to change the color but I do not want to have to change the color of each run especially when I am tuning a model which results in hundreds of runs.

- The stderr and stdout logs are unreadable especially when I am using progress bars.

- The optuna visualizations in full screen mode are quite small, especially the contours. I would expect that entering a full screen mode they would adjust according to my screen size and resolution. Review collected by and hosted on G2.com.