List of neptune.ai Customers
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United States
Since 2010, our global team of researchers has been studying neptune.ai customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased neptune.ai for MLOps Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using neptune.ai for MLOps Platforms include: Brainly Poland, a Poland based Education organisation with 220 employees and revenues of $30.0 million, Kobold Metals, a United States based Oil, Gas and Chemicals organisation with 200 employees and revenues of $10.0 million, Navier Ai, a United States based Aerospace and Defense organisation with 10 employees and revenues of $1.0 million and many others.
Contact us if you need a completed and verified list of companies using neptune.ai, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Brainly Poland | Education | 220 | $30M | Poland | neptune.ai | neptune.ai | MLOps Platforms | 2022 | n/a |
In 2022, Brainly Poland integrated neptune.ai into its Visual Search team to standardize experiment tracking for Snap to Solve model training. Neptune.ai was adopted as the MLOps Platforms component responsible for centralized experiment logging and metadata capture across model training workflows.
The implementation configured experiment tracking, artifact logging, run metadata capture, and monitoring for both model training and data processing jobs, improving reproducibility and supporting multi GPU resource optimization. Configuration emphasized consistent run naming, hyperparameter capture, and persisted artifacts to enable deterministic re runs and easier debugging across iterations.
The deployment integrated neptune.ai with Amazon SageMaker Pipelines to standardize tracking across pipeline stages, aligning pipeline orchestration events with experiment records and resource usage metadata. Integration delivered broader visibility into pipeline runs and enabled the Visual Search team to correlate SageMaker training jobs with neptune.ai experiment records for troubleshooting and capacity tuning.
Rollout was executed within Brainly Poland's Poland focused ML team and included governance to standardize tracking practices and access for non technical stakeholders. The implementation produced better debugging and monitoring of training and data processing jobs and enabled broader visibility to non technical stakeholders.
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Kobold Metals | Oil, Gas and Chemicals | 200 | $10M | United States | neptune.ai | neptune.ai | MLOps Platforms | 2022 | n/a |
In 2022, Kobold Metals deployed neptune.ai as a centralized experiment-tracking system. The neptune.ai deployment in the United States served as a single MLOps Platforms instance supporting geoscience and machine learning projects, capturing inputs, parameters and artifacts to make model runs reproducible for exploration decisions.
The implementation focused on experiment-tracking capabilities, instrumenting run metadata, artifact storage and parameter logging so that model provenance and results could be compared across thousands of runs. Neptune.ai was configured to record experiment inputs and outputs and to surface run-level metadata to both data scientists and geologists, enabling reproducible experiments and faster iteration on models used in mine-planning.
Operationally the platform provided cross-team visibility for geologists and data scientists and shortened feedback loops for model-driven mine-planning decisions. Governance centered on centralized tracking and standardized metadata capture to enforce experiment provenance and reproducibility, aligning the MLOps Platforms deployment with exploration and mine-planning business functions.
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Navier Ai | Aerospace and Defense | 10 | $1M | United States | neptune.ai | neptune.ai | MLOps Platforms | 2024 | n/a |
In 2024, Navier Ai implemented neptune.ai as its experiment tracking solution within the MLOps Platforms category. The deployment centralized experiment metadata and logging to support large-scale physics and foundation model training, while enabling real-time monitoring of CFD-related metrics and visualization outputs.
The neptune.ai implementation included experiment tracking, artifact and model versioning, real-time metric capture, and visualization dashboards tailored to computational fluid dynamics workflows. Configuration emphasized instrumentation of training pipelines to capture hyperparameters, training traces, and visual artifacts so engineers could iterate on simulation-driven models more consistently.
Operational coverage targeted Navier Ai engineering and physics teams in the United States, providing end-to-end support for ML and model training workflows used in CFD research and product development. Governance practices were established to standardize experiment naming, centralize repositories of runs and artifacts, and control access to experiment metadata to improve collaboration between data scientists and domain engineers.
The implementation delivered reduced downtime, faster iteration cycles for model development, and more reliable visualizations as stated outcomes of the neptune.ai deployment. These results strengthened continuous experimentation and improved observability of Navier Ai model training processes.
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Buyer Intent: Companies Evaluating neptune.ai
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