Alternatives to Mirai
Compare Mirai alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Mirai in 2026. Compare features, ratings, user reviews, pricing, and more from Mirai competitors and alternatives in order to make an informed decision for your business.
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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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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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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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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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EdgeCortix
EdgeCortix
Breaking the limits in AI processors and edge AI inference acceleration. Where AI inference acceleration needs it all, more TOPS, lower latency, better area and power efficiency, and scalability, EdgeCortix AI processor cores make it happen. General-purpose processing cores, CPUs, and GPUs, provide developers with flexibility for most applications. However, these general-purpose cores don’t match up well with workloads found in deep neural networks. EdgeCortix began with a mission in mind: redefining edge AI processing from the ground up. With EdgeCortix technology including a full-stack AI inference software development environment, run-time reconfigurable edge AI inference IP, and edge AI chips for boards and systems, designers can deploy near-cloud-level AI performance at the edge. Think about what that can do for these and other applications. Finding threats, raising situational awareness, and making vehicles smarter. -
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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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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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DeePhi Quantization Tool
DeePhi Quantization Tool
This is a model quantization tool for convolution neural networks(CNN). This tool could quantize both weights/biases and activations from 32-bit floating-point (FP32) format to 8-bit integer(INT8) format or any other bit depths. With this tool, you can boost the inference performance and efficiency significantly, while maintaining the accuracy. This tool supports common layer types in neural networks, including convolution, pooling, fully-connected, batch normalization and so on. The quantization tool does not need the retraining of the network or labeled datasets, only one batch of pictures are needed. The process time ranges from a few seconds to several minutes depending on the size of neural network, which makes rapid model update possible. This tool is collaborative optimized for DeePhi DPU and could generate INT8 format model files required by DNNC.Starting Price: $0.90 per hour -
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LEAP
Liquid AI
The LEAP Edge AI Platform offers a full-stack on-device AI toolchain that enables developers to build edge AI applications, from model selection through inference, entirely on device. It includes a best-model search engine to find the most appropriate model for a given task and device constraint, a curated library of pre-trained model bundles ready for download, and fine-tuning tools (such as GPU-optimized scripts) for customizing models like LFM2 to specific use cases. It supports vision-enabled capabilities across iOS, Android, and laptop devices, and includes function-calling so AI models can interact with external systems via structured outputs. For deployment, LEAP provides an Edge SDK that lets developers load and query models locally, just like a cloud API, but entirely offline, and a model bundling service to package any supported model or checkpoint into a bundle optimized for edge deployment.Starting Price: Free -
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LiteRT
Google
LiteRT (Lite Runtime), formerly known as TensorFlow Lite, is Google's high-performance runtime for on-device AI. It enables developers to deploy machine learning models across various platforms and microcontrollers. LiteRT supports models from TensorFlow, PyTorch, and JAX, converting them into the efficient FlatBuffers format (.tflite) for optimized on-device inference. Key features include low latency, enhanced privacy by processing data locally, reduced model and binary sizes, and efficient power consumption. The runtime offers SDKs in multiple languages such as Java/Kotlin, Swift, Objective-C, C++, and Python, facilitating integration into diverse applications. Hardware acceleration is achieved through delegates like GPU and iOS Core ML, improving performance on supported devices. LiteRT Next, currently in alpha, introduces a new set of APIs that streamline on-device hardware acceleration.Starting Price: Free -
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NVIDIA Modulus
NVIDIA
NVIDIA Modulus is a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. Whether you’re looking to get started with AI-driven physics problems or designing digital twin models for complex non-linear, multi-physics systems, NVIDIA Modulus can support your work. Offers building blocks for developing physics machine learning surrogate models that combine both physics and data. The framework is generalizable to different domains and use cases—from engineering simulations to life sciences and from forward simulations to inverse/data assimilation problems. Provides parameterized system representation that solves for multiple scenarios in near real time, letting you train once offline to infer in real time repeatedly. -
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Xilinx
Xilinx
The Xilinx’s AI development platform for AI inference on Xilinx hardware platforms consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP. Supports mainstream frameworks and the latest models capable of diverse deep learning tasks. Provides a comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices. You can find the closest model and start re-training for your applications! Provides a powerful open source quantizer that supports pruned and unpruned model quantization, calibration, and fine tuning. The AI profiler provides layer by layer analysis to help with bottlenecks. The AI library offers open source high-level C++ and Python APIs for maximum portability from edge to cloud. Efficient and scalable IP cores can be customized to meet your needs of many different applications. -
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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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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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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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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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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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Latent AI
Latent AI
We take the hard work out of AI processing on the edge. The Latent AI Efficient Inference Platform (LEIP) enables adaptive AI at the edge by optimizing for compute, energy and memory without requiring changes to existing AI/ML infrastructure and frameworks. LEIP is a modular, fully-integrated workflow designed to train, quantize, adapt and deploy edge AI neural networks. LEIP is a modular, fully-integrated workflow designed to train, quantize and deploy edge AI neural networks. Latent AI believes in a vibrant and sustainable future driven by the power of AI and the promise of edge computing. Our mission is to deliver on the vast potential of edge AI with solutions that are efficient, practical, and useful. Latent AI helps a variety of federal and commercial organizations gain the most from their edge AI with an automated edge MLOps pipeline that creates ultra-efficient, compressed, and secured edge models at scale while also removing all maintenance and configuration concerns -
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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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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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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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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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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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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. -
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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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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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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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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.Starting Price: Free -
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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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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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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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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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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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Cerebras
Cerebras
We’ve built the fastest AI accelerator, based on the largest processor in the industry, and made it easy to use. With Cerebras, blazing fast training, ultra low latency inference, and record-breaking time-to-solution enable you to achieve your most ambitious AI goals. How ambitious? We make it not just possible, but easy to continuously train language models with billions or even trillions of parameters – with near-perfect scaling from a single CS-2 system to massive Cerebras Wafer-Scale Clusters such as Andromeda, one of the largest AI supercomputers ever built. -
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Foundry Local
Microsoft
Foundry Local is a local version of Azure AI Foundry that enables local execution of large language models (LLMs) directly on your Windows device. This on-device AI inference solution provides privacy, customization, and cost benefits compared to cloud-based alternatives. Best of all, it fits into your existing workflows and applications with an easy-to-use CLI and REST API. -
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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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Gemma 3n
Google DeepMind
Gemma 3n is our state-of-the-art open multimodal model, engineered for on-device performance and efficiency. Made for responsive, low-footprint local inference, Gemma 3n empowers a new wave of intelligent, on-the-go applications. It analyzes and responds to combined images and text, with video and audio coming soon. Build intelligent, interactive features that put user privacy first and work reliably offline. Mobile-first architecture, with a significantly reduced memory footprint. Co-designed by Google's mobile hardware teams and industry leaders. 4B active memory footprint with the ability to create submodels for quality-latency tradeoffs. Gemma 3n is our first open model built on this groundbreaking, shared architecture, allowing developers to begin experimenting with this technology today in an early preview. -
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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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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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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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Mu
Microsoft
Mu is a 330-million-parameter encoder–decoder language model designed to power the agent in Windows settings by mapping natural-language queries to Settings function calls, running fully on-device via NPUs at over 100 tokens per second while maintaining high accuracy. Drawing on Phi Silica optimizations, Mu’s encoder–decoder architecture reuses a fixed-length latent representation to cut computation and memory overhead, yielding 47 percent lower first-token latency and 4.7× higher decoding speed on Qualcomm Hexagon NPUs compared to similar decoder-only models. Hardware-aware tuning, including a 2/3–1/3 encoder–decoder parameter split, weight sharing between input and output embeddings, Dual LayerNorm, rotary positional embeddings, and grouped-query attention, enables fast inference at over 200 tokens per second on devices like Surface Laptop 7 and sub-500 ms response times for settings queries. -
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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.Starting Price: $30 per month -
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Qualcomm AI Inference Suite
Qualcomm
The Qualcomm AI Inference Suite is a comprehensive software platform designed to streamline the deployment of AI models and applications across cloud and on-premises environments. It offers seamless one-click deployment, allowing users to easily integrate their own models, including generative AI, computer vision, and natural language processing, and build custom applications using common frameworks. The suite supports a wide range of AI use cases such as chatbots, AI agents, retrieval-augmented generation (RAG), summarization, image generation, real-time translation, transcription, and code development. Powered by Qualcomm Cloud AI accelerators, it ensures top performance and cost efficiency through embedded optimization techniques and state-of-the-art models. It is designed with high availability and strict data privacy in mind, ensuring that model inputs and outputs are not stored, thus providing enterprise-grade security. -
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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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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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Nexa AI
Nexa AI
Nexa AI enables developers and consumers to run state-of-the-art AI models locally on CPUs, GPUs, and NPUs, removing the reliance on cloud infrastructure. Its flagship Nexa SDK allows developers to deploy any AI model across devices in minutes, supporting compression for efficiency and acceleration on NPUs. For consumers, Hyperlink acts as a private offline AI agent that can search local files, provide insights, and ensure complete data privacy. Nexa’s technology emphasizes three pillars: absolute privacy, predictable cost with pay-per-device licensing, and offline reliability for use in secure or disconnected environments. Proprietary innovations like the NexaML Engine ensure performance optimization across hardware, from PCs to IoT devices. By combining flexibility, security, and speed, Nexa AI brings modern AI capabilities directly to the edge. -
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Accelerate your deep learning workload. Speed your time to value with AI model training and inference. With advancements in compute, algorithm and data access, enterprises are adopting deep learning more widely to extract and scale insight through speech recognition, natural language processing and image classification. Deep learning can interpret text, images, audio and video at scale, generating patterns for recommendation engines, sentiment analysis, financial risk modeling and anomaly detection. High computational power has been required to process neural networks due to the number of layers and the volumes of data to train the networks. Furthermore, businesses are struggling to show results from deep learning experiments implemented in silos.
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Zebra by Mipsology
Mipsology
Zebra by Mipsology is the ideal Deep Learning compute engine for neural network inference. Zebra seamlessly replaces or complements CPUs/GPUs, allowing any neural network to compute faster, with lower power consumption, at a lower cost. Zebra deploys swiftly, seamlessly, and painlessly without knowledge of underlying hardware technology, use of specific compilation tools, or changes to the neural network, the training, the framework, and the application. Zebra computes neural networks at world-class speed, setting a new standard for performance. Zebra runs on highest-throughput boards all the way to the smallest boards. The scaling provides the required throughput, in data centers, at the edge, or in the cloud. Zebra accelerates any neural network, including user-defined neural networks. Zebra processes the same CPU/GPU-based trained neural network with the same accuracy without any change. -
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NetsPresso
Nota AI
NetsPresso is a hardware-aware AI model optimization platform. NetsPresso powers on-device AI across industries, and it's the ultimate platform for hardware-aware AI model development. Lightweight models of LLaMA and Vicuna enable efficient text generation. BK-SDM is a lightweight version of Stable Diffusion models. VLMs combine visual data with natural language understanding. NetsPresso resolves Cloud and server-based AI solutions-related issues, such as limited network, excessive cost, and privacy breaches. NetsPresso is an automatic model compression platform that downsizes computer vision models to a size small enough to be deployed independently on the smaller edge and low-specification devices. Optimization of target models being key, the platform combines a variety of compression methods which enables it to downsize AI models without causing performance degradation. -
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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.
