Business Software for PyTorch - Page 2
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Top Software that integrates with PyTorch as of October 2025 - Page 2
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1
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 -
2
Akira AI
Akira AI
Akira.ai provides businesses with Agentic AI, a set of specialized AI agents designed to optimize and automate complex workflows across various industries. These AI agents collaborate with human teams, enhancing productivity, making real-time decisions, and automating repetitive tasks, such as data analysis, incident management, and HR processes. The platform integrates smoothly with existing systems, including CRMs and ERPs, ensuring a disruption-free transition to AI-enhanced operations. Akira’s AI agents help businesses streamline their operations, increase decision-making speed, and boost overall efficiency, driving innovation across sectors like manufacturing, finance, and IT.Starting Price: $15 per month -
3
ZenML
ZenML
Simplify your MLOps pipelines. Manage, deploy, and scale on any infrastructure with ZenML. ZenML is completely free and open-source. See the magic with just two simple commands. Set up ZenML in a matter of minutes, and start with all the tools you already use. ZenML standard interfaces ensure that your tools work together seamlessly. Gradually scale up your MLOps stack by switching out components whenever your training or deployment requirements change. Keep up with the latest changes in the MLOps world and easily integrate any new developments. Define simple and clear ML workflows without wasting time on boilerplate tooling or infrastructure code. Write portable ML code and switch from experimentation to production in seconds. Manage all your favorite MLOps tools in one place with ZenML's plug-and-play integrations. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code.Starting Price: Free -
4
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 -
5
DeepSpeed
Microsoft
DeepSpeed is an open source deep learning optimization library for PyTorch. It's designed to reduce computing power and memory use, and to train large distributed models with better parallelism on existing computer hardware. DeepSpeed is optimized for low latency, high throughput training. DeepSpeed can train DL models with over a hundred billion parameters on the current generation of GPU clusters. It can also train up to 13 billion parameters in a single GPU. DeepSpeed is developed by Microsoft and aims to offer distributed training for large-scale models. It's built on top of PyTorch, which specializes in data parallelism.Starting Price: Free -
6
Voxel51
Voxel51
FiftyOne by Voxel51 - the most powerful visual AI and computer vision data platform. Without the right data, even the smartest AI models fail. FiftyOne gives machine learning engineers the power to deeply understand and evaluate their visual datasets—across images, videos, 3D point clouds, geospatial, and medical data. With over 2.8 million open source installs and customers like Walmart, GM, Bosch, Medtronic, and the University of Michigan Health, FiftyOne is an indispensable tool for building computer vision systems that work in the real world, not just in the lab. FiftyOne streamlines visual data curation and model analysis with workflows to simplify the labor-intensive processes of visualizing and analyzing insights during data curation and model refinement—addressing a major challenge in large-scale data pipelines with billions of samples. Proven impact with FiftyOne: ⬆️30% increase in model accuracy ⏱️5+ months of development time saved 📈30% boost in productivityStarting Price: $0 -
7
PostgresML
PostgresML
PostgresML is a complete platform in a PostgreSQL extension. Build simpler, faster, and more scalable models right inside your database. Explore the SDK and test open source models in our hosted database. Combine and automate the entire workflow from embedding generation to indexing and querying for the simplest (and fastest) knowledge-based chatbot implementation. Leverage multiple types of natural language processing and machine learning models such as vector search and personalization with embeddings to improve search results. Leverage your data with time series forecasting to garner key business insights. Build statistical and predictive models with the full power of SQL and dozens of regression algorithms. Return results and detect fraud faster with ML at the database layer. PostgresML abstracts the data management overhead from the ML/AI lifecycle by enabling users to run ML/LLM models directly on a Postgres database.Starting Price: $.60 per hour -
8
Yandex DataSphere
Yandex.Cloud
Select the configuration and resources needed for specific code segments in your ongoing project. It takes seconds to apply changes within a training scenario and save the work result. Choose the right configuration for computing resources to start training models in just a few seconds. Everything will be created automatically with no need to manage infrastructure. Choose an operating mode: serverless or dedicated. Manage project data, save it to datasets, and set up connections to databases, object storage, or other repositories, all in one interface. Collaborate with colleagues around the world to create an ML model, share the project, and set budgets for teams across your organization. Launch your ML in minutes, without the help of developers. Run experiments with simultaneous publication of different versions of models.Starting Price: $0.095437 per GB -
9
Unify AI
Unify AI
Explore the power of choosing the right LLM for your needs and how to optimize for quality, speed, and cost-efficiency. Access all LLMs across all providers with a single API key and a standard API. Setup your own cost, latency, and output speed constraints. Define a custom quality metric. Personalize your router for your requirements. Systematically send your queries to the fastest provider, based on the very latest benchmark data for your region of the world, refreshed every 10 minutes. Get started with Unify with our dedicated walkthrough. Discover the features you already have access to and our upcoming roadmap. Just create a Unify account to access all models from all supported providers with a single API key. Our router balances output quality, speed, and cost based on user-specific preferences. The quality is predicted ahead of time using a neural scoring function, which predicts how good each model would be at responding to a given prompt.Starting Price: $1 per credit -
10
CodeQwen
Alibaba
CodeQwen is the code version of Qwen, the large language model series developed by the Qwen team, Alibaba Cloud. It is a transformer-based decoder-only language model pre-trained on a large amount of data of codes. Strong code generation capabilities and competitive performance across a series of benchmarks. Supporting long context understanding and generation with the context length of 64K tokens. CodeQwen supports 92 coding languages and provides excellent performance in text-to-SQL, bug fixes, etc. You can just write several lines of code with transformers to chat with CodeQwen. Essentially, we build the tokenizer and the model from pre-trained methods, and we use the generate method to perform chatting with the help of the chat template provided by the tokenizer. We apply the ChatML template for chat models following our previous practice. The model completes the code snippets according to the given prompts, without any additional formatting.Starting Price: Free -
11
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 -
12
Keepsake
Replicate
Keepsake is an open-source Python library designed to provide version control for machine learning experiments and models. It enables users to automatically track code, hyperparameters, training data, model weights, metrics, and Python dependencies, ensuring that all aspects of the machine learning workflow are recorded and reproducible. Keepsake integrates seamlessly with existing workflows by requiring minimal code additions, allowing users to continue training as usual while Keepsake saves code and weights to Amazon S3 or Google Cloud Storage. This facilitates the retrieval of code and weights from any checkpoint, aiding in re-training or model deployment. Keepsake supports various machine learning frameworks, including TensorFlow, PyTorch, scikit-learn, and XGBoost, by saving files and dictionaries in a straightforward manner. It also offers features such as experiment comparison, enabling users to analyze differences in parameters, metrics, and dependencies across experiments.Starting Price: Free -
13
Guild AI
Guild AI
Guild AI is an open-source experiment tracking toolkit designed to bring systematic control to machine learning workflows, enabling users to build better models faster. It automatically captures every detail of training runs as unique experiments, facilitating comprehensive tracking and analysis. Users can compare and analyze runs to deepen their understanding and incrementally improve models. Guild AI simplifies hyperparameter tuning by applying state-of-the-art algorithms through straightforward commands, eliminating the need for complex trial setups. It also supports the automation of pipelines, accelerating model development, reducing errors, and providing measurable results. The toolkit is platform-agnostic, running on all major operating systems and integrating seamlessly with existing software engineering tools. Guild AI supports various remote storage types, including Amazon S3, Google Cloud Storage, Azure Blob Storage, and SSH servers.Starting Price: Free -
14
NVIDIA TensorRT
NVIDIA
NVIDIA TensorRT is an ecosystem of APIs for high-performance deep learning inference, encompassing an inference runtime and model optimizations that deliver low latency and high throughput for production applications. Built on the CUDA parallel programming model, TensorRT optimizes neural network models trained on all major frameworks, calibrating them for lower precision with high accuracy, and deploying them across hyperscale data centers, workstations, laptops, and edge devices. It employs techniques such as quantization, layer and tensor fusion, and kernel tuning on all types of NVIDIA GPUs, from edge devices to PCs to data centers. The ecosystem includes TensorRT-LLM, an open source library that accelerates and optimizes inference performance of recent large language models on the NVIDIA AI platform, enabling developers to experiment with new LLMs for high performance and quick customization through a simplified Python API.Starting Price: Free -
15
Google AI Edge
Google
Google AI Edge offers a comprehensive suite of tools and frameworks designed to facilitate the deployment of artificial intelligence across mobile, web, and embedded applications. By enabling on-device processing, it reduces latency, allows offline functionality, and ensures data remains local and private. It supports cross-platform compatibility, allowing the same model to run seamlessly across embedded systems. It is also multi-framework compatible, working with models from JAX, Keras, PyTorch, and TensorFlow. Key components include low-code APIs for common AI tasks through MediaPipe, enabling quick integration of generative AI, vision, text, and audio functionalities. Visualize the transformation of your model through conversion and quantification. Overlays the results of the comparisons to debug the hotspots. Explore, debug, and compare your models visually. Overlays comparisons and numerical performance data to identify problematic hotspots.Starting Price: Free -
16
Hugging Face Transformers
Hugging Face
Transformers is a library of pretrained natural language processing, computer vision, audio, and multimodal models for inference and training. Use Transformers to train models on your data, build inference applications, and generate text with large language models. Explore the Hugging Face Hub today to find a model and use Transformers to help you get started right away. Simple and optimized inference class for many machine learning tasks like text generation, image segmentation, automatic speech recognition, document question answering, and more. A comprehensive trainer that supports features such as mixed precision, torch.compile, and FlashAttention for training and distributed training for PyTorch models. Fast text generation with large language models and vision language models. Every model is implemented from only three main classes (configuration, model, and preprocessor) and can be quickly used for inference or training.Starting Price: $9 per month -
17
Flower
Flower
Flower is an open source federated learning framework designed to simplify the development and deployment of machine learning models across decentralized data sources. It enables training on data located on devices or servers without transferring the data itself, thereby enhancing privacy and reducing bandwidth usage. Flower supports a wide range of machine learning frameworks, including PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and XGBoost, and is compatible with various platforms and cloud services like AWS, GCP, and Azure. It offers flexibility through customizable strategies and supports both horizontal and vertical federated learning scenarios. Flower's architecture allows for scalable experiments, with the capability to handle workloads involving tens of millions of clients. It also provides built-in support for privacy-preserving techniques like differential privacy and secure aggregation.Starting Price: Free -
18
NVIDIA FLARE
NVIDIA
NVIDIA FLARE (Federated Learning Application Runtime Environment) is an open source, extensible SDK designed to facilitate federated learning across diverse industries, including healthcare, finance, and automotive. It enables secure, privacy-preserving AI model training by allowing multiple parties to collaboratively train models without sharing raw data. FLARE supports various machine learning frameworks such as PyTorch, TensorFlow, RAPIDS, and XGBoost, making it adaptable to existing workflows. FLARE's componentized architecture allows for customization and scalability, supporting both horizontal and vertical federated learning. It is suitable for applications requiring data privacy and regulatory compliance, such as medical imaging and financial analytics. It is available for download via the NVIDIA NVFlare GitHub repository and PyPi.Starting Price: Free -
19
LiteRT
Google
LiteRT (Lite Runtime), formerly known as TensorFlow Lite, is Google's high-performance runtime for on-device AI. It enables developers to deploy machine learning models across various platforms and microcontrollers. LiteRT supports models from TensorFlow, PyTorch, and JAX, converting them into the efficient FlatBuffers format (.tflite) for optimized on-device inference. Key features include low latency, enhanced privacy by processing data locally, reduced model and binary sizes, and efficient power consumption. The runtime offers SDKs in multiple languages such as Java/Kotlin, Swift, Objective-C, C++, and Python, facilitating integration into diverse applications. Hardware acceleration is achieved through delegates like GPU and iOS Core ML, improving performance on supported devices. LiteRT Next, currently in alpha, introduces a new set of APIs that streamline on-device hardware acceleration.Starting Price: Free -
20
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. -
21
Collimator
Collimator
Collimator is a modeling and simulation platform for hybrid dynamical systems. We allow engineers to design and test complex, mission critical systems in a way that is reliable, secure, fast and intuitive. Our customers are electrical, mechanical and control systems engineers who are using Collimator to increase productivity, improve performance and collaborate more effectively. They do this using our out of the box features including an intuitive block diagram graphical editor, Python blocks to develop custom algorithms, Jupyter notebooks to parametrize and optimize their systems, high performance computing in the cloud and role based access controls. -
22
Lightning AI
Lightning AI
Use our platform to build AI products, train, fine tune and deploy models on the cloud without worrying about infrastructure, cost management, scaling, and other technical headaches. Train, fine tune and deploy models with prebuilt, fully customizable, modular components. Focus on the science and not the engineering. A Lightning component organizes code to run on the cloud, manage its own infrastructure, cloud costs, and more. 50+ optimizations to lower cloud costs and deliver AI in weeks not months. Get enterprise-grade control with consumer-level simplicity to optimize performance, reduce cost, and lower risk. Go beyond a demo. Launch the next GPT startup, diffusion startup, or cloud SaaS ML service in days not months.Starting Price: $10 per credit -
23
Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn, and more. You can also easily add Cloud GPU and Cloud TPU support. Deep Learning VM Image supports the most popular and latest machine learning frameworks, like TensorFlow and PyTorch. To accelerate your model training and deployment, Deep Learning VM Images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers and the Intel® Math Kernel Library. Get started immediately with all the required frameworks, libraries, and drivers pre-installed and tested for compatibility. Deep Learning VM Image delivers a seamless notebook experience with integrated support for JupyterLab.
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24
Coiled
Coiled
Coiled is enterprise-grade Dask made easy. Coiled manages Dask clusters in your AWS or GCP account, making it the easiest and most secure way to run Dask in production. Coiled manages cloud infrastructure for you, deploying on your AWS or Google Cloud account in minutes. Giving you a rock-solid deployment solution with zero effort. Customize cluster node types to fit your analysis needs. Run Dask in Jupyter Notebooks with real-time dashboards and cluster insights. Create software environments easily with customized dependencies for your Dask analysis. Enjoy enterprise-grade security. Reduce costs with SLAs, user-level management, and auto-termination of clusters. Coiled makes it easy to deploy your cluster on AWS or GCP. You can do it in minutes, without a credit card. Launch code from anywhere, including cloud services like AWS SageMaker, open source solutions, like JupyterHub, or even from the comfort of your very own laptop.Starting Price: $0.05 per CPU hour -
25
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. -
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Distributed AI is a computing paradigm that bypasses the need to move vast amounts of data and provides the ability to analyze data at the source. Distributed AI APIs built by IBM Research is a set of RESTful web services with data and AI algorithms to support AI applications across hybrid cloud, distributed, and edge computing environments. Each Distributed AI API addresses the challenges in enabling AI in distributed and edge environments with APIs. The Distributed AI APIs do not focus on the basic requirements of creating and deploying AI pipelines, for example, model training and model serving. You would use your favorite open-source packages such as TensorFlow or PyTorch. Then, you can containerize your application, including the AI pipeline, and deploy these containers at the distributed locations. In many cases, it’s useful to use a container orchestrator such as Kubernetes or OpenShift operators to automate the deployment process.
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Cameralyze
Cameralyze
Empower your product with AI. Our platform offers a vast selection of pre-built models and a user-friendly no-code interface for custom models. Integrate AI seamlessly into your application and gain a competitive edge. Sentiment analysis, also known as opinion mining, is the process of extracting subjective information from text data, such as reviews, social media posts, or customer feedback, and categorizing it as positive, negative, or neutral. This technology has gained increasing importance in recent years, as more and more companies are using it to understand their customers' opinions and needs, and to make data-driven decisions that can improve their products, services, and marketing strategies. Sentiment analysis is a powerful technology that helps companies understand customer feedback and make data-driven decisions to improve their products, services, and marketing strategies.Starting Price: $29 per month -
28
Label Studio
Label Studio
The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Configurable layouts and templates adapt to your dataset and workflow. Detect objects on images, boxes, polygons, circular, and key points supported. Partition the image into multiple segments. Use ML models to pre-label and optimize the process. Webhooks, Python SDK, and API allow you to authenticate, create projects, import tasks, manage model predictions, and more. Save time by using predictions to assist your labeling process with ML backend integration. Connect to cloud object storage and label data there directly with S3 and GCP. Prepare and manage your dataset in our Data Manager using advanced filters. Support multiple projects, use cases, and data types in one platform. Start typing in the config, and you can quickly preview the labeling interface. At the bottom of the page, you have live serialization updates of what Label Studio expects as an input. -
29
Horovod
Horovod
Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve.Starting Price: Free -
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GPUEater
GPUEater
Persistence container technology enables lightweight operation. Pay-per-use in seconds rather than hours or months. Fees will be paid by credit card in the next month. High performance, but low price compared to others. Will be installed in the world's fastest supercomputer by Oak Ridge National Laboratory. Machine learning applications like deep learning, computational fluid dynamics, video encoding, 3D graphics workstation, 3D rendering, VFX, computational finance, seismic analysis, molecular modeling, genomics, and other server-side GPU computation workloads.Starting Price: $0.0992 per hour