Alternatives to Hedra

Compare Hedra alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Hedra in 2026. Compare features, ratings, user reviews, pricing, and more from Hedra competitors and alternatives in order to make an informed decision for your business.

  • 1
    Compute with Hivenet
    Hivenet Compute gives you on-demand RTX 5090 and RTX Pro 6000 GPUs, plus vCPU instances, billed per second while they run. Hivenet owns and operates the infrastructure, with data centres in France, the UAE and the US, so EU teams can keep workloads and data in Europe. Use it for training and fine-tuning, inference, notebooks, rendering and batch jobs. Launch from ready-made templates in a few minutes, or automate everything through the public Compute API. If you'd rather not run the serving layer yourself, the Inference API gives you an OpenAI-compatible endpoint. Teams get organisations with role-based access, prepaid credits, and S3-compatible object storage alongside hivenet compute.
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  • 2
    Runpod

    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.
    4.2 out of 5 stars 230 Ratings for Runpod
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  • 3
    CoreWeave

    CoreWeave

    CoreWeave

    CoreWeave is a cloud infrastructure provider specializing in GPU-based compute solutions tailored for AI workloads. The platform offers scalable, high-performance GPU clusters that optimize the training and inference of AI models, making it ideal for industries like machine learning, visual effects (VFX), and high-performance computing (HPC). CoreWeave provides flexible storage, networking, and managed services to support AI-driven businesses, with a focus on reliability, cost efficiency, and enterprise-grade security. The platform is used by AI labs, research organizations, and businesses to accelerate their AI innovations.
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    Atlas Cloud

    Atlas Cloud

    Atlas Cloud

    Atlas Cloud is a full-modal AI inference platform built for developers who want to run every type of AI model through a single API. It supports chat, reasoning, image, audio, and video inference without requiring multiple providers. Developers can discover, test, and scale over 300 production-ready models from leading AI ecosystems in one unified workspace. Atlas Cloud simplifies experimentation with an interactive playground and one-click model customization. Its infrastructure is designed for high performance, low latency, and production stability at scale. With serverless access, agent solutions, and GPU cloud options, it adapts to different development and deployment needs. Atlas Cloud helps teams build and ship AI-powered applications faster and more efficiently.
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    Amazon SageMaker Model Deployment
    Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case. It provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. From low latency (a few milliseconds) and high throughput (hundreds of thousands of requests per second) to long-running inference for use cases such as natural language processing and computer vision, you can use Amazon SageMaker for all your inference needs.
  • 6
    NVIDIA Triton Inference Server
    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.
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    NetApp AIPod
    NetApp AIPod is a comprehensive AI infrastructure solution designed to streamline the deployment and management of artificial intelligence workloads. By integrating NVIDIA-validated turnkey solutions, such as NVIDIA DGX BasePOD™ and NetApp's cloud-connected all-flash storage, AIPod consolidates analytics, training, and inference capabilities into a single, scalable system. This convergence enables organizations to rapidly implement AI workflows, from model training to fine-tuning and inference, while ensuring robust data management and security. With preconfigured infrastructure optimized for AI tasks, NetApp AIPod reduces complexity, accelerates time to insights, and supports seamless integration into hybrid cloud environments.
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    GMI Cloud

    GMI Cloud

    GMI Cloud

    GMI Cloud provides a complete platform for building scalable AI solutions with enterprise-grade GPU access and rapid model deployment. Its Inference Engine offers ultra-low-latency performance optimized for real-time AI predictions across a wide range of applications. Developers can deploy models in minutes without relying on DevOps, reducing friction in the development lifecycle. The platform also includes a Cluster Engine for streamlined container management, virtualization, and GPU orchestration. Users can access high-performance GPUs, InfiniBand networking, and secure, globally scalable infrastructure. Paired with popular open-source models like DeepSeek R1 and Llama 3.3, GMI Cloud delivers a powerful foundation for training, inference, and production AI workloads.
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    LunaRoute

    LunaRoute

    LunaRoute

    LunaRoute is a hosted AI inference gateway and API for developers and teams running coding agents, personal agents, applications, and automations. It routes requests to a curated catalog of open-weight models through OpenAI-compatible and Anthropic-compatible endpoints, with a command-line client and setup guides for connecting coding tools and agent frameworks. Plans provide priority concurrent inference capacity, overflow handling, model access, API keys, usage reporting, and team controls depending on the plan. An optional hosted MCP server provides web search, document conversion with OCR, and image generation and editing. Enterprise offerings add dedicated deployment options, custom routing, and policy controls. LunaRoute is intended for software developers and engineering teams seeking a managed way to connect existing AI clients to model inference.
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    Intel Tiber AI Cloud
    Intel® Tiber™ AI Cloud is a powerful platform designed to scale AI workloads with advanced computing resources. It offers specialized AI processors, such as the Intel Gaudi AI Processor and Max Series GPUs, to accelerate model training, inference, and deployment. Optimized for enterprise-level AI use cases, this cloud solution enables developers to build and fine-tune models with support for popular libraries like PyTorch. With flexible deployment options, secure private cloud solutions, and expert support, Intel Tiber™ ensures seamless integration, fast deployment, and enhanced model performance.
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    Baseten

    Baseten

    Baseten

    Baseten is a high-performance platform designed for mission-critical AI inference workloads. It supports serving open-source, custom, and fine-tuned AI models on infrastructure built specifically for production scale. Users can deploy models on Baseten’s cloud, their own cloud, or in a hybrid setup, ensuring flexibility and scalability. The platform offers inference-optimized infrastructure that enables fast training and seamless developer workflows. Baseten also provides specialized performance optimizations tailored for generative AI applications such as image generation, transcription, text-to-speech, and large language models. With 99.99% uptime, low latency, and support from forward deployed engineers, Baseten aims to help teams bring AI products to market quickly and reliably.
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    Modular

    Modular

    Modular

    Modular is a unified AI inference platform designed to run models efficiently across diverse hardware environments. It enables developers to deploy and scale AI workloads on GPUs, CPUs, and ASICs using a single, integrated stack. The platform optimizes performance from low-level GPU kernels to high-level API endpoints. Modular supports both managed cloud deployments and self-hosted environments, offering flexibility for different use cases. It allows users to run open-source or custom models with high performance and cost efficiency. With features like hardware portability and dynamic scaling, it reduces vendor lock-in and infrastructure complexity. By combining performance optimization and deployment simplicity, Modular helps teams build and run AI applications at scale.
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    VESSL AI

    VESSL AI

    VESSL AI

    Build, train, and deploy models faster at scale with fully managed infrastructure, tools, and workflows. Deploy custom AI & LLMs on any infrastructure in seconds and scale inference with ease. Handle your most demanding tasks with batch job scheduling, only paying with per-second billing. Optimize costs with GPU usage, spot instances, and built-in automatic failover. Train with a single command with YAML, simplifying complex infrastructure setups. Automatically scale up workers during high traffic and scale down to zero during inactivity. Deploy cutting-edge models with persistent endpoints in a serverless environment, optimizing resource usage. Monitor system and inference metrics in real-time, including worker count, GPU utilization, latency, and throughput. Efficiently conduct A/B testing by splitting traffic among multiple models for evaluation.
    Starting Price: $100 + compute/month
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    Together AI

    Together AI

    Together AI

    Together AI provides an AI-native cloud platform built to accelerate training, fine-tuning, and inference on high-performance GPU clusters. Engineered for massive scale, the platform supports workloads that process trillions of tokens without performance drops. Together AI delivers industry-leading cost efficiency by optimizing hardware, scheduling, and inference techniques, lowering total cost of ownership for demanding AI workloads. With deep research expertise, the company brings cutting-edge models, hardware, and runtime innovations—like ATLAS runtime-learning accelerators—directly into production environments. Its full-stack ecosystem includes a model library, inference APIs, fine-tuning capabilities, pre-training support, and instant GPU clusters. Designed for AI-native teams, Together AI helps organizations build and deploy advanced applications faster and more affordably.
    Starting Price: $0.0001 per 1k tokens
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    Nscale

    Nscale

    Nscale

    Nscale is the Hyperscaler engineered for AI, offering high-performance computing optimized for training, fine-tuning, and intensive workloads. From our data centers to our software stack, we are vertically integrated in Europe to provide unparalleled performance, efficiency, and sustainability. Access thousands of GPUs tailored to your requirements using our AI cloud platform. Reduce costs, grow revenue, and run your AI workloads more efficiently on a fully integrated platform. Whether you're using Nscale's built-in AI/ML tools or your own, our platform is designed to simplify the journey from development to production. The Nscale Marketplace offers users access to various AI/ML tools and resources, enabling efficient and scalable model development and deployment. Serverless allows seamless, scalable AI inference without the need to manage infrastructure. It automatically scales to meet demand, ensuring low latency and cost-effective inference for popular generative AI models.
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    Google Cloud AI Infrastructure
    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.
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    EcoHash

    EcoHash

    EcoHash Platform Technology

    EcoHash is a cloud platform for AI compute and model serving. It provides on-demand NVIDIA RTX Pro 6000 GPU workspaces and clusters, persistent cloud drives and shared filesystems, dataset validation, managed LoRA and QLoRA fine-tuning, dedicated model endpoints, and an inference API for text, vision, embeddings, reranking, image, and speech workloads. Developers can run their own containers or deploy open models and call platform or dedicated models through an OpenAI-compatible API. A browser console, SSH, JupyterLab, Compute API, and MCP tools support building, training, deploying, and managing workloads. EcoHash serves AI developers and platforms, including startups and cloud providers, and energy and mining operators developing AI compute infrastructure.
    Starting Price: $0.02/1M tokens
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    Nextbit Inference

    Nextbit Inference

    NEXTBIT 256, S.L.

    Nextbit Inference hosts open-weight AI models behind API endpoints compatible with OpenAI and Anthropic request formats. Developers can select from a model catalog and invoke models through established client libraries, with token-based pricing. Service options include shared serverless inference and dedicated single-tenant endpoints with reserved capacity. Nextbit also offers model fine-tuning and deployment for fine-tuned or customer-provided models. It serves AI and machine-learning engineers building applications, agents, and production inference workflows. Customers can choose EU or global endpoints, and dedicated deployments can be configured for data-location or infrastructure needs. The API fits existing development workflows, while serverless inference lets customers access GPU-backed model serving without operating the underlying inference infrastructure.
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    Scalattice

    Scalattice

    Robottik

    Scalattice is a managed inference API and distributed GPU network for developers running open models and GPU owners providing compute. Developers submit chat-completion and image-generation requests through OpenAI-compatible endpoints, choose catalog models, and manage API keys, credits, usage, model access, and regional policies through Scalattice Cloud or its command-line interface. Requests run on provider machines, and the service tracks usage and distributes provider payouts. GPU providers install the Scalattice agent on compatible Windows, Linux, or Apple Silicon macOS computers, set availability, and monitor jobs and earnings. The service is intended for application developers, AI builders, and GPU operators seeking hosted inference capacity or a way to contribute compute. Model availability and prices depend on the catalog and available capacity.
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    NetMind AI

    NetMind AI

    NetMind AI

    NetMind.AI is a decentralized computing platform and AI ecosystem designed to accelerate global AI innovation. By leveraging idle GPU resources worldwide, it offers accessible and affordable AI computing power to individuals, businesses, and organizations of all sizes. The platform provides a range of services, including GPU rental, serverless inference, and an AI ecosystem that encompasses data processing, model training, inference, and agent development. Users can rent GPUs at competitive prices, deploy models effortlessly with on-demand serverless inference, and access a wide array of open-source AI model APIs with high-throughput, low-latency performance. NetMind.AI also enables contributors to add their idle GPUs to the network, earning NetMind Tokens (NMT) as rewards. These tokens facilitate transactions on the platform, allowing users to pay for services such as training, fine-tuning, inference, and GPU rentals.
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    Substrate

    Substrate

    Substrate

    Substrate is the platform for agentic AI. Elegant abstractions and high-performance components, optimized models, vector database, code interpreter, and model router. Substrate is the only compute engine designed to run multi-step AI workloads. Describe your task by connecting components and let Substrate run it as fast as possible. We analyze your workload as a directed acyclic graph and optimize the graph, for example, merging nodes that can be run in a batch. The Substrate inference engine automatically schedules your workflow graph with optimized parallelism, reducing the complexity of chaining multiple inference APIs. No more async programming, just connect nodes and let Substrate parallelize your workload. Our infrastructure guarantees your entire workload runs in the same cluster, often on the same machine. You won’t spend fractions of a second per task on unnecessary data roundtrips and cross-region HTTP transport.
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    Xinity

    Xinity

    Xinity

    Xinity is open-source, OpenAI-compatible LLM inference software that lets European enterprises run generative AI entirely on their own servers. The platform installs on existing hardware and exposes an OpenAI-compatible API, so existing applications migrate by changing one base URL. No cloud dependency, no data egress, no exposure to the US CLOUD Act. The core engine is open source under Apache 2.0 and supports open-weight models, including European sovereign models, with automatic model routing, audit trails on every inference request, role-based access control, and multi-node orchestration. Xinity is built in Vienna, Austria for regulated industries such as finance, healthcare, legal, public sector, and media, including fully air-gapped environments, and is designed for GDPR and EU AI Act requirements.
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    Deep Infra

    Deep Infra

    Deep Infra

    Powerful, self-serve machine learning platform where you can turn models into scalable APIs in just a few clicks. Sign up for Deep Infra account using GitHub or log in using GitHub. Choose among hundreds of the most popular ML models. Use a simple rest API to call your model. Deploy models to production faster and cheaper with our serverless GPUs than developing the infrastructure yourself. We have different pricing models depending on the model used. Some of our language models offer per-token pricing. Most other models are billed for inference execution time. With this pricing model, you only pay for what you use. There are no long-term contracts or upfront costs, and you can easily scale up and down as your business needs change. All models run on A100 GPUs, optimized for inference performance and low latency. Our system will automatically scale the model based on your needs.
    1.0 out of 5 stars 3 Ratings for Deep Infra
    Starting Price: $0.70 per 1M input tokens
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    Impossible Cloud

    Impossible Cloud

    Impossible Cloud

    Impossible Cloud is an enterprise cloud platform that delivers high-performance object storage, dedicated bare metal GPU servers, and managed AI infrastructure for data-intensive workloads. Its S3-compatible object storage provides scalable cloud storage with enterprise security, high availability, and transparent pricing that eliminates egress fees and vendor lock-in. The platform also offers dedicated bare metal GPU servers that provide direct hardware access without virtualization, enabling maximum performance for AI training and inference workloads. Managed AI services include LLM inference, model deployment, Kubernetes, and high-performance computing to simplify AI infrastructure management. Impossible Cloud emphasizes enterprise-grade security through ISO 27001, SOC 2, GDPR compliance, encryption, and customer-controlled access to data.
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    fal

    fal

    fal.ai

    fal is a serverless Python runtime that lets you scale your code in the cloud with no infra management. Build real-time AI applications with lightning-fast inference (under ~120ms). Check out some of the ready-to-use models, they have simple API endpoints ready for you to start your own AI-powered applications. Ship custom model endpoints with fine-grained control over idle timeout, max concurrency, and autoscaling. Use common models such as Stable Diffusion, Background Removal, ControlNet, and more as APIs. These models are kept warm for free. (Don't pay for cold starts) Join the discussion around our product and help shape the future of AI. Automatically scale up to hundreds of GPUs and scale down back to 0 GPUs when idle. Pay by the second only when your code is running. You can start using fal on any Python project by just importing fal and wrapping existing functions with the decorator.
    Starting Price: $0.00111 per second
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    NVIDIA Picasso
    NVIDIA Picasso is a cloud service for building generative AI–powered visual applications. Enterprises, software creators, and service providers can run inference on their models, train NVIDIA Edify foundation models on proprietary data, or start from pre-trained models to generate image, video, and 3D content from text prompts. Picasso service is fully optimized for GPUs and streamlines training, optimization, and inference on NVIDIA DGX Cloud. Organizations and developers can train NVIDIA’s Edify models on their proprietary data or get started with models pre-trained with our premier partners. Expert denoising network to generate photorealistic 4K images. Temporal layers and novel video denoiser generate high-fidelity videos with temporal consistency. A novel optimization framework for generating 3D objects and meshes with high-quality geometry. Cloud service for building and deploying generative AI-powered image, video, and 3D applications.
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    Wallaroo.AI

    Wallaroo.AI

    Wallaroo.AI

    Wallaroo facilitates the last-mile of your machine learning journey, getting ML into your production environment to impact the bottom line, with incredible speed and efficiency. Wallaroo is purpose-built from the ground up to be the easy way to deploy and manage ML in production, unlike Apache Spark, or heavy-weight containers. ML with up to 80% lower cost and easily scale to more data, more models, more complex models. Wallaroo is designed to enable data scientists to quickly and easily deploy their ML models against live data, whether to testing environments, staging, or prod. Wallaroo supports the largest set of machine learning training frameworks possible. You’re free to focus on developing and iterating on your models while letting the platform take care of deployment and inference at speed and scale.
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    CentML

    CentML

    CentML

    CentML accelerates Machine Learning workloads by optimizing models to utilize hardware accelerators, like GPUs or TPUs, more efficiently and without affecting model accuracy. Our technology boosts training and inference speed, lowers compute costs, increases your AI-powered product margins, and boosts your engineering team's productivity. Software is no better than the team who built it. Our team is stacked with world-class machine learning and system researchers and engineers. Focus on your AI products and let our technology take care of optimum performance and lower cost for you.
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    NVIDIA Run:ai
    NVIDIA Run:ai is an enterprise platform designed to optimize AI workloads and orchestrate GPU resources efficiently. It dynamically allocates and manages GPU compute across hybrid, multi-cloud, and on-premises environments, maximizing utilization and scaling AI training and inference. The platform offers centralized AI infrastructure management, enabling seamless resource pooling and workload distribution. Built with an API-first approach, Run:ai integrates with major AI frameworks and machine learning tools to support flexible deployment anywhere. It also features a powerful policy engine for strategic resource governance, reducing manual intervention. With proven results like 10x GPU availability and 5x utilization, NVIDIA Run:ai accelerates AI development cycles and boosts ROI.
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    NVIDIA NIM
    Explore the latest optimized AI models, connect AI agents to data with NVIDIA NeMo, and deploy anywhere with NVIDIA NIM microservices. NVIDIA NIM is a set of easy-to-use inference microservices that facilitate the deployment of foundation models across any cloud or data center, ensuring data security and streamlined AI integration. Additionally, NVIDIA AI provides access to the Deep Learning Institute (DLI), offering technical training to gain in-demand skills, hands-on experience, and expert knowledge in AI, data science, and accelerated computing. AI models generate responses and outputs based on complex algorithms and machine learning techniques, and those responses or outputs may be inaccurate, harmful, biased, or indecent. By testing this model, you assume the risk of any harm caused by any response or output of the model. Please do not upload any confidential information or personal data unless expressly permitted. Your use is logged for security purposes.
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    Geodd

    Geodd

    Geodd

    Geodd is an experienced AI inference operator providing serverless inference, dedicated inference and dedicated GPU infrastructure for production AI workloads. Through DeployPad, customers can deploy supported models, manage inference services and access Geodd’s infrastructure through a single platform. Geodd’s physical datacenter infrastructure and technical capabilities span GPU operations, model deployment, runtime engineering and AI driven optimization. Its autonomous AI agents use hardware specific LLMs trained by the company to develop verified optimizations that improve model efficiency, consistency and reliability from the underlying infrastructure through to model execution.
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    KServe

    KServe

    KServe

    Highly scalable and standards-based model inference platform on Kubernetes for trusted AI. KServe is a standard model inference platform on Kubernetes, built for highly scalable use cases. Provides performant, standardized inference protocol across ML frameworks. Support modern serverless inference workload with autoscaling including a scale to zero on GPU. Provides high scalability, density packing, and intelligent routing using ModelMesh. Simple and pluggable production serving for production ML serving including prediction, pre/post-processing, monitoring, and explainability. Advanced deployments with the canary rollout, experiments, ensembles, and transformers. ModelMesh is designed for high-scale, high-density, and frequently-changing model use cases. ModelMesh intelligently loads and unloads AI models to and from memory to strike an intelligent trade-off between responsiveness to users and computational footprint.
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    Radiant

    Radiant

    Radiant

    Radiant is a fully integrated AI infrastructure platform designed to deliver end-to-end capabilities for building and scaling AI systems. It combines compute, software, energy, and capital into a unified ecosystem, enabling organizations to move from concept to deployment efficiently. Radiant’s AI Cloud includes NVIDIA-accelerated computing along with MLOps tools such as inference, fine-tuning, model registry, and serverless Kubernetes. Its proprietary software platform supports intelligent scheduling, automated node management, and secure multi-tenancy for large-scale operations. With infrastructure designed to scale from thousands to over 100,000 GPUs, Radiant ensures consistent performance and operational control. The platform also integrates energy solutions through its powered-land portfolio, optimizing costs and sustainability. Backed by significant capital resources, Radiant can support large-scale AI initiatives globally.
    Starting Price: $3.24 per month
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    Amazon EC2 Inf1 Instances
    Amazon EC2 Inf1 instances are purpose-built to deliver high-performance and cost-effective machine learning inference. They provide up to 2.3 times higher throughput and up to 70% lower cost per inference compared to other Amazon EC2 instances. Powered by up to 16 AWS Inferentia chips, ML inference accelerators designed by AWS, Inf1 instances also feature 2nd generation Intel Xeon Scalable processors and offer up to 100 Gbps networking bandwidth to support large-scale ML applications. These instances are ideal for deploying applications such as search engines, recommendation systems, computer vision, speech recognition, natural language processing, personalization, and fraud detection. Developers can deploy their ML models on Inf1 instances using the AWS Neuron SDK, which integrates with popular ML frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing for seamless migration with minimal code changes.
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    Tensormesh

    Tensormesh

    Tensormesh

    Tensormesh is a caching layer built specifically for large-language-model inference workloads that enables organizations to reuse intermediate computations, drastically reduce GPU usage, and accelerate time-to-first-token and latency. It works by capturing and reusing key-value cache states that are normally thrown away after each inference, thereby cutting redundant compute and delivering “up to 10x faster inference” while substantially lowering GPU load. It supports deployments in public cloud or on-premises, with full observability and enterprise-grade control, SDKs/APIs, and dashboards for integration into existing inference pipelines, and compatibility with inference engines such as vLLM out of the box. Tensormesh emphasizes performance at scale, including sub-millisecond repeated queries, while optimizing every layer of inference from caching through computation.
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    Confidential AI

    Confidential AI

    Confidential AI

    Confidential AI provides private inference, private agents, confidential VMs and confidential computing infrastructure for AI labs, inference providers, routers, and organizations handling sensitive workloads. Its hosted inference service runs open-weight models in hardware-backed trusted execution environments (TEEs), including Intel TDX and AMD SEV-SNP confidential VMs with NVIDIA GPUs in confidential computing mode. Prompts, responses, and workload data are protected from infrastructure operators, and clients can verify attestation before sending sensitive input. Customers can use an OpenAI-compatible API to deploy inference endpoints and agent workloads, or license the confidential-computing stack for on-premises infrastructure, bare metal, and major clouds. Use cases include serving models while limiting operator access to prompts and model assets, and running sensitive AI workloads on dedicated confidential compute.
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    AWS Neuron

    AWS Neuron

    Amazon Web Services

    It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP).
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    SiliconFlow

    SiliconFlow

    SiliconFlow

    SiliconFlow is a high-performance, developer-focused AI infrastructure platform offering a unified and scalable solution for running, fine-tuning, and deploying both language and multimodal models. It provides fast, reliable inference across open source and commercial models, thanks to blazing speed, low latency, and high throughput, with flexible options such as serverless endpoints, dedicated compute, or private cloud deployments. Platform capabilities include one-stop inference, fine-tuning pipelines, and reserved GPU access, all delivered via an OpenAI-compatible API and complete with built-in observability, monitoring, and cost-efficient smart scaling. For diffusion-based tasks, SiliconFlow offers the open source OneDiff acceleration library, while its BizyAir runtime supports scalable multimodal workloads. Designed for enterprise-grade stability, it includes features like BYOC (Bring Your Own Cloud), robust security, and real-time metrics.
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    Climb

    Climb

    Climb

    Select a model, and we'll handle the deployment, hosting, versioning and tuning then give you an inference endpoint.
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    Canopy Wave

    Canopy Wave

    Canopy Wave

    Canopy Wave is the best inference platform for open models, built to deliver high-quality, reliable, and secure AI services from infrastructure to build, tune, and scale AI models. Its model platform gives users instant access to advanced open source models optimized for quality, speed, and security through API, with a model library covering different types and fields, so users can call models directly without additional development or adaptation. Canopy Wave’s serverless inference service lets teams run pretrained models through simple API calls without managing infrastructure, with fast response, low latency, no cold start issues, and globally optimized performance powered by next-generation GPUs and edge caching. For production workloads that need stronger control, dedicated endpoints run inference at scale with exceptional speed and reliability on hardware instances dedicated exclusively to the user.
    Starting Price: $0.07 per GB per month
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    FriendliAI

    FriendliAI

    FriendliAI

    FriendliAI is a generative AI infrastructure platform that offers fast, efficient, and reliable inference solutions for production environments. It provides a suite of tools and services designed to optimize the deployment and serving of large language models (LLMs) and other generative AI workloads at scale. Key offerings include Friendli Endpoints, which allow users to build and serve custom generative AI models, saving GPU costs and accelerating AI inference. It supports seamless integration with popular open source models from the Hugging Face Hub, enabling lightning-fast, high-performance inference. FriendliAI's cutting-edge technologies, such as Iteration Batching, Friendli DNN Library, Friendli TCache, and Native Quantization, contribute to significant cost savings (50–90%), reduced GPU requirements (6× fewer GPUs), higher throughput (10.7×), and lower latency (6.2×).
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    Neysa Nebula
    Nebula allows you to deploy and scale your AI projects quickly, easily and cost-efficiently2 on highly robust, on-demand GPU infrastructure. Train and infer your models securely and easily on the Nebula cloud powered by the latest on-demand Nvidia GPUs and create and manage your containerized workloads through Nebula’s user-friendly orchestration layer. Access Nebula’s MLOps and low-code/no-code engines to build and deploy AI use cases for business teams and to deploy AI-powered applications swiftly and seamlessly with little to no coding. Choose between the Nebula containerized AI cloud, your on-prem environment, or any cloud of your choice. Build and scale AI-enabled business use-cases within a matter of weeks, not months, with the Nebula Unify platform.
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    kluster.ai

    kluster.ai

    kluster.ai

    Kluster.ai is a developer-centric AI cloud platform designed to deploy, scale, and fine-tune large language models (LLMs) with speed and efficiency. Built for developers by developers, it offers Adaptive Inference, a flexible and scalable service that adjusts seamlessly to workload demands, ensuring high-performance processing and consistent turnaround times. Adaptive Inference provides three distinct processing options: real-time inference for ultra-low latency needs, asynchronous inference for cost-effective handling of flexible timing tasks, and batch inference for efficient processing of high-volume, bulk tasks. It supports a range of open-weight, cutting-edge multimodal models for chat, vision, code, and more, including Meta's Llama 4 Maverick and Scout, Qwen3-235B-A22B, DeepSeek-R1, and Gemma 3 . Kluster.ai's OpenAI-compatible API allows developers to integrate these models into their applications seamlessly.
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    Nebius Token Factory
    Nebius Token Factory is a scalable AI inference platform designed to run open-source and custom AI models in production without manual infrastructure management. It offers enterprise-ready inference endpoints with predictable performance, autoscaling throughput, and sub-second latency — even at very high request volumes. It delivers 99.9% uptime availability and supports unlimited or tailored traffic profiles based on workload needs, simplifying the transition from experimentation to global deployment. Nebius Token Factory supports a broad set of open source models such as Llama, Qwen, DeepSeek, GPT-OSS, Flux, and many others, and lets teams host and fine-tune models through an API or dashboard. Users can upload LoRA adapters or full fine-tuned variants directly, with the same enterprise performance guarantees applied to custom models.
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    Wafer

    Wafer

    Wafer

    Wafer delivers the fastest open source LLMs for enterprise through serverless and dedicated inference built for production AI workloads. Its serverless inference gives teams access to top open models with no infrastructure, no deployment overhead, and fast APIs, including GLM-5.2-Fast for low-latency inference with EAGLE speculative decoding and a per-stream throughput SLA, GLM-5.2 as a flagship model with stronger coding and reasoning capabilities, and more. Wafer’s technology uses agents that optimize inference across the stack, identifying and enhancing bottlenecks in orchestration, algorithms, serving engines, GPU kernels, and diverse hardware. It profiles the stack to see whether latency or throughput comes from scheduling, decoding, kernels, memory pressure, or hardware fit, then tries many paths and ships the measured winner. Instead of relying on a single switch or heuristic, Wafer searches model, engine, kernel, and hardware combinations.
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    AWS EC2 Trn3 Instances
    Amazon EC2 Trn3 UltraServers are AWS’s newest accelerated computing instances, powered by the in-house Trainium3 AI chips and engineered specifically for high-performance deep-learning training and inference workloads. These UltraServers are offered in two configurations, a “Gen1” with 64 Trainium3 chips and a “Gen2” with up to 144 Trainium3 chips per UltraServer. The Gen2 configuration delivers up to 362 petaFLOPS of dense MXFP8 compute, 20 TB of HBM memory, and a staggering 706 TB/s of aggregate memory bandwidth, making it one of the highest-throughput AI compute platforms available. Interconnects between chips are handled by a new “NeuronSwitch-v1” fabric to support all-to-all communication patterns, which are especially important for large models, mixture-of-experts architectures, or large-scale distributed training.
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    Qubrid AI

    Qubrid AI

    Qubrid AI

    Qubrid AI is an advanced Artificial Intelligence (AI) company with a mission to solve real world complex problems in multiple industries. Qubrid AI’s software suite comprises of AI Hub, a one-stop shop for everything AI models, AI Compute GPU Cloud and On-Prem Appliances and AI Data Connector! Train our inference industry-leading models or your own custom creations, all within a streamlined, user-friendly interface. Test and refine your models with ease, then seamlessly deploy them to unlock the power of AI in your projects. AI Hub empowers you to embark on your AI Journey, from concept to implementation, all in a single, powerful platform. Our leading cutting-edge AI Compute platform harnesses the power of GPU Cloud and On-Prem Server Appliances to efficiently develop and run next generation AI applications. Qubrid team is comprised of AI developers, researchers and partner teams all focused on enhancing this unique platform for the advancement of scientific applications.
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    DeepInfra

    DeepInfra

    DeepInfra

    DeepInfra is an AI inference cloud that makes it simple to run the latest machine learning models at scale, including LLMs, vision models, embeddings, image generation, video generation, speech, and more. It provides serverless inference through simple APIs, allowing developers to integrate production-ready AI models without managing GPU infrastructure, autoscaling, deployment complexity, or model hosting operations. DeepInfra supports OpenAI-compatible APIs for LLMs and embeddings, making it easier to switch from existing OpenAI-style integrations while accessing a broad catalog of open and commercial models. Its Native API gives access to every model type available on the platform, including image generation, speech recognition, object detection, token classification, fill-mask, image classification, zero-shot image classification, and text classification. DeepInfra is optimized for scalable, low-latency inference and runs models on high-performance GPU infrastructure.
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    Fireworks AI

    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.
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    Oblivus

    Oblivus

    Oblivus

    Our infrastructure is equipped to meet your computing requirements, be it one or thousands of GPUs, or one vCPU to tens of thousands of vCPUs, we've got you covered. Our resources are readily available to cater to your needs, whenever you need them. Switching between GPU and CPU instances is a breeze with our platform. You have the flexibility to deploy, modify, and rescale your instances according to your needs, without any hassle. Outstanding machine learning performance without breaking the bank. The latest technology at a significantly lower cost. Cutting-edge GPUs are designed to meet the demands of your workloads. Gain access to computational resources that are tailored to suit the intricacies of your models. Leverage our infrastructure to perform large-scale inference and access necessary libraries with our OblivusAI OS. Unleash the full potential of your gaming experience by utilizing our robust infrastructure to play games in the settings of your choice.