Alternatives to NVIDIA FLARE
Compare NVIDIA FLARE alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to NVIDIA FLARE in 2025. Compare features, ratings, user reviews, pricing, and more from NVIDIA FLARE competitors and alternatives in order to make an informed decision for your business.
-
1
RunPod
RunPod
RunPod offers a cloud-based platform designed for running AI workloads, focusing on providing scalable, on-demand GPU resources to accelerate machine learning (ML) model training and inference. With its diverse selection of powerful GPUs like the NVIDIA A100, RTX 3090, and H100, RunPod supports a wide range of AI applications, from deep learning to data processing. The platform is designed to minimize startup time, providing near-instant access to GPU pods, and ensures scalability with autoscaling capabilities for real-time AI model deployment. RunPod also offers serverless functionality, job queuing, and real-time analytics, making it an ideal solution for businesses needing flexible, cost-effective GPU resources without the hassle of managing infrastructure. -
2
Amazon Bedrock
Amazon
Amazon Bedrock is a fully managed service that simplifies building and scaling generative AI applications by providing access to a variety of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself. Through a single API, developers can experiment with these models, customize them using techniques like fine-tuning and Retrieval Augmented Generation (RAG), and create agents that interact with enterprise systems and data sources. As a serverless platform, Amazon Bedrock eliminates the need for infrastructure management, allowing seamless integration of generative AI capabilities into applications with a focus on security, privacy, and responsible AI practices. -
3
BentoML
BentoML
Serve your ML model in any cloud in minutes. Unified model packaging format enabling both online and offline serving on any platform. 100x the throughput of your regular flask-based model server, thanks to our advanced micro-batching mechanism. Deliver high-quality prediction services that speak the DevOps language and integrate perfectly with common infrastructure tools. Unified format for deployment. High-performance model serving. DevOps best practices baked in. The service uses the BERT model trained with the TensorFlow framework to predict movie reviews' sentiment. DevOps-free BentoML workflow, from prediction service registry, deployment automation, to endpoint monitoring, all configured automatically for your team. A solid foundation for running serious ML workloads in production. Keep all your team's models, deployments, and changes highly visible and control access via SSO, RBAC, client authentication, and auditing logs.Starting Price: Free -
4
Amazon SageMaker
Amazon
Amazon SageMaker is an advanced machine learning service that provides an integrated environment for building, training, and deploying machine learning (ML) models. It combines tools for model development, data processing, and AI capabilities in a unified studio, enabling users to collaborate and work faster. SageMaker supports various data sources, such as Amazon S3 data lakes and Amazon Redshift data warehouses, while ensuring enterprise security and governance through its built-in features. The service also offers tools for generative AI applications, making it easier for users to customize and scale AI use cases. SageMaker’s architecture simplifies the AI lifecycle, from data discovery to model deployment, providing a seamless experience for developers. -
5
TensorFlow
TensorFlow
An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.Starting Price: Free -
6
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 -
7
Azure Machine Learning
Microsoft
Accelerate the end-to-end machine learning lifecycle. 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. -
8
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 -
9
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.
-
10
WhyLabs
WhyLabs
Enable observability to detect data and ML issues faster, deliver continuous improvements, and avoid costly incidents. Start with reliable data. Continuously monitor any data-in-motion for data quality issues. Pinpoint data and model drift. Identify training-serving skew and proactively retrain. Detect model accuracy degradation by continuously monitoring key performance metrics. Identify risky behavior in generative AI applications and prevent data leakage. Protect your generative AI applications are safe from malicious actions. Improve AI applications through user feedback, monitoring, and cross-team collaboration. Integrate in minutes with purpose-built agents that analyze raw data without moving or duplicating it, ensuring privacy and security. Onboard the WhyLabs SaaS Platform for any use cases using the proprietary privacy-preserving integration. Security approved for healthcare and banks. -
11
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 -
12
Options for every business to train deep learning and machine learning models cost-effectively. AI accelerators for every use case, from low-cost inference to high-performance training. Simple to get started with a range of services for development and deployment. Tensor Processing Units (TPUs) are custom-built ASIC to train and execute deep neural networks. Train and run more powerful and accurate models cost-effectively with faster speed and scale. A range of NVIDIA GPUs to help with cost-effective inference or scale-up or scale-out training. Leverage RAPID and Spark with GPUs to execute deep learning. Run GPU workloads on Google Cloud where you have access to industry-leading storage, networking, and data analytics technologies. Access CPU platforms when you start a VM instance on Compute Engine. Compute Engine offers a range of both Intel and AMD processors for your VMs.
-
13
SuperDuperDB
SuperDuperDB
Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on top of it. SuperDuperDB enables vector search in your existing database. Integrate and combine models from Sklearn, PyTorch, and HuggingFace with AI APIs such as OpenAI to build even the most complex AI applications and workflows. Deploy all your AI models to automatically compute outputs (inference) in your datastore in a single environment with simple Python commands. -
14
PyTorch
PyTorch
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies. -
15
Caffe
BAIR
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU. -
16
NVIDIA AI Foundations
NVIDIA
Impacting virtually every industry, generative AI unlocks a new frontier of opportunities, for knowledge and creative workers, to solve today’s most important challenges. NVIDIA is powering generative AI through an impressive suite of cloud services, pre-trained foundation models, as well as cutting-edge frameworks, optimized inference engines, and APIs to bring intelligence to your enterprise applications. NVIDIA AI Foundations is a set of cloud services that advance enterprise-level generative AI and enable customization across use cases in areas such as text (NVIDIA NeMo™), visual content (NVIDIA Picasso), and biology (NVIDIA BioNeMo™). Unleash the full potential with NeMo, Picasso, and BioNeMo cloud services, powered by NVIDIA DGX™ Cloud, the AI supercomputer. Marketing copy, storyline creation, and global translation in many languages. For news, email, meeting minutes, and information synthesis. -
17
NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.Starting Price: Free
-
18
Granica
Granica
The Granica AI efficiency platform reduces the cost to store and access data while preserving its privacy to unlock it for training. Granica is developer-first, petabyte-scale, and AWS/GCP-native. Granica makes AI pipelines more efficient, privacy-preserving, and more performant. Efficiency is a new layer in the AI stack. Byte-granular data reduction uses novel compression algorithms, cutting costs to store and transfer objects in Amazon S3 and Google Cloud Storage by up to 80% and API costs by up to 90%. Estimate in 30 mins in your cloud environment, on a read-only sample of your S3/GCS data. No need for budget allocation or total cost of ownership analysis. Granica deploys into your environment and VPC, respecting all of your security policies. Granica supports a wide range of data types for AI/ML/analytics, with lossy and fully lossless compression variants. Detect and protect sensitive data even before it is persisted into your cloud object store. -
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
PromptQL
Hasura
PromptQL is a platform developed by Hasura that enables Large Language Models (LLMs) to access and interact with structured data sources through agentic query planning. This approach allows AI agents to retrieve and process data in a human-like manner, enhancing their ability to handle complex, real-world user queries. By providing LLMs with access to a Python runtime and a standardized SQL interface, PromptQL facilitates accurate data querying and manipulation. The platform supports integration with various data sources, including GitHub repositories and PostgreSQL databases, allowing users to build AI assistants tailored to their specific needs. PromptQL addresses the limitations of traditional search-based retrieval methods by enabling AI agents to perform tasks such as gathering relevant emails and classifying follow-ups with greater accuracy. Users can get started by connecting their data, adding their LLM API key, and building with AI. -
21
Griptape
Griptape AI
Build, deploy, and scale end-to-end AI applications in the cloud. Griptape gives developers everything they need to build, deploy, and scale retrieval-driven AI-powered applications, from the development framework to the execution runtime. 🎢 Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. ☁️ Griptape Cloud is a one-stop shop to hosting your AI structures, whether they are built with Griptape, another framework, or call directly to the LLMs themselves. Simply point to your GitHub repository to get started. 🔥 Run your hosted code by hitting a basic API layer from wherever you need, offloading the expensive tasks of AI development to the cloud. 📈 Automatically scale workloads to fit your needs.Starting Price: Free -
22
Together AI
Together AI
Whether prompt engineering, fine-tuning, or training, we are ready to meet your business demands. Easily integrate your new model into your production application using the Together Inference API. With the fastest performance available and elastic scaling, Together AI is built to scale with your needs as you grow. Inspect how models are trained and what data is used to increase accuracy and minimize risks. You own the model you fine-tune, not your cloud provider. Change providers for whatever reason, including price changes. Maintain complete data privacy by storing data locally or in our secure cloud.Starting Price: $0.0001 per 1k tokens -
23
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 -
24
Tune Studio
NimbleBox
Tune Studio is an intuitive and versatile platform designed to streamline the fine-tuning of AI models with minimal effort. It empowers users to customize pre-trained machine learning models to suit their specific needs without requiring extensive technical expertise. With its user-friendly interface, Tune Studio simplifies the process of uploading datasets, configuring parameters, and deploying fine-tuned models efficiently. Whether you're working on NLP, computer vision, or other AI applications, Tune Studio offers robust tools to optimize performance, reduce training time, and accelerate AI development, making it ideal for both beginners and advanced users in the AI space.Starting Price: $10/user/month -
25
Hugging Face
Hugging Face
Hugging Face is a leading platform for AI and machine learning, offering a vast hub for models, datasets, and tools for natural language processing (NLP) and beyond. The platform supports a wide range of applications, from text, image, and audio to 3D data analysis. Hugging Face fosters collaboration among researchers, developers, and companies by providing open-source tools like Transformers, Diffusers, and Tokenizers. It enables users to build, share, and access pre-trained models, accelerating AI development for a variety of industries.Starting Price: $9 per month -
26
Xilinx
Xilinx
The Xilinx’s AI development platform for AI inference on Xilinx hardware platforms consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP. Supports mainstream frameworks and the latest models capable of diverse deep learning tasks. Provides a comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices. You can find the closest model and start re-training for your applications! Provides a powerful open source quantizer that supports pruned and unpruned model quantization, calibration, and fine tuning. The AI profiler provides layer by layer analysis to help with bottlenecks. The AI library offers open source high-level C++ and Python APIs for maximum portability from edge to cloud. Efficient and scalable IP cores can be customized to meet your needs of many different applications. -
27
alwaysAI
alwaysAI
alwaysAI provides developers with a simple and flexible way to build, train, and deploy computer vision applications to a wide variety of IoT devices. Select from a catalog of deep learning models or upload your own. Use our flexible and customizable APIs to quickly enable core computer vision services. Quickly prototype, test and iterate with a variety of camera-enabled ARM-32, ARM-64 and x86 devices. Identify objects in an image by name or classification. Identify and count objects appearing in a real-time video feed. Follow the same object across a series of frames. Find faces or full bodies in a scene to count or track. Locate and define borders around separate objects. Separate key objects in an image from background visuals. Determine human body poses, fall detection, emotions. Use our model training toolkit to train an object detection model to identify virtually any object. Create a model tailored to your specific use-case. -
28
Viso Suite
Viso Suite
Viso Suite is the world’s only end-to-end platform for computer vision. It enables teams to rapidly train, create, deploy and manage computer vision applications – without writing code from scratch. Use Viso Suite to deliver industry-leading computer vision and real-time deep learning systems with low-code and automated software infrastructure. The use of traditional development methods, fragmented software tools, and the lack of experienced engineers are costing organizations lots of time and leading to inefficient, low-performing, and expensive computer vision systems. Build and deploy better computer vision applications faster by abstracting and automating the entire lifecycle with Viso Suite, the all-in-one enterprise vision platform. Collect data for computer vision annotation with Viso Suite. Use automated collection capabilities to gather high-quality training data. Control and secure all data collection. Enable continuous data collection to further improve your AI models. -
29
IBM watsonx.ai
IBM
Now available—a next generation enterprise studio for AI builders to train, validate, tune and deploy AI models IBM® watsonx.ai™ AI studio is part of the IBM watsonx™ AI and data platform, bringing together new generative AI (gen AI) capabilities powered by foundation models and traditional machine learning (ML) into a powerful studio spanning the AI lifecycle. Tune and guide models with your enterprise data to meet your needs with easy-to-use tools for building and refining performant prompts. With watsonx.ai, you can build AI applications in a fraction of the time and with a fraction of the data. Watsonx.ai offers: End-to-end AI governance: Enterprises can scale and accelerate the impact of AI with trusted data across the business, using data wherever it resides. Hybrid, multi-cloud deployments: IBM provides the flexibility to integrate and deploy your AI workloads into your hybrid-cloud stack of choice. -
30
Cerebrium
Cerebrium
Deploy all major ML frameworks such as Pytorch, Onnx, XGBoost etc with 1 line of code. Don't have your own models? Deploy our prebuilt models that have been optimised to run with sub-second latency. Fine-tune smaller models on particular tasks in order to decrease costs and latency while increasing performance. It takes just a few lines of code and don't worry about infrastructure, we got it. Integrate with top ML observability platforms in order to be alerted about feature or prediction drift, compare model versions and resolve issues quickly. Discover the root causes for prediction and feature drift to resolve degraded model performance. Understand which features are contributing most to the performance of your model.Starting Price: $ 0.00055 per second -
31
Stochastic
Stochastic
Enterprise-ready AI system that trains locally on your data, deploys on your cloud and scales to millions of users without an engineering team. Build customize and deploy your own chat-based AI. Finance chatbot. xFinance, a 13-billion parameter model fine-tuned on an open-source model using LoRA. Our goal was to show that it is possible to achieve impressive results in financial NLP tasks without breaking the bank. Personal AI assistant, your own AI to chat with your documents. Single or multiple documents, easy or complex questions, and much more. Effortless deep learning platform for enterprises, hardware efficient algorithms to speed up inference at a lower cost. Real-time logging and monitoring of resource utilization and cloud costs of deployed models. xTuring is an open-source AI personalization software. xTuring makes it easy to build and control LLMs by providing a simple interface to personalize LLMs to your own data and application. -
32
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.
-
33
NVIDIA AI Enterprise
NVIDIA
The software layer of the NVIDIA AI platform, NVIDIA AI Enterprise accelerates the data science pipeline and streamlines development and deployment of production AI including generative AI, computer vision, speech AI and more. With over 50 frameworks, pretrained models and development tools, NVIDIA AI Enterprise is designed to accelerate enterprises to the leading edge of AI, while also simplifying AI to make it accessible to every enterprise. The adoption of artificial intelligence and machine learning has gone mainstream, and is core to nearly every company’s competitive strategy. One of the toughest challenges for enterprises is the struggle with siloed infrastructure across the cloud and on-premises data centers. AI requires their environments to be managed as a common platform, instead of islands of compute. -
34
TensorBlock
TensorBlock
TensorBlock is an open source AI infrastructure platform designed to democratize access to large language models through two complementary components. It has a self-hosted, privacy-first API gateway that unifies connections to any LLM provider under a single, OpenAI-compatible endpoint, with encrypted key management, dynamic model routing, usage analytics, and cost-optimized orchestration. TensorBlock Studio delivers a lightweight, developer-friendly multi-LLM interaction workspace featuring a plugin-based UI, extensible prompt workflows, real-time conversation history, and integrated natural-language APIs for seamless prompt engineering and model comparison. Built on a modular, scalable architecture and guided by principles of openness, composability, and fairness, TensorBlock enables organizations to experiment, deploy, and manage AI agents with full control and minimal infrastructure overhead.Starting Price: Free -
35
NVIDIA NeMo Guardrails
NVIDIA
NVIDIA NeMo Guardrails is an open-source toolkit designed to enhance the safety, security, and compliance of large language model-based conversational applications. It enables developers to define, orchestrate, and enforce multiple AI guardrails, ensuring that generative AI interactions remain accurate, appropriate, and on-topic. The toolkit leverages Colang, a specialized language for designing flexible dialogue flows, and integrates seamlessly with popular AI development frameworks like LangChain and LlamaIndex. NeMo Guardrails offers features such as content safety, topic control, personal identifiable information detection, retrieval-augmented generation enforcement, and jailbreak prevention. Additionally, the recently introduced NeMo Guardrails microservice simplifies rail orchestration with API-based interaction and tools for enhanced guardrail management and maintenance. -
36
Alibaba Cloud Machine Learning Platform for AI
Alibaba Cloud
An end-to-end platform that provides various machine learning algorithms to meet your data mining and analysis requirements. Machine Learning Platform for AI provides end-to-end machine learning services, including data processing, feature engineering, model training, model prediction, and model evaluation. Machine learning platform for AI combines all of these services to make AI more accessible than ever. Machine Learning Platform for AI provides a visualized web interface allowing you to create experiments by dragging and dropping different components to the canvas. Machine learning modeling is a simple, step-by-step procedure, improving efficiencies and reducing costs when creating an experiment. Machine Learning Platform for AI provides more than one hundred algorithm components, covering such scenarios as regression, classification, clustering, text analysis, finance, and time series.Starting Price: $1.872 per hour -
37
Supavec
Supavec
Supavec is an open source Retrieval-Augmented Generation (RAG) platform designed to help developers build powerful AI applications that integrate seamlessly with any data source, regardless of scale. As an alternative to Carbon.ai, Supavec offers full control over your AI infrastructure, allowing you to choose between a cloud version or self-hosting on your own systems. Built with technologies like Supabase, Next.js, and TypeScript, Supavec ensures scalability, enabling the handling of millions of documents with support for concurrent processing and horizontal scaling. The platform emphasizes enterprise-grade privacy by utilizing Supabase Row Level Security (RLS), ensuring that your data remains private and secure with granular access control. Developers benefit from a simple API, comprehensive documentation, and easy integration, facilitating quick setup and deployment of AI applications.Starting Price: Free -
38
Predibase
Predibase
Declarative machine learning systems provide the best of flexibility and simplicity to enable the fastest-way to operationalize state-of-the-art models. Users focus on specifying the “what”, and the system figures out the “how”. Start with smart defaults, but iterate on parameters as much as you’d like down to the level of code. Our team pioneered declarative machine learning systems in industry, with Ludwig at Uber and Overton at Apple. Choose from our menu of prebuilt data connectors that support your databases, data warehouses, lakehouses, and object storage. Train state-of-the-art deep learning models without the pain of managing infrastructure. Automated Machine Learning that strikes the balance of flexibility and control, all in a declarative fashion. With a declarative approach, finally train and deploy models as quickly as you want. -
39
Cargoship
Cargoship
Select a model from our open source collection, run the container and access the model API in your product. No matter if Image Recognition or Language Processing - all models are pre-trained and packaged in an easy-to-use API. Choose from a large selection of models that is always growing. We curate and fine-tune the best models from HuggingFace and Github. You can either host the model yourself very easily or get your personal endpoint and API-Key with one click. Cargoship is keeping up with the development of the AI space so you don’t have to. With the Cargoship Model Store you get a collection for every ML use case. On the website you can try them out in demos and get detailed guidance from what the model does to how to implement it. Whatever your level of expertise, we will pick you up and give you detailed instructions. -
40
Fireworks AI
Fireworks AI
Fireworks partners with the world's leading generative AI researchers to serve the best models, at the fastest speeds. Independently benchmarked to have the top speed of all inference providers. Use powerful models curated by Fireworks or our in-house trained multi-modal and function-calling models. Fireworks is the 2nd most used open-source model provider and also generates over 1M images/day. Our OpenAI-compatible API makes it easy to start building with Fireworks. Get dedicated deployments for your models to ensure uptime and speed. Fireworks is proudly compliant with HIPAA and SOC2 and offers secure VPC and VPN connectivity. Meet your needs with data privacy - own your data and your models. Serverless models are hosted by Fireworks, there's no need to configure hardware or deploy models. Fireworks.ai is a lightning-fast inference platform that helps you serve generative AI models.Starting Price: $0.20 per 1M tokens -
41
Teachable Machine
Teachable Machine
A fast, easy way to create machine learning models for your sites, apps, and more – no expertise or coding required. Teachable Machine is flexible – use files or capture examples live. It’s respectful of the way you work. You can even choose to use it entirely on-device, without any webcam or microphone data leaving your computer. Teachable Machine is a web-based tool that makes creating machine learning models fast, easy, and accessible to everyone. Educators, artists, students, innovators, makers of all kinds – really, anyone who has an idea they want to explore. No prerequisite machine learning knowledge required. You train a computer to recognize your images, sounds, and poses without writing any machine learning code. Then, use your model in your own projects, sites, apps, and more. -
42
Tencent Cloud TI Platform
Tencent
Tencent Cloud TI Platform is a one-stop machine learning service platform designed for AI engineers. It empowers AI development throughout the entire process from data preprocessing to model building, model training, model evaluation, and model service. Preconfigured with diverse algorithm components, it supports multiple algorithm frameworks to adapt to different AI use cases. Tencent Cloud TI Platform delivers a one-stop machine learning experience that covers a complete and closed-loop workflow from data preprocessing to model building, model training, and model evaluation. With Tencent Cloud TI Platform, even AI beginners can have their models constructed automatically, making it much easier to complete the entire training process. Tencent Cloud TI Platform's auto-tuning tool can also further enhance the efficiency of parameter tuning. Tencent Cloud TI Platform allows CPU/GPU resources to elastically respond to different computing power needs with flexible billing modes. -
43
Composio
Composio
Composio is an integration platform designed to enhance AI agents and Large Language Models (LLMs) by providing seamless connections to over 150 tools with minimal code. It supports a wide array of agentic frameworks and LLM providers, facilitating function calling for efficient task execution. Composio offers a comprehensive repository of tools, including GitHub, Salesforce, file management systems, and code execution environments, enabling AI agents to perform diverse actions and subscribe to various triggers. The platform features managed authentication, allowing users to oversee authentication processes for all users and agents from a centralized dashboard. Composio's core capabilities include a developer-first integration approach, built-in authentication management, an expanding catalog of over 90 ready-to-connect tools, a 30% increase in reliability through simplified JSON structures and improved error handling, SOC Type II compliance ensuring maximum data security.Starting Price: $49 per month -
44
Determined AI
Determined AI
Distributed training without changing your model code, determined takes care of provisioning machines, networking, data loading, and fault tolerance. Our open source deep learning platform enables you to train models in hours and minutes, not days and weeks. Instead of arduous tasks like manual hyperparameter tuning, re-running faulty jobs, and worrying about hardware resources. Our distributed training implementation outperforms the industry standard, requires no code changes, and is fully integrated with our state-of-the-art training platform. With built-in experiment tracking and visualization, Determined records metrics automatically, makes your ML projects reproducible and allows your team to collaborate more easily. Your researchers will be able to build on the progress of their team and innovate in their domain, instead of fretting over errors and infrastructure. -
45
Simplismart
Simplismart
Fine-tune and deploy AI models with Simplismart's fastest inference engine. Integrate with AWS/Azure/GCP and many more cloud providers for simple, scalable, cost-effective deployment. Import open source models from popular online repositories or deploy your own custom model. Leverage your own cloud resources or let Simplismart host your model. With Simplismart, you can go far beyond AI model deployment. You can train, deploy, and observe any ML model and realize increased inference speeds at lower costs. Import any dataset and fine-tune open-source or custom models rapidly. Run multiple training experiments in parallel efficiently to speed up your workflow. Deploy any model on our endpoints or your own VPC/premise and see greater performance at lower costs. Streamlined and intuitive deployment is now a reality. Monitor GPU utilization and all your node clusters in one dashboard. Detect any resource constraints and model inefficiencies on the go. -
46
Xero.AI
Xero.AI
Building an AI-powered machine learning engineer that can handle all your data science and ML needs. Xero's artificial analyst is the future of data science and ML. Just ask Xara what you want to do with your data and she will do it for you. Explore your data and create custom visuals using natural language to help you better understand your data and generate insights. Clean and transform your data and extract new features in the most seamless way possible. Create, train, and test unlimited customizable machine learning models by simply asking XARA.Starting Price: $30 per month -
47
Entry Point AI
Entry Point AI
Entry Point AI is the modern AI optimization platform for proprietary and open source language models. Manage prompts, fine-tunes, and evals all in one place. When you reach the limits of prompt engineering, it’s time to fine-tune a model, and we make it easy. Fine-tuning is showing a model how to behave, not telling. It works together with prompt engineering and retrieval-augmented generation (RAG) to leverage the full potential of AI models. Fine-tuning can help you to get better quality from your prompts. Think of it like an upgrade to few-shot learning that bakes the examples into the model itself. For simpler tasks, you can train a lighter model to perform at or above the level of a higher-quality model, greatly reducing latency and cost. Train your model not to respond in certain ways to users, for safety, to protect your brand, and to get the formatting right. Cover edge cases and steer model behavior by adding examples to your dataset.Starting Price: $49 per month -
48
Foundry Local
Microsoft
Foundry Local is a local version of Azure AI Foundry that enables local execution of large language models (LLMs) directly on your Windows device. This on-device AI inference solution provides privacy, customization, and cost benefits compared to cloud-based alternatives. Best of all, it fits into your existing workflows and applications with an easy-to-use CLI and REST API. -
49
Exspanse
Exspanse
Exspanse streamlines the path from development to business value. Build, train & rapidly deploy powerful machine learning models from a single user interface that can scale with your business. Train, tune, and prototype models from the Exspanse Notebook with the help of high-powered GPUs, CPUs & our AI code assistant. Think beyond training & modeling when you can use the rapid deploy feature to deploy models as an API right from an Exspanse Notebook. Clone and publish unique AI projects to DeepSpace AI marketplace to advance the AI community. Power, efficiency, and collaboration in one comprehensive platform. Unleash your full potential as a solo data scientist while maximizing your impact. Manage and accelerate your AI development process through our integrated platform. Turn your innovative ideas into working models quickly and effectively. Seamlessly transition from building to deploying AI solutions, without the need for extensive DevOps knowledge.Starting Price: $50 per month -
50
Amazon Bedrock Guardrails
Amazon
Amazon Bedrock Guardrails is a configurable safeguard system designed to enhance the safety and compliance of generative AI applications built on Amazon Bedrock. It enables developers to implement customized safety, privacy, and truthfulness controls across various foundation models, including those hosted within Amazon Bedrock, fine-tuned models, and self-hosted models. Guardrails provide a consistent approach to enforcing responsible AI policies by evaluating both user inputs and model responses based on defined policies. These policies include content filters for harmful text and image content, denial of specific topics, word filters for undesirable terms, sensitive information filters to redact personally identifiable information, and contextual grounding checks to detect and filter hallucinations in model responses.
