Alternatives to KServe
Compare KServe alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to KServe in 2026. Compare features, ratings, user reviews, pricing, and more from KServe competitors and alternatives in order to make an informed decision for your business.
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Vertex AI
Google
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex. -
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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. -
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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
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Nebius Token Factory
Nebius
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.Starting Price: $0.02 -
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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.Starting Price: Free -
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Intel Open Edge Platform
Intel
The Intel Open Edge Platform simplifies the development, deployment, and scaling of AI and edge computing solutions on standard hardware with cloud-like efficiency. It provides a curated set of components and workflows that accelerate AI model creation, optimization, and application development. From vision models to generative AI and large language models (LLM), the platform offers tools to streamline model training and inference. By integrating Intel’s OpenVINO toolkit, it ensures enhanced performance on Intel CPUs, GPUs, and VPUs, allowing organizations to bring AI applications to the edge with ease. -
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Intel Tiber AI Cloud
Intel
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.Starting Price: Free -
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Seldon
Seldon Technologies
Deploy machine learning models at scale with more accuracy. Turn R&D into ROI with more models into production at scale, faster, with increased accuracy. Seldon reduces time-to-value so models can get to work faster. Scale with confidence and minimize risk through interpretable results and transparent model performance. Seldon Deploy reduces the time to production by providing production grade inference servers optimized for popular ML framework or custom language wrappers to fit your use cases. Seldon Core Enterprise provides access to cutting-edge, globally tested and trusted open source MLOps software with the reassurance of enterprise-level support. Seldon Core Enterprise is for organizations requiring: - Coverage across any number of ML models deployed plus unlimited users - Additional assurances for models in staging and production - Confidence that their ML model deployments are supported and protected. -
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NVIDIA Picasso
NVIDIA
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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Alibaba Cloud Model Studio
Alibaba
Model Studio is Alibaba Cloud’s one-stop generative AI platform that lets developers build intelligent, business-aware applications using industry-leading foundation models like Qwen-Max, Qwen-Plus, Qwen-Turbo, the Qwen-2/3 series, visual-language models (Qwen-VL/Omni), and the video-focused Wan series. Users can access these powerful GenAI models through familiar OpenAI-compatible APIs or purpose-built SDKs, no infrastructure setup required. It supports a full development workflow, experiment with models in the playground, perform real-time and batch inferences, fine-tune with tools like SFT or LoRA, then evaluate, compress, accelerate deployment, and monitor performance, all within an isolated Virtual Private Cloud (VPC) for enterprise-grade security. Customization is simplified via one-click Retrieval-Augmented Generation (RAG), enabling integration of business data into model outputs. Visual, template-driven interfaces facilitate prompt engineering and application design. -
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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.
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ONNX
ONNX
ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers. Develop in your preferred framework without worrying about downstream inferencing implications. ONNX enables you to use your preferred framework with your chosen inference engine. ONNX makes it easier to access hardware optimizations. Use ONNX-compatible runtimes and libraries designed to maximize performance across hardware. Our active community thrives under our open governance structure, which provides transparency and inclusion. We encourage you to engage and contribute. -
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Synexa
Synexa
Synexa AI enables users to deploy AI models with a single line of code, offering a simple, fast, and stable solution. It supports various functionalities, including image and video generation, image restoration, image captioning, model fine-tuning, and speech generation. Synexa provides access to over 100 production-ready AI models, such as FLUX Pro, Ideogram v2, and Hunyuan Video, with new models added weekly and zero setup required. Synexa's optimized inference engine delivers up to 4x faster performance on diffusion models, achieving sub-second generation times with FLUX and other popular models. Developers can integrate AI capabilities in minutes using intuitive SDKs and comprehensive API documentation, with support for Python, JavaScript, and REST API. Synexa offers enterprise-grade GPU infrastructure with A100s and H100s across three continents, ensuring sub-100ms latency with smart routing and a 99.9% uptime guarantee.Starting Price: $0.0125 per image -
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NVIDIA DGX Cloud Serverless Inference is a high-performance, serverless AI inference solution that accelerates AI innovation with auto-scaling, cost-efficient GPU utilization, multi-cloud flexibility, and seamless scalability. With NVIDIA DGX Cloud Serverless Inference, you can scale down to zero instances during periods of inactivity to optimize resource utilization and reduce costs. There's no extra cost for cold-boot start times, and the system is optimized to minimize them. NVIDIA DGX Cloud Serverless Inference is powered by NVIDIA Cloud Functions (NVCF), which offers robust observability features. It allows you to integrate your preferred monitoring tools, such as Splunk, for comprehensive insights into your AI workloads. NVCF offers flexible deployment options for NIM microservices while allowing you to bring your own containers, models, and Helm charts.
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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.Starting Price: $0.15per input -
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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×).Starting Price: $5.9 per hour -
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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.Starting Price: $2.50 per hour -
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Qualcomm Cloud AI SDK
Qualcomm
The Qualcomm Cloud AI SDK is a comprehensive software suite designed to optimize trained deep learning models for high-performance inference on Qualcomm Cloud AI 100 accelerators. It supports a wide range of AI frameworks, including TensorFlow, PyTorch, and ONNX, enabling developers to compile, optimize, and execute models efficiently. The SDK provides tools for model onboarding, tuning, and deployment, facilitating end-to-end workflows from model preparation to production deployment. Additionally, it offers resources such as model recipes, tutorials, and code samples to assist developers in accelerating AI development. It ensures seamless integration with existing systems, allowing for scalable and efficient AI inference in cloud environments. By leveraging the Cloud AI SDK, developers can achieve enhanced performance and efficiency in their AI applications. -
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FPT AI Factory
FPT Cloud
FPT AI Factory is a comprehensive, enterprise-grade AI development platform built on NVIDIA H100 and H200 superchips, offering a full-stack solution that spans the entire AI lifecycle, FPT AI Infrastructure delivers high-performance, scalable GPU resources for rapid model training; FPT AI Studio provides data hubs, AI notebooks, model pre‑training, fine‑tuning pipelines, and model hub for streamlined experimentation and development; FPT AI Inference offers production-ready model serving and “Model-as‑a‑Service” for real‑world applications with low latency and high throughput; and FPT AI Agents, a GenAI agent builder, enables the creation of adaptive, multilingual, multitasking conversational agents. Integrated with ready-to-deploy generative AI solutions and enterprise tools, FPT AI Factory empowers businesses to innovate quickly, deploy reliably, and scale AI workloads from proof-of-concept to operational systems.Starting Price: $2.31 per hour -
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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.Starting Price: $0.70 per 1M input tokens -
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NVIDIA TensorRT
NVIDIA
NVIDIA TensorRT is an ecosystem of APIs for high-performance deep learning inference, encompassing an inference runtime and model optimizations that deliver low latency and high throughput for production applications. Built on the CUDA parallel programming model, TensorRT optimizes neural network models trained on all major frameworks, calibrating them for lower precision with high accuracy, and deploying them across hyperscale data centers, workstations, laptops, and edge devices. It employs techniques such as quantization, layer and tensor fusion, and kernel tuning on all types of NVIDIA GPUs, from edge devices to PCs to data centers. The ecosystem includes TensorRT-LLM, an open source library that accelerates and optimizes inference performance of recent large language models on the NVIDIA AI platform, enabling developers to experiment with new LLMs for high performance and quick customization through a simplified Python API.Starting Price: Free -
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ModelArk
ByteDance
ModelArk is ByteDance’s one-stop large model service platform, providing access to cutting-edge AI models for video, image, and text generation. With powerful options like Seedance 1.0 for video, Seedream 3.0 for image creation, and DeepSeek-V3.1 for reasoning, it enables businesses and developers to build scalable, AI-driven applications. Each model is backed by enterprise-grade security, including end-to-end encryption, data isolation, and auditability, ensuring privacy and compliance. The platform’s token-based pricing keeps costs transparent, starting with 500,000 free inference tokens per LLM and 2 million tokens per vision model. Developers can quickly integrate APIs for inference, fine-tuning, evaluation, and plugins to extend model capabilities. Designed for scalability, ModelArk offers fast deployment, high GPU availability, and seamless enterprise integration. -
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Tinfoil
Tinfoil
Tinfoil is a verifiably private AI platform built to deliver zero-trust, zero-data-retention inference by running open-source or custom models inside secure hardware enclaves in the cloud, giving you the data-privacy assurances of on-premises systems with the scalability and convenience of the cloud. All user inputs and inference operations are processed in confidential-computing environments so that no one, not even Tinfoil or the cloud provider, can access or retain your data. It supports private chat, private data analysis, user-trained fine-tuning, and an OpenAI-compatible inference API, covers workloads such as AI agents, private content moderation, and proprietary code models, and provides features like public verification of enclave attestation, “provable zero data access,” and full compatibility with major open source models. -
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Core Scientific
Core Scientific
Core Scientific delivers purpose-built high-density colocation infrastructure and intelligent software solutions designed for demanding compute workloads such as AI, machine learning, high-performance computing, and digital asset mining. It features ready-to-scale high-density compute environments with contracted power capacity of over 1.3 GW, faster deployment timelines, and optimized cooling and power systems tailored for intensive workloads. Core Scientific’s digital mining offering incorporates proprietary software for fleet management capable of handling up to one million miners, real-time thermal monitoring, and hash-price economics analysis to optimize profitability. In its colocation and AI-focused infrastructure business, Core Scientific combines high-density racks (50–200 kW+ per rack) and enterprise-grade infrastructure to support AI model training/inference, cloud workloads, financial services analytics, government mission-critical systems, and healthcare research. -
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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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NLP Cloud
NLP Cloud
Fast and accurate AI models suited for production. Highly-available inference API leveraging the most advanced NVIDIA GPUs. We selected the best open-source natural language processing (NLP) models from the community and deployed them for you. Fine-tune your own models - including GPT-J - or upload your in-house custom models, and deploy them easily to production. Upload or Train/Fine-Tune your own AI models - including GPT-J - from your dashboard, and use them straight away in production without worrying about deployment considerations like RAM usage, high-availability, scalability... You can upload and deploy as many models as you want to production.Starting Price: $29 per month -
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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 -
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Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. Features are used repeatedly by multiple teams and feature quality is critical to ensure a highly accurate model. Also, when features used to train models offline in batch are made available for real-time inference, it’s hard to keep the two feature stores synchronized. SageMaker Feature Store provides a secured and unified store for feature use across the ML lifecycle. Store, share, and manage ML model features for training and inference to promote feature reuse across ML applications. Ingest features from any data source including streaming and batch such as application logs, service logs, clickstreams, sensors, etc.
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kgateway
Cloud Native Computing Foundation
kgateway is a Kubernetes-native gateway platform designed to manage microservices and AI agent traffic at scale. It acts as a unified control plane for API gateways, AI gateways, inference routing, and agent-to-agent communication. Built on Envoy and open standards, kgateway implements the Kubernetes Gateway API for modern cloud-native environments. The platform enables centralized authentication, authorization, rate limiting, and traffic management. Kgateway also secures LLM consumption by controlling access to models, tools, and agents. It supports intelligent routing for AI inference workloads running in Kubernetes. Trusted by enterprises worldwide, kgateway delivers scalable, secure, and flexible connectivity across any cloud. -
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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.Starting Price: $0.04 per image -
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NetApp AIPod
NetApp
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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vLLM
vLLM
vLLM is a high-performance library designed to facilitate efficient inference and serving of Large Language Models (LLMs). Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. It offers state-of-the-art serving throughput by efficiently managing attention key and value memory through its PagedAttention mechanism. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, including integration with FlashAttention and FlashInfer, to enhance model execution speed. Additionally, vLLM provides quantization support for GPTQ, AWQ, INT4, INT8, and FP8, as well as speculative decoding capabilities. Users benefit from seamless integration with popular Hugging Face models, support for various decoding algorithms such as parallel sampling and beam search, and compatibility with NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs, and more. -
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SquareFactory
SquareFactory
End-to-end project, model and hosting management platform, which allows companies to convert data and algorithms into holistic, execution-ready AI-strategies. Build, train and manage models securely with ease. Create products that consume AI models from anywhere, any time. Minimize risks of AI investments, while increasing strategic flexibility. Completely automated model testing, evaluation deployment, scaling and hardware load balancing. From real-time, low-latency, high-throughput inference to batch, long-running inference. Pay-per-second-of-use model, with an SLA, and full governance, monitoring and auditing tools. Intuitive interface that acts as a unified hub for managing projects, creating and visualizing datasets, and training models via collaborative and reproducible workflows. -
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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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Groq
Groq
GroqCloud is a high-performance AI inference platform built specifically for developers who need speed, scale, and predictable costs. It delivers ultra-fast responses for leading generative AI models across text, audio, and vision workloads. Powered by Groq’s purpose-built LPU (Language Processing Unit), the platform is designed for inference from the ground up, not adapted from training hardware. GroqCloud supports popular LLMs, speech-to-text, text-to-speech, and image-to-text models through industry-standard APIs. Developers can start for free and scale seamlessly as usage grows, with clear usage-based pricing. The platform is available in public, private, or co-cloud deployments to match different security and performance needs. GroqCloud combines consistent low latency with enterprise-grade reliability. -
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Mirai
Mirai
Mirai is a developer-focused on-device AI infrastructure platform designed to convert, optimize, and run machine learning models directly on Apple devices with high performance and privacy. It provides a unified pipeline that enables teams to convert and quantize models, benchmark them, distribute them, and execute inference locally. It is built specifically for Apple Silicon and aims to deliver near-zero latency, zero inference cost, and full data privacy by keeping sensitive processing on the user’s device. Through its SDK and inference engine, developers can integrate AI features into applications quickly, using hardware-aware optimizations that unlock the full power of the GPU and Neural Engine. Mirai also includes dynamic routing capabilities that automatically decide whether a request should run locally or in the cloud based on latency, privacy, or workload requirements. -
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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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Valohai
Valohai
Models are temporary, pipelines are forever. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform that automates everything from data extraction to model deployment. Automate everything from data extraction to model deployment. Store every single model, experiment and artifact automatically. Deploy and monitor models in a managed Kubernetes cluster. Point to your code & data and hit run. Valohai launches workers, runs your experiments and shuts down the instances for you. Develop through notebooks, scripts or shared git projects in any language or framework. Expand endlessly through our open API. Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable.Starting Price: $560 per month -
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Amazon EC2 Inf1 Instances
Amazon
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.Starting Price: $0.228 per hour -
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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. -
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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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OpenVINO
Intel
The Intel® Distribution of OpenVINO™ toolkit is an open-source AI development toolkit that accelerates inference across Intel hardware platforms. Designed to streamline AI workflows, it allows developers to deploy optimized deep learning models for computer vision, generative AI, and large language models (LLMs). With built-in tools for model optimization, the platform ensures high throughput and lower latency, reducing model footprint without compromising accuracy. OpenVINO™ is perfect for developers looking to deploy AI across a range of environments, from edge devices to cloud servers, ensuring scalability and performance across Intel architectures.Starting Price: Free -
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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. -
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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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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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ByteDance Seed
ByteDance
Seed Diffusion Preview is a large-scale, code-focused language model that uses discrete-state diffusion to generate code non-sequentially, achieving dramatically faster inference without sacrificing quality by decoupling generation from the token-by-token bottleneck of autoregressive models. It combines a two-stage curriculum, mask-based corruption followed by edit-based augmentation, to robustly train a standard dense Transformer, striking a balance between speed and accuracy and avoiding shortcuts like carry-over unmasking to preserve principled density estimation. The model delivers an inference speed of 2,146 tokens/sec on H20 GPUs, outperforming contemporary diffusion baselines while matching or exceeding their accuracy on standard code benchmarks, including editing tasks, thereby establishing a new speed-quality Pareto frontier and demonstrating discrete diffusion’s practical viability for real-world code generation.Starting Price: Free -
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MaiaOS
Zyphra Technologies
Zyphra is an artificial intelligence company based in Palo Alto with a growing presence in Montreal and London. We’re building MaiaOS, a multimodal agent system combining advanced research in next-gen neural network architectures (SSM hybrids), long-term memory & reinforcement learning. We believe the future of AGI will involve a combination of cloud and on-device deployment strategies with an increasing shift toward local inference. MaiaOS is built around a deployment framework that maximizes inference efficiency for real-time intelligence. Our AI & product teams come from leading organizations and institutions including Google DeepMind, Anthropic, StabilityAI, Qualcomm, Neuralink, Nvidia, and Apple. We have deep expertise across AI models, learning algorithms, and systems/infrastructure with a focus on inference efficiency and AI silicon performance. Zyphra's team is committed to democratizing advanced AI systems. -
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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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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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Stanhope AI
Stanhope AI
Active Inference is a novel framework for agentic AI based on world models, emerging from over 30 years of research in computational neuroscience. From this paradigm, we offer an AI built for power and computational efficiency, designed to live on-device and on the edge. Integrating with traditional computer vision stacks our intelligent decision-making systems provide an explainable output that allows organizations to build accountability into their AI tools and products. We are taking active inference from neuroscience into AI as the foundation for software that will allow robots and embodied platforms to make autonomous decisions like the human brain.
