Best Data Management Software for TensorFlow
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Compare the Top Data Management Software that integrates with TensorFlow as of November 2025
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This a list of Data Management software that integrates with TensorFlow. Use the filters on the left to add additional filters for products that have integrations with TensorFlow. View the products that work with TensorFlow in the table below.
What is Data Management Software for TensorFlow?
Data management software systems are software platforms that help organize, store and analyze information. They provide a secure platform for data sharing and analysis with features such as reporting, automation, visualizations, and collaboration. Data management software can be customized to fit the needs of any organization by providing numerous user options to easily access or modify data. These systems enable organizations to keep track of their data more efficiently while reducing the risk of data loss or breaches for improved business security. Compare and read user reviews of the best Data Management software for TensorFlow currently available using the table below. This list is updated regularly.
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1
Vertex AI
Google
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex.Starting Price: Free ($300 in free credits) -
2
Qloo
Qloo
Qloo is the “Cultural AI”, decoding and predicting consumer taste across the globe. A privacy-first API that predicts global consumer preferences and catalogs hundreds of millions of cultural entities. Through our API, we provide contextualized personalization and insights based on a deep understanding of consumer behavior and more than 575 million people, places, and things. Our technology empowers you to look beyond trends and uncover the connections behind people’s tastes in the world around them. Look up entities in our vast library spanning categories like brands, music, film, fashion, travel destinations, and notable people. Results are delivered within milliseconds and can be weighted by factors such as regionalization and real-time popularity. Used by companies who want to incorporate best-in-class data in their consumer experiences. Our flagship recommendation API delivers results based on demographics, preferences, cultural entities, metadata, and geolocational factors. -
3
Jupyter Notebook
Project Jupyter
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. -
4
Dataiku
Dataiku
Dataiku is an advanced data science and machine learning platform designed to enable teams to build, deploy, and manage AI and analytics projects at scale. It empowers users, from data scientists to business analysts, to collaboratively create data pipelines, develop machine learning models, and prepare data using both visual and coding interfaces. Dataiku supports the entire AI lifecycle, offering tools for data preparation, model training, deployment, and monitoring. The platform also includes integrations for advanced capabilities like generative AI, helping organizations innovate and deploy AI solutions across industries. -
5
Deepnote
Deepnote
Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore, and analyze it with real-time collaboration and version control. Users can easily share project links with team collaborators, or with end-users to present polished assets. All of this is done through a powerful, browser-based UI that runs in the cloud. We built Deepnote because data scientists don't work alone. Features: - Sharing notebooks and projects via URL - Inviting others to view, comment and collaborate, with version control - Publishing notebooks with visualizations for presentations - Sharing datasets between projects - Set team permissions to decide who can edit vs view code - Full linux terminal access - Code completion - Automatic python package management - Importing from github - PostgreSQL DB connectionStarting Price: Free -
6
Azure Data Science Virtual Machines
Microsoft
DSVMs are Azure Virtual Machine images, pre-installed, configured and tested with several popular tools that are commonly used for data analytics, machine learning and AI training. Consistent setup across team, promote sharing and collaboration, Azure scale and management, Near-Zero Setup, full cloud-based desktop for data science. Quick, Low friction startup for one to many classroom scenarios and online courses. Ability to run analytics on all Azure hardware configurations with vertical and horizontal scaling. Pay only for what you use, when you use it. Readily available GPU clusters with Deep Learning tools already pre-configured. Examples, templates and sample notebooks built or tested by Microsoft are provided on the VMs to enable easy onboarding to the various tools and capabilities such as Neural Networks (PYTorch, Tensorflow, etc.), Data Wrangling, R, Python, Julia, and SQL Server.Starting Price: $0.005 -
7
LeanXcale
LeanXcale
LeanXcale is a fast and scalable database that combines the characteristics of SQL and NoSQL. It is built to ingest massive batch and real-time data pipelines and make it available through SQL or GIS for any use, such as operational applications, analytics, dashboarding, or machine learning processing. No matter what stack you use, LeanXcale provides you both SQL and NoSQL interfaces. KiVi storage engine is a relational key-value data store. Users can access the data not only through the standard SQL API but also through a direct ACID key-value interface. This key-value interface allows users to perform data ingestion at very high rates and very efficiently by avoiding SQL processing overhead. Highly-scalable, efficient and distributed storage engine distributed data along the cluster to improve the performance and increase the reliability.Starting Price: $0.127 per GB per month -
8
neptune.ai
neptune.ai
Neptune.ai is a machine learning operations (MLOps) platform designed to streamline the tracking, organizing, and sharing of experiments and model-building processes. It provides a comprehensive environment for data scientists and machine learning engineers to log, visualize, and compare model training runs, datasets, hyperparameters, and metrics in real-time. Neptune.ai integrates easily with popular machine learning libraries, enabling teams to efficiently manage both research and production workflows. With features that support collaboration, versioning, and experiment reproducibility, Neptune.ai enhances productivity and helps ensure that machine learning projects are transparent and well-documented across their lifecycle.Starting Price: $49 per month -
9
Comet
Comet
Manage and optimize models across the entire ML lifecycle, from experiment tracking to monitoring models in production. Achieve your goals faster with the platform built to meet the intense demands of enterprise teams deploying ML at scale. Supports your deployment strategy whether it’s private cloud, on-premise servers, or hybrid. Add two lines of code to your notebook or script and start tracking your experiments. Works wherever you run your code, with any machine learning library, and for any machine learning task. Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. Monitor your models during every step from training to production. Get alerts when something is amiss, and debug your models to address the issue. Increase productivity, collaboration, and visibility across all teams and stakeholders.Starting Price: $179 per user per month -
10
TrueFoundry
TrueFoundry
TrueFoundry is a Cloud-native Machine Learning Training and Deployment PaaS on top of Kubernetes that enables Machine learning teams to train and Deploy models at the speed of Big Tech with 100% reliability and scalability - allowing them to save cost and release Models to production faster. We abstract out the Kubernetes for Data Scientists and enable them to operate in a way they are comfortable. It also allows teams to deploy and fine-tune large language models seamlessly with full security and cost optimization. TrueFoundry is open-ended, API Driven and integrates with the internal systems, deploys on a company's internal infrastructure and ensures complete Data Privacy and DevSecOps practices.Starting Price: $5 per month -
11
HStreamDB
EMQ
A streaming database is purpose-built to ingest, store, process, and analyze massive data streams. It is a modern data infrastructure that unifies messaging, stream processing, and storage to help get value out of your data in real-time. Ingest massive amounts of data continuously generated from various sources, such as IoT device sensors. Store millions of data streams reliably in a specially designed distributed streaming data storage cluster. Consume data streams in real-time as fast as from Kafka by subscribing to topics in HStreamDB. With the permanent data stream storage, you can playback and consume data streams anytime. Process data streams based on event-time with the same familiar SQL syntax you use to query data in a relational database. You can use SQL to filter, transform, aggregate, and even join multiple data streams.Starting Price: Free -
12
Deep Lake
activeloop
Generative AI may be new, but we've been building for this day for the past 5 years. Deep Lake thus combines the power of both data lakes and vector databases to build and fine-tune enterprise-grade, LLM-based solutions, and iteratively improve them over time. Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop. Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model. Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained. Deep Lake datasets are visualized right in your browser or Jupyter Notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.Starting Price: $995 per month -
13
Yandex Data Proc
Yandex
You select the size of the cluster, node capacity, and a set of services, and Yandex Data Proc automatically creates and configures Spark and Hadoop clusters and other components. Collaborate by using Zeppelin notebooks and other web apps via a UI proxy. You get full control of your cluster with root permissions for each VM. Install your own applications and libraries on running clusters without having to restart them. Yandex Data Proc uses instance groups to automatically increase or decrease computing resources of compute subclusters based on CPU usage indicators. Data Proc allows you to create managed Hive clusters, which can reduce the probability of failures and losses caused by metadata unavailability. Save time on building ETL pipelines and pipelines for training and developing models, as well as describing other iterative tasks. The Data Proc operator is already built into Apache Airflow.Starting Price: $0.19 per hour -
14
ApertureDB
ApertureDB
Build your competitive edge with the power of vector search. Streamline your AI/ML pipeline workflows, reduce infrastructure costs, and stay ahead of the curve with up to 10x faster time-to-market. Break free of data silos with ApertureDB's unified multimodal data management, freeing your AI teams to innovate. Set up and scale complex multimodal data infrastructure for billions of objects across your entire enterprise in days, not months. Unifying multimodal data, advanced vector search, and innovative knowledge graph with a powerful query engine to build AI applications faster at enterprise scale. ApertureDB can enhance the productivity of your AI/ML teams and accelerate returns from AI investment with all your data. Try it for free or schedule a demo to see it in action. Find relevant images based on labels, geolocation, and regions of interest. Prepare large-scale multi-modal medical scans for ML and clinical studies.Starting Price: $0.33 per hour -
15
Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. Automate AI lifecycles with ModelOps pipelines. Speed data science development with AutoAI. Prepare and build models visually and programmatically. Deploy and run models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.
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16
Intel Tiber AI Studio
Intel
Intel® Tiber™ AI Studio is a comprehensive machine learning operating system that unifies and simplifies the AI development process. The platform supports a wide range of AI workloads, providing a hybrid and multi-cloud infrastructure that accelerates ML pipeline development, model training, and deployment. With its native Kubernetes orchestration and meta-scheduler, Tiber™ AI Studio offers complete flexibility in managing on-prem and cloud resources. Its scalable MLOps solution enables data scientists to easily experiment, collaborate, and automate their ML workflows while ensuring efficient and cost-effective utilization of resources. -
17
GigaSpaces
GigaSpaces
Smart DIH is an operational data hub that powers real-time modern applications. It unleashes the power of customers’ data by transforming data silos into assets, turning organizations into data-driven enterprises. Smart DIH consolidates data from multiple heterogeneous systems into a highly performant data layer. Low code tools empower data professionals to deliver data microservices in hours, shortening developing cycles and ensuring data consistency across all digital channels. XAP Skyline is a cloud-native, in memory data grid (IMDG) and developer framework designed for mission critical, cloud-native apps. XAP Skyline delivers maximal throughput, microsecond latency and scale, while maintaining transactional consistency. It provides extreme performance, significantly reducing data access time, which is crucial for real-time decisioning, and transactional applications. XAP Skyline is used in financial services, retail, and other industries where speed and scalability are critical. -
18
Mona
Mona
Gain complete visibility into the performance of your data, models, and processes with the most flexible monitoring solution. Automatically surface and resolve performance issues within your AI/ML or intelligent automation processes to avoid negative impacts on both your business and customers. Learning how your data, models, and processes perform in the real world is critical to continuously improving your processes. Monitoring is the ‘eyes and ears' needed to observe your data and workflows to tell you if they’re performing well. Mona exhaustively analyzes your data to provide actionable insights based on advanced anomaly detection mechanisms, to alert you before your business KPIs are hurt. Take stock of any part of your production workflows and business processes, including models, pipelines, and business outcomes. Whatever datatype you work with, whether you have a batch or streaming real-time processes, and for the specific way in which you want to measure your performance. -
19
MLReef
MLReef
MLReef enables domain experts and data scientists to securely collaborate via a hybrid of pro-code & no-code development approaches. 75% increase in productivity due to distributed workloads. This enables teams to complete more ML projects faster. Domain experts and data scientists collaborate on the same platform reducing 100% of unnecessary communication ping-pong. MLReef works on your premises and uniquely enables 100% reproducibility and continuity. Rebuild all work at any time. You can use already well-known and established git repositories to create explorable, interoperable, and versioned AI modules. AI Modules created by your data scientists become drag-and-drop elements. These are adjustable by parameters, versioned, interoperable, and explorable within your entire organization. Data handling often requires expert knowledge that a single data scientist often lacks. MLReef enables your field experts to relieve your data processing task, reducing complexities. -
20
Tausight
Tausight
Tausight’s healthcare data security platform is trained using a patented algorithm to find ePHI on devices, data stores and cloud assets. The result is powerful insights on the how PHI is being accessed, where it’s traveling, and how it might be at risk. Tausight is designed to fit into the unique, decentralized environments of healthcare. API integrations with leading security operations, ticketing and response systems enable automated protection of vulnerable ePHI. Tausight’s agentless cloud deployment and lightweight sensor can be installed in minutes, helping you discover ePHI in 60 minutes or less. -
21
Databricks Data Intelligence Platform
Databricks
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker. -
22
Weights & Biases
Weights & Biases
Experiment tracking, hyperparameter optimization, model and dataset versioning with Weights & Biases (WandB). Track, compare, and visualize ML experiments with 5 lines of code. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Optimize models with our massively scalable hyperparameter search tool. Sweeps are lightweight, fast to set up, and plug in to your existing infrastructure for running models. Save every detail of your end-to-end machine learning pipeline — data preparation, data versioning, training, and evaluation. It's never been easier to share project updates. Quickly and easily implement experiment logging by adding just a few lines to your script and start logging results. Our lightweight integration works with any Python script. W&B Weave is here to help developers build and iterate on their AI applications with confidence. -
23
HPE Ezmeral
Hewlett Packard Enterprise
Run, manage, control and secure the apps, data and IT that run your business, from edge to cloud. HPE Ezmeral advances digital transformation initiatives by shifting time and resources from IT operations to innovations. Modernize your apps. Simplify your Ops. And harness data to go from insights to impact. Accelerate time-to-value by deploying Kubernetes at scale with integrated persistent data storage for app modernization on bare metal or VMs, in your data center, on any cloud or at the edge. Harness data and get insights faster by operationalizing the end-to-end process to build data pipelines. Bring DevOps agility to the machine learning lifecycle, and deliver a unified data fabric. Boost efficiency and agility in IT Ops with automation and advanced artificial intelligence. And provide security and control to eliminate risk and reduce costs. HPE Ezmeral Container Platform provides an enterprise-grade platform to deploy Kubernetes at scale for a wide range of use cases. -
24
witboost
Agile Lab
witboost is a modular, scalable, fast, efficient data management system for your company to truly become data driven, reduce time-to-market, it expenditures and overheads. witboost comprises a series of modules. These are building blocks that can work as standalone solutions to address and solve a single need or problem, or they can be combined to create the perfect data management ecosystem for your company. Each module improves a specific data engineering function and they can be combined to create the perfect solution to answer your specific needs, guaranteeing a blazingly fact and smooth implementation, thus dramatically reducing time-to-market, time-to-value and consequently the TCO of your data engineering infrastructure. Smart Cities need digital twins to predict needs and avoid unforeseen problems, gathering data from thousands of sources and managing ever more complex telematics. -
25
TruEra
TruEra
A machine learning monitoring solution that helps you easily oversee and troubleshoot high model volumes. With explainability accuracy that’s unparalleled and unique analyses that are not available anywhere else, data scientists avoid false alarms and dead ends, addressing critical problems quickly and effectively. Your machine learning models stay optimized, so that your business is optimized. TruEra’s solution is based on an explainability engine that, due to years of dedicated research and development, is significantly more accurate than current tools. TruEra’s enterprise-class AI explainability technology is without peer. The core diagnostic engine is based on six years of research at Carnegie Mellon University and dramatically outperforms competitors. The platform quickly performs sophisticated sensitivity analysis that enables data scientists, business users, and risk and compliance teams to understand exactly how and why a model makes predictions. -
26
teX.ai
teX.ai
Given the sea of content, your business generates, identifies, and processes only text that is of interest to you, quickly, accurately, and efficiently. Regardless of your business needs, operational agility, faster decisions, obtaining customer insights or more, teXai, a Forbes recognized text analytics company, helps you take advantage of text to propel your business forward. teXai's powerful customizable preprocessor engine identifies and extracts objects of your interest in the nooks and crannies of your organization’s emails, text messages, tables, website, social media, archives, or any documents of your choice. Its intelligent customizable linguistic application identifies text genre, groups, similar content and creates concise summaries so that your business teams can obtain the right context from the right text. The easy-to-use text analytics software extracts the essence of your text and simplifies the decision-making process. -
27
Cloudera Data Platform
Cloudera
Unlock the potential of private and public clouds with the only hybrid data platform for modern data architectures with data anywhere. Cloudera is a hybrid data platform designed for unmatched freedom to choose—any cloud, any analytics, any data. Cloudera delivers faster and easier data management and data analytics for data anywhere, with optimal performance, scalability, and security. With Cloudera you get all the advantages of private cloud and public cloud for faster time to value and increased IT control. Cloudera provides the freedom to securely move data, applications, and users bi-directionally between the data center and multiple data clouds, regardless of where your data lives. -
28
Fosfor Decision Cloud
Fosfor
Everything you need to make better business decisions. The Fosfor Decision Cloud unifies the modern data ecosystem to deliver the long-sought promise of AI: enhanced business outcomes. The Fosfor Decision Cloud unifies the components of your data stack into a modern decision stack, built to amplify business outcomes. Fosfor works seamlessly with its partners to create the modern decision stack, which delivers unprecedented value from your data investments. -
29
Feast
Tecton
Make your offline data available for real-time predictions without having to build custom pipelines. Ensure data consistency between offline training and online inference, eliminating train-serve skew. Standardize data engineering workflows under one consistent framework. Teams use Feast as the foundation of their internal ML platforms. Feast doesn’t require the deployment and management of dedicated infrastructure. Instead, it reuses existing infrastructure and spins up new resources when needed. You are not looking for a managed solution and are willing to manage and maintain your own implementation. You have engineers that are able to support the implementation and management of Feast. You want to run pipelines that transform raw data into features in a separate system and integrate with it. You have unique requirements and want to build on top of an open source solution. -
30
Zepl
Zepl
Sync, search and manage all the work across your data science team. Zepl’s powerful search lets you discover and reuse models and code. Use Zepl’s enterprise collaboration platform to query data from Snowflake, Athena or Redshift and build your models in Python. Use pivoting and dynamic forms for enhanced interactions with your data using heatmap, radar, and Sankey charts. Zepl creates a new container every time you run your notebook, providing you with the same image each time you run your models. Invite team members to join a shared space and work together in real time or simply leave their comments on a notebook. Use fine-grained access controls to share your work. Allow others have read, edit, and run access as well as enable collaboration and distribution. All notebooks are auto-saved and versioned. You can name, manage and roll back all versions through an easy-to-use interface, and export seamlessly into Github.
