Best AI Inference Platforms for Docker
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Compare the Top AI Inference Platforms that integrate with Docker as of July 2025
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This a list of AI Inference platforms that integrate with Docker. Use the filters on the left to add additional filters for products that have integrations with Docker. View the products that work with Docker in the table below.
What are AI Inference Platforms for Docker?
AI inference platforms enable the deployment, optimization, and real-time execution of machine learning models in production environments. These platforms streamline the process of converting trained models into actionable insights by providing scalable, low-latency inference services. They support multiple frameworks, hardware accelerators (like GPUs, TPUs, and specialized AI chips), and offer features such as batch processing and model versioning. Many platforms also prioritize cost-efficiency, energy savings, and simplified API integrations for seamless model deployment. By leveraging AI inference platforms, organizations can accelerate AI-driven decision-making in applications like computer vision, natural language processing, and predictive analytics. Compare and read user reviews of the best AI Inference platforms for Docker currently available using the table below. This list is updated regularly.
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1
Google AI Studio
Google
AI inference in Google AI Studio allows businesses to leverage trained models to make real-time predictions or decisions based on new, incoming data. This process is critical for deploying AI applications in production, such as recommendation systems, fraud detection tools, or intelligent chatbots that respond to user inputs. Google AI Studio optimizes the inference process to ensure that predictions are both fast and accurate, even when dealing with large-scale data. With built-in tools for model monitoring and performance tracking, users can ensure that their AI applications continue to deliver reliable results over time, even as data evolves.Starting Price: Free -
2
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.Starting Price: $0.40 per hour -
3
GMI Cloud
GMI Cloud
Build your generative AI applications in minutes on GMI GPU Cloud. GMI Cloud is more than bare metal. Train, fine-tune, and infer state-of-the-art models. Our clusters are ready to go with scalable GPU containers and preconfigured popular ML frameworks. Get instant access to the latest GPUs for your AI workloads. Whether you need flexible on-demand GPUs or dedicated private cloud instances, we've got you covered. Maximize GPU resources with our turnkey Kubernetes software. Easily allocate, deploy, and monitor GPUs or nodes with our advanced orchestration tools. Customize and serve models to build AI applications using your data. GMI Cloud lets you deploy any GPU workload quickly and easily, so you can focus on running ML models, not managing infrastructure. Launch pre-configured environments and save time on building container images, installing software, downloading models, and configuring environment variables. Or use your own Docker image to fit your needs.Starting Price: $2.50 per hour -
4
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 -
5
Lamini
Lamini
Lamini makes it possible for enterprises to turn proprietary data into the next generation of LLM capabilities, by offering a platform for in-house software teams to uplevel to OpenAI-level AI teams and to build within the security of their existing infrastructure. Guaranteed structured output with optimized JSON decoding. Photographic memory through retrieval-augmented fine-tuning. Improve accuracy, and dramatically reduce hallucinations. Highly parallelized inference for large batch inference. Parameter-efficient finetuning that scales to millions of production adapters. Lamini is the only company that enables enterprise companies to safely and quickly develop and control their own LLMs anywhere. It brings several of the latest technologies and research to bear that was able to make ChatGPT from GPT-3, as well as Github Copilot from Codex. These include, among others, fine-tuning, RLHF, retrieval-augmented training, data augmentation, and GPU optimization.Starting Price: $99 per month -
6
Pruna AI
Pruna AI
Pruna uses generative AI to enable companies to produce professional-grade visual content quickly and affordably. By eliminating the traditional need for studios and manual editing, it empowers brands to create consistent, customized images for advertising, product displays, and digital campaigns with minimal effort.Starting Price: $0.40 per runtime hour -
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Amazon EC2 G5 Instances
Amazon
Amazon EC2 G5 instances are the latest generation of NVIDIA GPU-based instances that can be used for a wide range of graphics-intensive and machine-learning use cases. They deliver up to 3x better performance for graphics-intensive applications and machine learning inference and up to 3.3x higher performance for machine learning training compared to Amazon EC2 G4dn instances. Customers can use G5 instances for graphics-intensive applications such as remote workstations, video rendering, and gaming to produce high-fidelity graphics in real time. With G5 instances, machine learning customers get high-performance and cost-efficient infrastructure to train and deploy larger and more sophisticated models for natural language processing, computer vision, and recommender engine use cases. G5 instances deliver up to 3x higher graphics performance and up to 40% better price performance than G4dn instances. They have more ray tracing cores than any other GPU-based EC2 instance.Starting Price: $1.006 per hour -
8
NVIDIA NIM
NVIDIA
Explore the latest optimized AI models, connect AI agents to data with NVIDIA NeMo, and deploy anywhere with NVIDIA NIM microservices. NVIDIA NIM is a set of easy-to-use inference microservices that facilitate the deployment of foundation models across any cloud or data center, ensuring data security and streamlined AI integration. Additionally, NVIDIA AI provides access to the Deep Learning Institute (DLI), offering technical training to gain in-demand skills, hands-on experience, and expert knowledge in AI, data science, and accelerated computing. AI models generate responses and outputs based on complex algorithms and machine learning techniques, and those responses or outputs may be inaccurate, harmful, biased, or indecent. By testing this model, you assume the risk of any harm caused by any response or output of the model. Please do not upload any confidential information or personal data unless expressly permitted. Your use is logged for security purposes. -
9
Tenstorrent DevCloud
Tenstorrent
We developed Tenstorrent DevCloud to give people the opportunity to try their models on our servers without purchasing our hardware. We are building Tenstorrent AI in the cloud so programmers can try our AI solutions. The first log-in is free, after that, you get connected with our team who can help better assess your needs. Tenstorrent is a team of competent and motivated people that came together to build the best computing platform for AI and software 2.0. Tenstorrent is a next-generation computing company with the mission of addressing the rapidly growing computing demands for software 2.0. Headquartered in Toronto, Canada, Tenstorrent brings together experts in the field of computer architecture, basic design, advanced systems, and neural network compilers. ur processors are optimized for neural network inference and training. They can also execute other types of parallel computation. Tenstorrent processors comprise a grid of cores known as Tensix cores. -
10
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. -
11
Second State
Second State
Fast, lightweight, portable, rust-powered, and OpenAI compatible. We work with cloud providers, especially edge cloud/CDN compute providers, to support microservices for web apps. Use cases include AI inference, database access, CRM, ecommerce, workflow management, and server-side rendering. We work with streaming frameworks and databases to support embedded serverless functions for data filtering and analytics. The serverless functions could be database UDFs. They could also be embedded in data ingest or query result streams. Take full advantage of the GPUs, write once, and run anywhere. Get started with the Llama 2 series of models on your own device in 5 minutes. Retrieval-argumented generation (RAG) is a very popular approach to building AI agents with external knowledge bases. Create an HTTP microservice for image classification. It runs YOLO and Mediapipe models at native GPU speed. -
12
Open WebUI
Open WebUI
Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It supports various LLM runners like Ollama and OpenAI-compatible APIs, with a built-in inference engine for Retrieval Augmented Generation (RAG), making it a powerful AI deployment solution. Key features include effortless setup via Docker or Kubernetes, seamless integration with OpenAI-compatible APIs, granular permissions and user groups for enhanced security, responsive design across devices, and full Markdown and LaTeX support for enriched interactions. Additionally, Open WebUI offers a Progressive Web App (PWA) for mobile devices, providing offline access and a native app-like experience. The platform also includes a Model Builder, allowing users to create custom models from base Ollama models directly within the interface. With over 156,000 users, Open WebUI is a versatile solution for deploying and managing AI models in a secure, offline environment. -
13
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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Beam Cloud
Beam Cloud
Beam is a serverless GPU platform designed for developers to deploy AI workloads with minimal configuration and rapid iteration. It enables running custom models with sub-second container starts and zero idle GPU costs, allowing users to bring their code while Beam manages the infrastructure. It supports launching containers in 200ms using a custom runc runtime, facilitating parallelization and concurrency by fanning out workloads to hundreds of containers. Beam offers a first-class developer experience with features like hot-reloading, webhooks, and scheduled jobs, and supports scale-to-zero workloads by default. It provides volume storage options, GPU support, including running on Beam's cloud with GPUs like 4090s and H100s or bringing your own, and Python-native deployment without the need for YAML or config files. -
15
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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