Alternatives to Azure Notebooks
Compare Azure Notebooks alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Azure Notebooks in 2026. Compare features, ratings, user reviews, pricing, and more from Azure Notebooks competitors and alternatives in order to make an informed decision for your business.
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1
Teradata VantageCloud
Teradata
Teradata VantageCloud: The complete cloud analytics and data platform for AI. Teradata VantageCloud is an enterprise-grade, cloud-native data and analytics platform that unifies data management, advanced analytics, and AI/ML capabilities in a single environment. Designed for scalability and flexibility, VantageCloud supports multi-cloud and hybrid deployments, enabling organizations to manage structured and semi-structured data across AWS, Azure, Google Cloud, and on-premises systems. It offers full ANSI SQL support, integrates with open-source tools like Python and R, and provides built-in governance for secure, trusted AI. VantageCloud empowers users to run complex queries, build data pipelines, and operationalize machine learning models—all while maintaining interoperability with modern data ecosystems. -
2
Posit
Posit
Posit builds tools that help data scientists work more efficiently, collaborate seamlessly, and share insights securely across their organizations. Its Positron code editor provides the speed of an interactive console combined with the power to build, debug, and deploy data-science workflows in Python and R. Posit’s platform enables teams to scale open-source data science, offering enterprise-ready capabilities for publishing, sharing, and operationalizing applications. Companies rely on Posit’s secure infrastructure to host Shiny apps, dashboards, APIs, and analytical reports with confidence. Whether using open-source packages or cloud-based solutions, Posit supports reproducible, high-quality work at every stage of the data lifecycle. Trusted by millions of users—and more than half of the Fortune 100—Posit empowers professionals across industries to innovate with data. -
3
Google Colab
Google
Google Colab is a free, hosted Jupyter Notebook service that provides cloud-based environments for machine learning, data science, and educational purposes. It offers no-setup, easy access to computational resources such as GPUs and TPUs, making it ideal for users working with data-intensive projects. Colab allows users to run Python code in an interactive, notebook-style environment, share and collaborate on projects, and access extensive pre-built resources for efficient experimentation and learning. Colab also now offers a Data Science Agent automating analysis, from understanding the data to delivering insights in a working Colab notebook (Sequences shortened. Results for illustrative purposes. Data Science Agent may make mistakes.) -
4
CoCalc
SageMath
Teaching scientific software online. CoCalc is a virtual online computer lab: it takes away the pain of teaching scientific software. Every student works 100% online – inside their own, isolated workspace. Follow the progress of each student in real-time. At any time you can jump into a file of a student, right where they are working. Use TimeTravel to see each step a student took to get to the solution. Integrated chat rooms allows you to guide students directly where they work or discuss collected files with your teaching assistants. The project's Activity Log records exactly when and by whom a file was accessed. Forget any complicated software setup – everyone is able to start working in seconds! Since everyone works with exactly the same software stack, any inconsistencies between your and your students' environments are eliminated. -
5
MLJAR Studio
MLJAR
It's a desktop app with Jupyter Notebook and Python built in, installed with just one click. It includes interactive code snippets and an AI assistant to make coding faster and easier, perfect for data science projects. We manually hand crafted over 100 interactive code recipes that you can use in your Data Science projects. Code recipes detect packages available in the current environment. Install needed modules with 1-click, literally. You can create and interact with all variables available in your Python session. Interactive recipes speed-up your work. AI Assistant has access to your current Python session, variables and modules. Broad context makes it smart. Our AI Assistant was designed to solve data problems with Python programming language. It can help you with plots, data loading, data wrangling, Machine Learning and more. Use AI to quickly solve issues with code, just click Fix button. The AI assistant will analyze the error and propose the solution.Starting Price: $20 per month -
6
Gradient
Gradient
Explore a new library or dataset in a notebook. Automate preprocessing, training, or testing with a 2orkflow. Bring your application to life with a deployment. Use notebooks, workflows, and deployments together or independently. Compatible with everything. Gradient supports all major frameworks and libraries. Gradient is powered by Paperspace's world-class GPU instances. Move faster with source control integration. Connect to GitHub to manage all your work & compute resources with git. Launch a GPU-enabled Jupyter Notebook from your browser in seconds. Use any library or framework. Easily invite collaborators or share a public link. A simple cloud workspace that runs on free GPUs. Get started in seconds with a notebook environment that's easy to use and share. Perfect for ML developers. A powerful no-fuss environment with loads of features that just works. Choose a pre-built template or bring your own. Try a free GPU!Starting Price: $8 per month -
7
Oracle Machine Learning
Oracle
Machine learning uncovers hidden patterns and insights in enterprise data, generating new value for the business. Oracle Machine Learning accelerates the creation and deployment of machine learning models for data scientists using reduced data movement, AutoML technology, and simplified deployment. Increase data scientist and developer productivity and reduce their learning curve with familiar open source-based Apache Zeppelin notebook technology. Notebooks support SQL, PL/SQL, Python, and markdown interpreters for Oracle Autonomous Database so users can work with their language of choice when developing models. A no-code user interface supporting AutoML on Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression. Data scientists gain integrated model deployment from the Oracle Machine Learning AutoML User Interface. -
8
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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9
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 -
10
Modelbit
Modelbit
Don't change your day-to-day, works with Jupyter Notebooks and any other Python environment. Simply call modelbi.deploy to deploy your model, and let Modelbit carry it — and all its dependencies — to production. ML models deployed with Modelbit can be called directly from your warehouse as easily as calling a SQL function. They can also be called as a REST endpoint directly from your product. Modelbit is backed by your git repo. GitHub, GitLab, or home grown. Code review. CI/CD pipelines. PRs and merge requests. Bring your whole git workflow to your Python ML models. Modelbit integrates seamlessly with Hex, DeepNote, Noteable and more. Take your model straight from your favorite cloud notebook into production. Sick of VPC configurations and IAM roles? Seamlessly redeploy your SageMaker models to Modelbit. Immediately reap the benefits of Modelbit's platform with the models you've already built. -
11
Azure Machine Learning
Microsoft
Accelerate the end-to-end machine learning lifecycle with Azure Machine Learning Studio. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R. -
12
JetBrains DataSpell
JetBrains
Switch between command and editor modes with a single keystroke. Navigate over cells with arrow keys. Use all of the standard Jupyter shortcuts. Enjoy fully interactive outputs – right under the cell. When editing code cells, enjoy smart code completion, on-the-fly error checking and quick-fixes, easy navigation, and much more. Work with local Jupyter notebooks or connect easily to remote Jupyter, JupyterHub, or JupyterLab servers right from the IDE. Run Python scripts or arbitrary expressions interactively in a Python Console. See the outputs and the state of variables in real-time. Split Python scripts into code cells with the #%% separator and run them individually as you would in a Jupyter notebook. Browse DataFrames and visualizations right in place via interactive controls. All popular Python scientific libraries are supported, including Plotly, Bokeh, Altair, ipywidgets, and others.Starting Price: $229 -
13
JupyterLab
Jupyter
Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. JupyterLab is flexible, configure and arrange the user interface to support a wide range of workflows in data science, scientific computing, and machine learning. JupyterLab is extensible and modular, write plugins that add new components and integrate with existing ones. 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. Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala. -
14
Hopsworks
Logical Clocks
Hopsworks is an open-source Enterprise platform for the development and operation of Machine Learning (ML) pipelines at scale, based around the industry’s first Feature Store for ML. You can easily progress from data exploration and model development in Python using Jupyter notebooks and conda to running production quality end-to-end ML pipelines, without having to learn how to manage a Kubernetes cluster. Hopsworks can ingest data from the datasources you use. Whether they are in the cloud, on‑premise, IoT networks, or from your Industry 4.0-solution. Deploy on‑premises on your own hardware or at your preferred cloud provider. Hopsworks will provide the same user experience in the cloud or in the most secure of air‑gapped deployments. Learn how to set up customized alerts in Hopsworks for different events that are triggered as part of the ingestion pipeline.Starting Price: $1 per month -
15
Azure Cloud Shell
Microsoft
Connect to Azure using an authenticated, browser-based shell experience that’s hosted in the cloud and accessible from virtually anywhere. Azure Cloud Shell is assigned per unique user account and automatically authenticated with each session. Azure Cloud Shell gives you the flexibility of choosing the shell experience that best suits the way you work. Both Bash and PowerShell experiences are available. Microsoft routinely maintains and updates Cloud Shell, which comes equipped with commonly used CLI tools including Linux shell interpreters, PowerShell modules, Azure tools, text editors, source control, build tools, container tools, database tools, and more. Cloud Shell also includes language support for several popular programming languages such as Node.js, .NET, and Python. Use common tools and programming languages in a shell that's updated and maintained by Microsoft. -
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Rapidminer AI Studio
Siemens
RapidMiner AI Studio is a dedicated environment for rapidly developing and prototyping AI solutions, helping teams unify the complete data science lifecycle from data exploration and machine learning to model operations and visualization. It allows data scientists and engineers to build, train, and test AI models locally, giving organizations full control and flexibility for initial exploration and development. It connects directly to enterprise data sources, including files, databases, data lakes, cloud data platforms, warehouses, SQL databases, and Internet of Things data streams, helping teams unify data, prevent errors, and power accurate, explainable AI. RapidMiner AI Studio supports both domain experts and technical teams: users without coding experience can quickly build effective machine learning models with an intuitive drag-and-drop canvas, while data scientists can create complex models in a fully integrated notebook environment using Python and R. -
17
Azure Storage
Microsoft
The Azure Storage platform is Microsoft's cloud storage solution for modern data storage scenarios. Azure Storage offers highly available, massively scalable, durable, and secure storage for a variety of data objects in the cloud. Azure Storage data objects are accessible from anywhere in the world over HTTP or HTTPS via a REST API. Azure Storage also offers client libraries for developers building applications or services with .NET, Java, Python, JavaScript, C++, and Go. Developers and IT professionals can use Azure PowerShell and Azure CLI to write scripts for data management or configuration tasks. The Azure portal and Azure Storage Explorer provide user-interface tools for interacting with Azure Storage. Durable and highly available. Redundancy ensures that your data is safe in the event of transient hardware failures. You can also opt to replicate data across data centers or geographical regions for additional protection from local catastrophes or natural disasters. -
18
Gradio
Gradio
Build & Share Delightful Machine Learning Apps. Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that lets them interact with the model on your computer remotely from their own devices. Once you've created an interface, you can permanently host it on Hugging Face. Hugging Face Spaces will host the interface on its servers and provide you with a link you can share. -
19
Gurobi Optimizer
Gurobi Optimization
With our powerful algorithms, you can add complexity to your model to better represent the real world, and still solve your model within the available time. Integrate Gurobi into your applications easily, using the languages you know best. Our programming interfaces are designed to be lightweight, modern, and intuitive, to minimize your learning curve while maximizing your productivity. Our Python API includes higher-level modeling constructs that make it easier to build optimization models. Choose from Anaconda Python distributions with pre-built libraries to support application development, Spyder for graphical development, and Jupyter for notebook-style development. -
20
PythonAnywhere
PythonAnywhere
Get started for free. Our basic plan gives you access to machines with a full Python environment already installed. You can develop and host your website or any other code directly from your browser without having to install software or manage your own server. Just write your application. No need to configure or maintain a web server — everything is set up and ready to go. Take your development environment with you! If you have a browser and an Internet connection, you've got everything you need. PythonAnywhere is a fully-fledged Python environment, ready to go, for students and teachers — concentrate on teaching, not on installation hassles. Need help with PythonAnywhere? If you get in touch, you can talk directly with the development team. Help for developers, from developers. We make a normally complicated process very simple, letting you focus on creating exciting applications for your users.Starting Price: $5 per month per app -
21
Edison Analysis
Edison Scientific
Edison Analysis is a next-generation scientific data-analysis agent built by Edison Scientific. It is the analytical engine underpinning their AI Scientist platform, Kosmos, and it’s available both on Edison’s platform and via API. Edison Analysis performs complex scientific data analysis by iteratively building and updating Jupyter notebooks in a dedicated environment; given a dataset plus a prompt, the agent explores, analyzes, and interprets the data to provide comprehensive insights, reports, and visualizations, very much like a human scientist. It supports execution of Python, R, and Bash code, and includes a full suite of common scientific-analysis packages in a Docker environment. Because all work is done within a notebook, the reasoning is fully transparent and auditable; users can inspect exactly how data was manipulated, which parameters were chosen, how conclusions were drawn, and can download the notebook and associated assets at any time.Starting Price: $50 per month -
22
Pathway
Pathway
Pathway is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. Pathway comes with an easy-to-use Python API, allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: you can use it in both development and production environments, handling both batch and streaming data effectively. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a scalable Rust engine based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with Docker and Kubernetes. -
23
Kubeflow
Kubeflow
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model. In particular, Kubeflow's job operator can handle distributed TensorFlow training jobs. Configure the training controller to use CPUs or GPUs and to suit various cluster sizes. Kubeflow includes services to create and manage interactive Jupyter notebooks. You can customize your notebook deployment and your compute resources to suit your data science needs. Experiment with your workflows locally, then deploy them to a cloud when you're ready. -
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Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case. In Amazon SageMaker you can pick different algorithms, including over 15 that are built-in and optimized for SageMaker, and use over 150 pre-built models from popular model zoos available with a few clicks. SageMaker also offers a variety of model-building tools including Amazon SageMaker Studio Notebooks and RStudio where you can run ML models on a small scale to see results and view reports on their performance so you can come up with high-quality working prototypes. Amazon SageMaker Studio Notebooks help you build ML models faster and collaborate with your team. Amazon SageMaker Studio notebooks provide one-click Jupyter notebooks that you can start working within seconds. Amazon SageMaker also enables one-click sharing of notebooks.
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25
CData Python Connectors
CData Software
CData Python Connectors simplify the way that Python users connect to SaaS, Big Data, NoSQL, and relational data sources. Our Python Connectors offer simple Python database interfaces (DB-API), making it easy to connect with popular tooling like Jupyter Notebook, SQLAlchemy, pandas, Dash, Apache Airflow, petl, and more. CData Python Connectors create a SQL wrapper around APIs and data protocols, simplifying data access from within Python and enabling Python users to easily connect more than 150 SaaS, Big Data, NoSQL, and relational data sources with advanced Python processing. The CData Python Connectors fill a critical gap in Python tooling by providing consistent connectivity with data-centric interfaces to hundreds of different SaaS/Cloud, NoSQL, and Big Data sources. Download a 30-day free trial or learn more at: https://www.cdata.com/python/ -
26
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 -
27
marimo
marimo
A reactive notebook for Python — run reproducible experiments, execute as a script, deploy as an app, and version with git. 🚀 batteries-included: replaces jupyter, streamlit, jupytext, ipywidgets, papermill, and more ⚡️ reactive: run a cell, and marimo reactively runs all dependent cells or marks them as stale 🖐️ interactive: bind sliders, tables, plots, and more to Python — no callbacks required 🔬 reproducible: no hidden state, deterministic execution, built-in package management 🏃 executable: execute as a Python script, parametrized by CLI args 🛜 shareable: deploy as an interactive web app or slides, run in the browser via WASM 🛢️ designed for data: query dataframes and databases with SQL, filter and search dataframes 🐍 git-friendly: notebooks are stored as .py files ⌨️ a modern editor: GitHub Copilot, AI assistants, vim keybindings, variable explorer, and moreStarting Price: $0 -
28
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. -
29
Chalk
Chalk
Powerful data engineering workflows, without the infrastructure headaches. Complex streaming, scheduling, and data backfill pipelines, are all defined in simple, composable Python. Make ETL a thing of the past, fetch all of your data in real-time, no matter how complex. Incorporate deep learning and LLMs into decisions alongside structured business data. Make better predictions with fresher data, don’t pay vendors to pre-fetch data you don’t use, and query data just in time for online predictions. Experiment in Jupyter, then deploy to production. Prevent train-serve skew and create new data workflows in milliseconds. Instantly monitor all of your data workflows in real-time; track usage, and data quality effortlessly. Know everything you computed and data replay anything. Integrate with the tools you already use and deploy to your own infrastructure. Decide and enforce withdrawal limits with custom hold times.Starting Price: Free -
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Lightly
Lightly
Develop your apps efficiently with Multilingual cloud IDE - Lightly. Create high-quality code in no time with AI-generated code and collaborative development features. Lightly is a powerful cloud IDE that supports multiple programming languages, including Java, Python, C++, HTML, JavaScript. Write, run, and debug code on iPad, anywhere, anytime. The AI-generated code feature helps you quickly generate code. Collaborative development enables easy team collaboration in real-time. Lightly can deploy and host your projects without managing infrastructures. We'll provide project images to efficiently help you deploy on AWS, Azure, GCP, or any other cloud provider. You focus on your creativity and ideas, and let the AI programming assistant do the rest for you!Starting Price: $9 per month -
31
scikit-learn
scikit-learn
Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.Starting Price: Free -
32
Azure Arc
Microsoft
Azure Arc is Microsoft’s hybrid and multicloud solution that extends Azure services across on-premises, edge, and other cloud environments. It enables organizations to manage servers, Kubernetes clusters, and applications anywhere with consistent tools and APIs. With Arc, businesses can modernize SQL Server and Windows Server, deploy containerized apps, and access Azure services like security, observability, and governance across diverse infrastructures. Its agentless multicloud connector streamlines management while maintaining embedded compliance with over 100 certifications. Azure Arc also integrates with existing tools such as GitHub and Visual Studio Code, allowing developers to innovate without disrupting workflows. By bridging traditional infrastructure and cloud-native services, it gives enterprises the flexibility to innovate anywhere while staying secure and cost-efficient. -
33
Ray
Anyscale
Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.Starting Price: Free -
34
Azure App Service
Microsoft
Quickly build, deploy, and scale web apps and APIs on your terms. Work with .NET, .NET Core, Node.js, Java, Python or PHP, in containers or running on Windows or Linux. Meet rigorous, enterprise-grade performance, security and compliance requirements used a trusted, fully managed platform that handles over 40 billion requests per day. Fully managed platform with built-in infrastructure maintenance, security patching, and scaling. Built-in CI/CD integration and zero-downtime deployments. Rigorous security and compliance, including SOC and PCI, for seamless deployments across public cloud, Azure Government, and on-premises environments. Bring your code or container using the framework language of your choice. Increase developer productivity with tight integration of Visual Studio Code and Visual Studio. Streamline CI/CD with Git, GitHub, GitHub Actions, Atlassian Bitbucket, Azure DevOps, Docker Hub, and Azure Container Registry.Starting Price: $0.013 per hour -
35
Hex
Hex
Hex brings together the best of notebooks, BI, and docs into a seamless, collaborative UI. Hex is a modern Data Workspace. It makes it easy to connect to data, analyze it in collaborative SQL and Python-powered notebooks, and share work as interactive data apps and stories. Your default landing page in Hex is the Projects page. You can quickly find projects you created, as well as those shared with you and your workspace. The outline provides an easy-to-browse overview of all the cells in a project's Logic View. Every cell in the outline lists the variables it defines, and cells that return a displayed output (chart cells, Input Parameters, markdown cells, etc.) display a preview of that output. You can click any cell in the outline to automatically jump to that position in the logic.Starting Price: $24 per user per month -
36
Microsoft Genomics
Microsoft
Instead of managing your own data centers, take advantage of Microsoft's scale and experience in running exabyte-scale workloads. Because Microsoft Genomics is on Azure, you have the performance and scalability of a world-class supercomputing center, on demand in the cloud. Take advantage of a backend network with MPI latency under three microseconds and non-blocking 32 gigabits per second (Gbps) throughput. This backend network includes remote direct memory access technology that enables parallel applications to scale to thousands of cores. Azure provides you with high memory and HPC-class CPUs to help you get results fast. Scale up and down based on what you need and pay only for what you use to reduce costs. Tackle data sovereignty requirements with a worldwide network of Azure data centers and adhere to your compliance requirements. Easily integrate into your existing pipeline code using a REST-based API and simple Python client. -
37
Rapidminer Knowledge Studio
Siemens
Rapidminer Knowledge Studio is a no-code machine learning and predictive analytics solution from Siemens designed for data scientists, business analysts, and business users. It helps users create predictive and prescriptive models through an interactive visual interface without requiring programming skills. The platform uses explainable decision trees and strategy trees to make machine learning models easier to understand, trust, and manage. Users can build drag-and-drop workflows, connect to diverse data sources, and generate actionable insights from business data. Rapidminer Knowledge Studio supports use cases such as credit risk, fraud detection, marketing analytics, product lifecycle planning, and customer loyalty programs. With model code generation in Python, R, SAS, SQL, PMML, and more, it helps organizations move from visual model design to practical implementation. -
38
Azure DocumentDB
Microsoft
Azure DocumentDB is an open source, MongoDB-compatible document database service built to help teams build AI-driven apps, migrate MongoDB workloads, and standardize on a portable document database engine. It supports hybrid and multicloud architectures with enterprise-grade performance, availability, security, management, and easy Azure AI integration. Built on DocumentDB, the open-source engine hosted at the Linux Foundation, Azure DocumentDB gives developers transparency, community-driven innovation, and freedom from restrictive licenses while supporting familiar MongoDB skills, drivers, tools, APIs, BSON and JSON documents, and popular languages such as Node.js, Python, Java, and .NET. Teams can build and test MongoDB-compatible apps in their own environment, including local, on-premises, and other clouds, then deploy to Azure DocumentDB for enterprise-grade scale and management.Starting Price: $13.943 per month -
39
Azure Data Lake
Microsoft
Azure Data Lake includes all the capabilities required to make it easy for developers, data scientists, and analysts to store data of any size, shape, and speed, and do all types of processing and analytics across platforms and languages. It removes the complexities of ingesting and storing all of your data while making it faster to get up and running with batch, streaming, and interactive analytics. Azure Data Lake works with existing IT investments for identity, management, and security for simplified data management and governance. It also integrates seamlessly with operational stores and data warehouses so you can extend current data applications. We’ve drawn on the experience of working with enterprise customers and running some of the largest scale processing and analytics in the world for Microsoft businesses like Office 365, Xbox Live, Azure, Windows, Bing, and Skype. Azure Data Lake solves many of the productivity and scalability challenges that prevent you from maximizing the -
40
Google Cloud Datalab
Google
An easy-to-use interactive tool for data exploration, analysis, visualization, and machine learning. Cloud Datalab is a powerful interactive tool created to explore, analyze, transform, and visualize data and build machine learning models on Google Cloud Platform. It runs on Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks. Cloud Datalab is built on Jupyter (formerly IPython), which boasts a thriving ecosystem of modules and a robust knowledge base. Cloud Datalab enables analysis of your data on BigQuery, AI Platform, Compute Engine, and Cloud Storage using Python, SQL, and JavaScript (for BigQuery user-defined functions). Whether you're analyzing megabytes or terabytes, Cloud Datalab has you covered. Query terabytes of data in BigQuery, run local analysis on sampled data, and run training jobs on terabytes of data in AI Platform seamlessly. -
41
Visual Studio
Microsoft
Microsoft Visual Studio is the industry-leading integrated development environment (IDE) for building modern applications across desktop, mobile, cloud, and web. It empowers developers to write, refactor, debug, test, and deploy software faster with intelligent assistance powered by GitHub Copilot and AI-driven workflows. With Agent Mode, developers can automate repetitive coding tasks, optimize performance, and receive contextual help directly in the IDE. The suite includes Visual Studio 2022, the comprehensive IDE for .NET and C++ development on Windows, and Visual Studio Code, the lightweight, cross-platform editor supporting JavaScript, Python, and dozens of other languages. Visual Studio integrates seamlessly with Azure, GitHub, and CI/CD pipelines, enabling teams to collaborate and ship code efficiently. Trusted by millions worldwide, Visual Studio provides the tools and intelligence developers need to build reliable, scalable, and secure applications from concept to release.Starting Price: $45/user/month -
42
LUIS
Microsoft
Language Understanding (LUIS): A machine learning-based service to build natural language into apps, bots, and IoT devices. Quickly create enterprise-ready, custom models that continuously improve. Add natural language to your apps. Designed to identify valuable information in conversations, LUIS interprets user goals (intents) and distills valuable information from sentences (entities), for a high quality, nuanced language model. LUIS integrates seamlessly with the Azure Bot Service, making it easy to create a sophisticated bot. Powerful developer tools are combined with customizable pre-built apps and entity dictionaries, such as Calendar, Music, and Devices, so you can build and deploy a solution more quickly. Dictionaries are mined from the collective knowledge of the web and supply billions of entries, helping your model to correctly identify valuable information from user conversations. Active learning is used to continuously improve the quality of the models. -
43
CodeSpace
Firia Labs
Easy to use browser-based software. Interactive curriculum modules, standards-based, project-driven learning. Physical computing-based robotics. Our unique learning platform brings together a powerful, open-ended coding environment, a teacher-friendly curriculum suite, and an exciting, thoroughly hackable set of hardware tools. CodeSpace helps you deliver real-world learning experiences to your students. Perfect for learning but industry-proven, Python has been used to develop software for Google, YouTube, Spotify, and countless other applications highly relevant to students' lives. Our exclusive use of text-based code, rather than drag and drop blocks, appeals to students seeking relevance, meaning, and real-world value from instruction. Our Python-based curriculum modules lead students (and teachers!) step by step, from basic coding concepts to deep and complex projects. Instructional content is integrated with development tools, so students can practice the mechanics of programming. -
44
Azure DevOps
Microsoft
Azure DevOps is a comprehensive set of modern development tools that help teams plan smarter, collaborate better, and deliver software faster. It provides services like Azure Boards for work tracking, Azure Pipelines for continuous integration and deployment, Azure Repos for Git-based source control, and Azure Test Plans for quality assurance. With built-in support for GitHub Copilot, developers can boost productivity by leveraging AI-assisted coding. The platform offers seamless integration with a variety of tools and supports any language, platform, or cloud environment. Azure DevOps emphasizes security with extensive compliance certifications and a dedicated engineering team. Trusted by leading global companies, it enables organizations to accelerate development cycles while maintaining high code quality and operational agility.Starting Price: $6 per user per month -
45
SSDN Technologies
SSDN Technologies
SSDN Technologies is one of the leading IT Training in India, which located in Gurgaon location. We are authorized partner of Citrix, VMware, Microsoft, EC-Council, CompTIA, IBM, etc... SSDN Technologies deliver official training on IT courses like VMware, Citrix, Microsoft, CEH, AWS, Azure, Machine learning, CCNA, Linux, Java, Python, Digital Marketing and many more courses and certification. You can take official training, corporate training, online/offline classes from Certified experienced trainers. If you anyone want to start and upgrade your career in technology, this is the right place to make successful career. -
46
Launchable
Launchable
You can have the best developers in the world, but every test is making them slower. 80% of your software tests are pointless. The problem is you don't know which 80%. We find the right 20% using your data so that you can ship faster. We have shrink-wrapped predictive test selection, a machine learning-based approach being used at companies like Facebook so that it can be used by any company. We support multiple languages, test runners, and CI systems. Just bring Git to the table. Launchable uses machine learning to analyze your test failures and source code. It doesn't rely on code syntax analysis. This means it's trivial for Launchable to add support for almost any file-based programming language. It also means we can scale across teams and projects with different languages and tools. Out of the box, we currently support Python, Ruby, Java, JavaScript, Go, C, and C++, and we regularly add support for new languages. -
47
Amazon SageMaker JumpStart
Amazon
Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access built-in algorithms with pretrained models from model hubs, pretrained foundation models to help you perform tasks such as article summarization and image generation, and prebuilt solutions to solve common use cases. In addition, you can share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment. SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis. -
48
Azure Marketplace
Microsoft
Azure Marketplace is a comprehensive online store that provides access to thousands of certified, ready-to-use software applications, services, and solutions from Microsoft and third-party vendors. It enables businesses to discover, purchase, and deploy software directly within the Azure cloud environment. The marketplace offers a wide range of products, including virtual machine images, AI and machine learning models, developer tools, security solutions, and industry-specific applications. With flexible pricing options like pay-as-you-go, free trials, and subscription models, Azure Marketplace simplifies the procurement process and centralizes billing through a single Azure invoice. It supports seamless integration with Azure services, enabling organizations to enhance their cloud infrastructure, streamline workflows, and accelerate digital transformation initiatives. -
49
Azure Media Player
Microsoft
Azure Media Player automatically picks the best format for a browser or device, and uses the dynamic packaging capabilities of Azure Media Services to play adaptive streaming content in formats like MPEG-DASH, Microsoft Smooth Streaming, or Apple HTTP Live Streaming (HLS). Media Player is also designed to select the correct technology based on the platform—HTML5 (MSE/EME), Adobe Flash Player, or Microsoft Silverlight. Regardless of the playback technology, there’s a simple interface to access APIs. Media Player gives you simplified development using standard HTML5 video tags. A unified JavaScript interface lets advanced customers create a media player experience without having to develop for specific platforms or features. -
50
Anaconda
Anaconda
Anaconda is an AI-native development platform that helps teams move from experimentation to production with trusted open-source packages, governed environments, and production-grade orchestration. The platform provides a secure foundation for Python, data science, machine learning, and AI development across the full model lifecycle. Anaconda Core helps teams manage complex Python dependencies with validated packages, automated security scanning, and intelligent conflict resolution. The Anaconda Platform supports governed AI development so organizations can reduce broken environments, stalled deployments, and unmanaged open-source risk. Its trusted distribution is used by millions of users, developers, contributors, organizations, and Fortune 500 companies. Built for enterprise AI teams, Anaconda helps organizations accelerate open-source AI innovation while maintaining control, security, and governance.
