hydrosphere.io

What hydrosphere.io does

Hydrosphere.io, Inc. is a Palo Alto-based open-source MLOps platform company that provides tooling for deploying, versioning, and monitoring machine learning models in production. The platform is built as a collection of Dockerized microservices (Manager, Gateway, Sonar services in Scala plus Python-based microservices) that run on Kubernetes or Docker, and is distributed under the Apache 2.0 license via GitHub, Docker Hub, and PyPI. Its three core products are Hydrosphere Serving (framework- and language-agnostic model serving with automatic HTTP/gRPC/Kafka interfaces, model versioning, and traffic split/shadowing for A/B testing and canary deployments), Hydrosphere Monitoring (real-time statistical and ML-based drift detection, outlier/anomaly detection across tabular, image, and text data), and Hydrosphere Interpretability (black-box explanation of model predictions and data-drift root causes with high-dimensional visualization, positioned for GDPR compliance).

The company serves data science and ML engineering teams at AI-first enterprises that need to manage model degradation, explain predictions for regulatory purposes, and safely roll out model updates. Its go-to-market is primarily product-led and community-led through open-source distribution, a Slack community, Medium technical content, and GitBook documentation, with a secondary enterprise sales motion routed through a 'Request a Demo' contact form for organizations requiring support, customization, or managed services. The company is operated by Provectus IT, Inc., which shares the same Palo Alto headquarters (125 University Avenue, Suite 290). No funding rounds, revenue figures, named customers, or pricing tiers are publicly disclosed.

Founded circa 2016, the company operates with 1–10 employees. Documented product milestones include Hydrosphere 2.2.0 (March 2020) and Hydrosphere 3.0.0 (2021), with integrations added for AWS SageMaker and Kubeflow. The company's website copyright remains dated 2020, and there is no public evidence of subsequent major releases or commercial expansion.

Name

hydrosphere.io

Legal name

Hydrosphere.io, Inc.

Website

https://hydrosphere.io

Company type

Private

Founded year

2016

Operating status

Operating

Headcount range

1–10 employees

Short description

Hydrosphere.io, Inc. provides an open-source Apache 2.0 platform for deploying, versioning, and monitoring machine learning models in production, with integrated drift detection and black-box model interpretability. It serves data science and ML engineering teams at AI-first enterprises via product-led open-source distribution supplemented by enterprise sales.

Ownership category

akta.pro rank

Product category

MLOps Platform

NAICS

Software Publishers (5132), Computer Systems Design and Related Services (54151)

SIC

Services-Prepackaged Software (7372), Services-Computer Integrated Systems Design (7373)

akta.pro primary industry

End-to-End MLOps & ML Platform Suites (HDAAABAA)

akta.pro secondary industries

MLOps/LLMOps & Model Lifecycle Management Services (BPAEAHAH), Kubernetes & Container Platform Management (Private Cloud) (HDABABAC), Cloud-Native Development (Containers, Kubernetes, Microservices) (BPAEACAD)

Keywords

• MLOps platform
• Model serving
• Model monitoring
• ML interpretability
• Open source AI

Headquarters

HQ city

Palo Alto

HQ country

United States

HQ region

North America

Offices1 record

Markets served

GTM type

B2B

Offering type

Software

Cost components

Technology or R&D, Personnel, Marketing or Sales, Operations

Revenue model

1. Open-Source Platform: Core platform is open-source under Apache 2.0 license, freely available for download and use via GitHub, Docker Hub, and package managers (pip). Revenue model not explicitly disclosed but implied to include enterprise licensing, support contracts, or managed services.

Go-to-market motion2 records

Distribution channels4 records

Marketing channels6 records

Core offering

Hydrosphere.io provides an open-source MLOps platform for deploying, versioning, and managing machine learning models in production, monitoring their performance, and explaining their predictions. The platform is sold as three complementary components — Hydrosphere Serving, Hydrosphere Monitoring, and Hydrosphere Interpretability — that together support the full lifecycle of production ML systems. It targets ML engineering and data science teams at enterprises needing a self-hosted, framework-agnostic alternative to cloud-locked AI platforms.

Product overview

Hydrosphere is a platform for deploying, versioning, and monitoring machine learning models in production, consisting of three core integrated components. Hydrosphere Serving is the open-source deployment cluster for deploying and scaling ML models framework-agnostically. Hydrosphere Monitoring provides real-time drift detection and alerting to catch data quality issues before they impact model performance. Hydrosphere Interpretability explains model predictions and data drift root causes using black-box methods for regulatory compliance. These three products work together as a unified ML lifecycle management platform.

Differentiator

Problem solved

Functional benefit

Brands

• Hydrosphere Serving: An open-source cluster for deploying machine learning models in production. It is a collection of dockerized services that can run anywhere Docker or Kubernetes runs.
• Hydrosphere Monitoring
• Hydrosphere Interpretability

Products and services

• Hydrosphere Serving An ML model deployment and management service that handles model versioning, traffic splitting across versions, and packaging of production ML models as microservices. It is intended for ML engineering teams that need to deploy and update models reliably in production environments.
• Hydrosphere Monitoring A production ML monitoring service that detects data and concept drift, tracks model performance metrics, and alerts on anomalies in deployed models. It is used by data science and ML platform teams to maintain model quality after deployment.
• Hydrosphere Interpretability A model interpretability service that explains predictions from black-box machine learning models. It is designed for organizations that need to audit, validate, or communicate the behavior of deployed ML models to stakeholders and regulators.

Segments2 records

Ideal customer profiles2 records

Technology focussed Yes

API detail

Has API

Yes

API docs

API detail

Core technology

AI maturity

App detail

Integration4 records

AI capability7 records

Feature7 records

Partnerships

One partnership is on record.

• Provectus IT, Inc.parentOthersParent company and operator of Hydrosphere.io. Provectus IT is headquartered at 125 University Avenue, Suite 290, Palo Alto, CA 94301. Provides corporate infrastructure and likely enterprise services for Hydrosphere platform.

Scale indicators2 records

Recent moves5 records

Expansion highlights4 records

Broad incumbents

• Weights & Biases: Commercial ML developer platform spanning experiment tracking, model registry, and production monitoring; comparable as a broader incumbent in MLOps that competes for the same data science and ML engineering budget.
• Domino Data Lab: Enterprise MLOps platform covering model development, deployment, and monitoring on a unified governance substrate, addressing the same AI-first enterprise buyer Hydrosphere targets at the high end.
• Amazon SageMaker: AWS-managed ML platform with built-in deployment, monitoring, and drift detection across the full ML lifecycle; broad incumbent that overlaps every Hydrosphere capability and is explicitly integrated via SageMaker Components.

Direct peers

• Algorithmia (Microsoft): Enterprise ML deployment and MLOps platform acquired by Microsoft; historically a direct competitor in model serving, traffic management, and governance-comparable features to Hydrosphere Serving.
• BentoML: Open-source framework for building, shipping, and scaling ML prediction services, comparable to Hydrosphere Serving in language/framework-agnostic model serving with Docker/Kubernetes deployment.
• MLflow (Databricks): Open-source platform for managing the end-to-end machine learning lifecycle (experimentation, reproducibility, deployment), directly comparable to Hydrosphere's open-source serving-monitoring-interpretability stack.
• Kubeflow: Kubernetes-native ML toolkit covering training, serving, and pipelines; directly referenced by Hydrosphere via its Kubeflow Components integration and overlapping open-source MLOps positioning.
• Seldon: Open-source ML model deployment and monitoring platform on Kubernetes, closely aligned with Hydrosphere Serving/Monitoring in target customer, framework support, and traffic-split capabilities.
• Cortex Labs: Open-source platform for deploying, scaling, and monitoring machine learning models on Kubernetes, mirroring Hydrosphere's framework-agnostic multi-model serving and monitoring positioning.

Emerging players

• Arize AI: ML observability platform specializing in production data drift, model performance monitoring, and explainability, directly overlapping Hydrosphere Monitoring and Interpretability for enterprise ML reliability use cases.

Market position

Strengths5 records

Weaknesses5 records

Competitive moat4 records

Key risks7 records

Key highlights7 records

Customer concentration

• GitHubgithub.com/Hydrospheredata/hydro-serving
• YouTubejoin.slack.com/t/hydrospheredata/shared_invite/zt-gg41mrlg-eDz6v6yhkOfoMf~oi2mTug
• Facebookfacebook.com/hydrosphere.io
• LinkedInlinkedin.com/company/hydrospherebigdata
• Xx.com/hydrospheredata

Revenue estimate

Valuation estimate

Number of profiles

Profiles1 record

Funding overview

Funding rounds

Investors

Funding detail is available on the Subscription and Enterprise plan.Contact sales →

M&A

Investments

M&A and investment is available on the Subscription and Enterprise plan.Contact sales →

Frequently asked questions about hydrosphere.io