Alternatives to Amazon SageMaker HyperPod
Compare Amazon SageMaker HyperPod alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Amazon SageMaker HyperPod in 2026. Compare features, ratings, user reviews, pricing, and more from Amazon SageMaker HyperPod competitors and alternatives in order to make an informed decision for your business.
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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. -
2
Tinker
Thinking Machines Lab
Tinker is a training API designed for researchers and developers that allows full control over model fine-tuning while abstracting away the infrastructure complexity. It supports primitives and enables users to build custom training loops, supervision logic, and reinforcement learning flows. It currently supports LoRA fine-tuning on open-weight models across both LLama and Qwen families, ranging from small models to large mixture-of-experts architectures. Users write Python code to handle data, loss functions, and algorithmic logic; Tinker handles scheduling, resource allocation, distributed training, and failure recovery behind the scenes. The service lets users download model weights at different checkpoints and doesn’t force them to manage the compute environment. Tinker is delivered as a managed offering; training jobs run on Thinking Machines’ internal GPU infrastructure, freeing users from cluster orchestration. -
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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 -
4
Amazon Nova Forge
Amazon
Amazon Nova Forge is a groundbreaking service that enables organizations to build their own frontier models by leveraging early Nova checkpoints and proprietary data. It provides complete flexibility across the full training lifecycle, including pre-training, mid-training, supervised fine-tuning, and reinforcement learning. With access to Nova-curated datasets and responsible AI tooling, customers can create powerful and safer custom models tailored to their domain. Nova Forge allows teams to mix their own datasets at the peak learning stage to maximize accuracy while preventing catastrophic forgetting. Companies across industries—from Reddit to Sony—use Nova Forge to consolidate ML workflows, accelerate innovation, and outperform specialized models. Hosted securely on AWS, it offers the most cost-effective, streamlined path to building next-generation AI systems. -
5
FinetuneFast
FinetuneFast
FinetuneFast is your ultimate solution for finetuning AI models and deploying them quickly to start making money online with ease. Here are the key features that make FinetuneFast stand out: - Finetune your ML models in days, not weeks - The ultimate ML boilerplate for text-to-image, LLMs, and more - Build your first AI app and start earning online fast - Pre-configured training scripts for efficient model training - Efficient data loading pipelines for streamlined data processing - Hyperparameter optimization tools for improved model performance - Multi-GPU support out of the box for enhanced processing power - No-Code AI model finetuning for easy customization - One-click model deployment for quick and hassle-free deployment - Auto-scaling infrastructure for seamless scaling as your models grow - API endpoint generation for easy integration with other systems - Monitoring and logging setup for real-time performance tracking -
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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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Amazon SageMaker Model Training reduces the time and cost to train and tune machine learning (ML) models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, SageMaker distributed training libraries can automatically split large models and training datasets across AWS GPU instances, or you can use third-party libraries, such as DeepSpeed, Horovod, or Megatron. Efficiently manage system resources with a wide choice of GPUs and CPUs including P4d.24xl instances, which are the fastest training instances currently available in the cloud. Specify the location of data, indicate the type of SageMaker instances, and get started with a single click.
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8
Tune Studio
NimbleBox
Tune Studio is an intuitive and versatile platform designed to streamline the fine-tuning of AI models with minimal effort. It empowers users to customize pre-trained machine learning models to suit their specific needs without requiring extensive technical expertise. With its user-friendly interface, Tune Studio simplifies the process of uploading datasets, configuring parameters, and deploying fine-tuned models efficiently. Whether you're working on NLP, computer vision, or other AI applications, Tune Studio offers robust tools to optimize performance, reduce training time, and accelerate AI development, making it ideal for both beginners and advanced users in the AI space.Starting Price: $10/user/month -
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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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HPC-AI
HPC-AI
HPC-AI is an enterprise AI infrastructure and GPU cloud platform designed to accelerate deep learning training, inference, and large-scale compute workloads with high performance and cost efficiency. It delivers a pre-configured AI-optimized stack that enables rapid deployment and real-time inference while supporting demanding workloads that require high IOPS, ultra-low latency, and massive throughput. It provides a robust GPU cloud environment built for artificial intelligence, high-performance computing, and other compute-intensive applications, giving teams the tools needed to run complex workflows efficiently. At its core, the company’s software focuses on parallel and distributed training, inference, and fine-tuning of large neural networks, helping organizations reduce infrastructure costs while maintaining performance. It is powered in part by technologies such as Colossal-AI, which significantly accelerates model training and improves productivity.Starting Price: $3.05 per hour -
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Amazon EC2 Trn2 Instances
Amazon
Amazon EC2 Trn2 instances, powered by AWS Trainium2 chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and diffusion models. They offer up to 50% cost-to-train savings over comparable Amazon EC2 instances. Trn2 instances support up to 16 Trainium2 accelerators, providing up to 3 petaflops of FP16/BF16 compute power and 512 GB of high-bandwidth memory. To facilitate efficient data and model parallelism, Trn2 instances feature NeuronLink, a high-speed, nonblocking interconnect, and support up to 1600 Gbps of second-generation Elastic Fabric Adapter (EFAv2) network bandwidth. They are deployed in EC2 UltraClusters, enabling scaling up to 30,000 Trainium2 chips interconnected with a nonblocking petabit-scale network, delivering 6 exaflops of compute performance. The AWS Neuron SDK integrates natively with popular machine learning frameworks like PyTorch and TensorFlow. -
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Replicate
Replicate
Replicate is a platform that enables developers and businesses to run, fine-tune, and deploy machine learning models at scale with minimal effort. It offers an easy-to-use API that allows users to generate images, videos, speech, music, and text using thousands of community-contributed models. Users can fine-tune existing models with their own data to create custom versions tailored to specific tasks. Replicate supports deploying custom models using its open-source tool Cog, which handles packaging, API generation, and scalable cloud deployment. The platform automatically scales compute resources based on demand, charging users only for the compute time they consume. With robust logging, monitoring, and a large model library, Replicate aims to simplify the complexities of production ML infrastructure.Starting Price: Free -
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Helix AI
Helix AI
Build and optimize text and image AI for your needs, train, fine-tune, and generate from your data. We use best-in-class open source models for image and language generation and can train them in minutes thanks to LoRA fine-tuning. Click the share button to create a link to your session, or create a bot. Optionally deploy to your own fully private infrastructure. You can start chatting with open source language models and generating images with Stable Diffusion XL by creating a free account right now. Fine-tuning your model on your own text or image data is as simple as drag’n’drop, and takes 3-10 minutes. You can then chat with and generate images from those fine-tuned models straight away, all using a familiar chat interface.Starting Price: $20 per month -
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Axolotl
Axolotl
Axolotl is an open source tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures. It enables users to train models, supporting methods like full fine-tuning, LoRA, QLoRA, ReLoRA, and GPTQ. Users can customize configurations using simple YAML files or command-line interface overrides, and load different dataset formats, including custom or pre-tokenized datasets. Axolotl integrates with technologies like xFormers, Flash Attention, Liger kernel, RoPE scaling, and multipacking, and works with single or multiple GPUs via Fully Sharded Data Parallel (FSDP) or DeepSpeed. It can be run locally or on the cloud using Docker and supports logging results and checkpoints to several platforms. It is designed to make fine-tuning AI models friendly, fast, and fun, without sacrificing functionality or scale.Starting Price: Free -
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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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Lens
Moondream
Lens is Moondream’s official fine-tuning service, designed to turn a general vision-language model into a highly specialized system tailored to a specific task. It provides a simple, structured workflow where users start by collecting a small dataset of images relevant to their use case, then fine-tune the model through an API using techniques such as supervised fine-tuning (SFT) or reinforcement learning, and finally deploy the customized model either through the cloud or locally with Photon. It is built around the idea that Moondream begins as a general model trained on broad, public data, and fine-tuning adapts it to understand the exact products, documents, categories, or internal information that matter to a business, significantly improving accuracy and reliability for that domain. Lens is designed for production scenarios where performance matters, enabling teams to achieve large gains in accuracy with minimal data by teaching the model to master a defined task.Starting Price: $300 per month -
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Amazon EC2 Capacity Blocks for ML enable you to reserve accelerated compute instances in Amazon EC2 UltraClusters for your machine learning workloads. This service supports Amazon EC2 P5en, P5e, P5, and P4d instances, powered by NVIDIA H200, H100, and A100 Tensor Core GPUs, respectively, as well as Trn2 and Trn1 instances powered by AWS Trainium. You can reserve these instances for up to six months in cluster sizes ranging from one to 64 instances (512 GPUs or 1,024 Trainium chips), providing flexibility for various ML workloads. Reservations can be made up to eight weeks in advance. By colocating in Amazon EC2 UltraClusters, Capacity Blocks offer low-latency, high-throughput network connectivity, facilitating efficient distributed training. This setup ensures predictable access to high-performance computing resources, allowing you to plan ML development confidently, run experiments, build prototypes, and accommodate future surges in demand for ML applications.
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Entry Point AI
Entry Point AI
Entry Point AI is the modern AI optimization platform for proprietary and open source language models. Manage prompts, fine-tunes, and evals all in one place. When you reach the limits of prompt engineering, it’s time to fine-tune a model, and we make it easy. Fine-tuning is showing a model how to behave, not telling. It works together with prompt engineering and retrieval-augmented generation (RAG) to leverage the full potential of AI models. Fine-tuning can help you to get better quality from your prompts. Think of it like an upgrade to few-shot learning that bakes the examples into the model itself. For simpler tasks, you can train a lighter model to perform at or above the level of a higher-quality model, greatly reducing latency and cost. Train your model not to respond in certain ways to users, for safety, to protect your brand, and to get the formatting right. Cover edge cases and steer model behavior by adding examples to your dataset.Starting Price: $49 per month -
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LLaMA-Factory
hoshi-hiyouga
LLaMA-Factory is an open source platform designed to streamline and enhance the fine-tuning process of over 100 Large Language Models (LLMs) and Vision-Language Models (VLMs). It supports various fine-tuning techniques, including Low-Rank Adaptation (LoRA), Quantized LoRA (QLoRA), and Prefix-Tuning, allowing users to customize models efficiently. It has demonstrated significant performance improvements; for instance, its LoRA tuning offers up to 3.7 times faster training speeds with better Rouge scores on advertising text generation tasks compared to traditional methods. LLaMA-Factory's architecture is designed for flexibility, supporting a wide range of model architectures and configurations. Users can easily integrate their datasets and utilize the platform's tools to achieve optimized fine-tuning results. Detailed documentation and diverse examples are provided to assist users in navigating the fine-tuning process effectively.Starting Price: Free -
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Ilus AI
Ilus AI
The quickest way to get started with our illustration generator is to use pre-made models. If you want to depict a style or an object that is not available in the premade models you can train your own fine tune by uploading 5-15 illustrations. there are no limits to fine-tuning you can use it for illustrations icons or any assets you need. Read more about fine-tuning. Illustrations are exportable in PNG and SVG formats. Fine-tuning allows you to train the stable-diffusion AI model, on a particular object or style, and create a new model that generates images of those objects or styles. The fine-tuning will be only as good as the data you provide. Around 5-15 images are recommended for fine-tuning. Images can be of any unique object or style. Images should contain only the subject itself, without background noise or other objects. Images must not include any gradients or shadows if you want to export it as SVG later. PNG export still works fine with gradients and shadows.Starting Price: $0.06 per credit -
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AWS EC2 Trn3 Instances
Amazon
Amazon EC2 Trn3 UltraServers are AWS’s newest accelerated computing instances, powered by the in-house Trainium3 AI chips and engineered specifically for high-performance deep-learning training and inference workloads. These UltraServers are offered in two configurations, a “Gen1” with 64 Trainium3 chips and a “Gen2” with up to 144 Trainium3 chips per UltraServer. The Gen2 configuration delivers up to 362 petaFLOPS of dense MXFP8 compute, 20 TB of HBM memory, and a staggering 706 TB/s of aggregate memory bandwidth, making it one of the highest-throughput AI compute platforms available. Interconnects between chips are handled by a new “NeuronSwitch-v1” fabric to support all-to-all communication patterns, which are especially important for large models, mixture-of-experts architectures, or large-scale distributed training. -
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prompteasy.ai
prompteasy.ai
You can now fine-tune GPT with absolutely zero technical skills. Enhance AI models by tailoring them to your specific needs. Prompteasy.ai helps you fine-tune AI models in a matter of seconds. We make AI tailored to your needs by helping you fine-tune it. The best part is, that you don't even have to know AI fine-tuning. Our AI models will take care of everything. We will be offering prompteasy for free as part of our initial launch. We'll be rolling out pricing plans later this year. Our vision is to make AI smart and easily accessible to anyone. We believe that the true power of AI lies in how we train and orchestrate the foundational models, as opposed to just using them off the shelf. Forget generating massive datasets, just upload relevant materials and interact with our AI through natural language. We take care of building the dataset ready for fine-tuning. You just chat with the AI, download the dataset, and fine-tune GPT.Starting Price: Free -
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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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Compute with Hivenet
Hivenet
Compute with Hivenet is the world's first truly distributed cloud computing platform, providing reliable and affordable on-demand computing power from a certified network of contributors. Designed for AI model training, inference, and other compute-intensive tasks, it provides secure, scalable, and on-demand GPU resources at up to 70% cost savings compared to traditional cloud providers. Powered by RTX 4090 GPUs, Compute rivals top-tier platforms, offering affordable, transparent pricing with no hidden fees. Compute is part of the Hivenet ecosystem, a comprehensive suite of distributed cloud solutions that prioritizes sustainability, security, and affordability. Through Hivenet, users can leverage their underutilized hardware to contribute to a powerful, distributed cloud infrastructure.Starting Price: $0.10/hour -
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AWS AI Factories
Amazon
AWS AI Factories is a fully-managed solution that embeds high-performance AI infrastructure directly into a customer’s own data center. You supply the space and power, and AWS deploys a dedicated, secure AI environment optimized for training and inference. It includes leading AI accelerators (such as AWS Trainium chips or NVIDIA GPUs), low-latency networking, high-performance storage, and integration with AWS’s AI services, such as Amazon SageMaker and Amazon Bedrock, giving immediate access to foundational models and AI tools without separate licensing or contracts. AWS handles the full deployment, maintenance, and management, eliminating the typical months-long effort to build comparable infrastructure. Each deployment is isolated, operating like a private AWS Region, which meets strict data sovereignty, compliance, and regulatory requirements, making it particularly suited for sectors with sensitive data. -
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FinetuneDB
FinetuneDB
Capture production data, evaluate outputs collaboratively, and fine-tune your LLM's performance. Know exactly what goes on in production with an in-depth log overview. Collaborate with product managers, domain experts and engineers to build reliable model outputs. Track AI metrics such as speed, quality scores, and token usage. Copilot automates evaluations and model improvements for your use case. Create, manage, and optimize prompts to achieve precise and relevant interactions between users and AI models. Compare foundation models, and fine-tuned versions to improve prompt performance and save tokens. Collaborate with your team to build a proprietary fine-tuning dataset for your AI models. Build custom fine-tuning datasets to optimize model performance for specific use cases. -
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Forefront
Forefront.ai
Powerful language models a click away. Join over 8,000 developers building the next wave of world-changing applications. Fine-tune and deploy GPT-J, GPT-NeoX, Codegen, and FLAN-T5. Multiple models, each with different capabilities and price points. GPT-J is the fastest model, while GPT-NeoX is the most powerful—and more are on the way. Use these models for classification, entity extraction, code generation, chatbots, content generation, summarization, paraphrasing, sentiment analysis, and much more. These models have been pre-trained on a vast amount of text from the open internet. Fine-tuning improves upon this for specific tasks by training on many more examples than can fit in a prompt, letting you achieve better results on a wide number of tasks. -
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Dynamiq
Dynamiq
Dynamiq is a platform built for engineers and data scientists to build, deploy, test, monitor and fine-tune Large Language Models for any use case the enterprise wants to tackle. Key features: 🛠️ Workflows: Build GenAI workflows in a low-code interface to automate tasks at scale 🧠 Knowledge & RAG: Create custom RAG knowledge bases and deploy vector DBs in minutes 🤖 Agents Ops: Create custom LLM agents to solve complex task and connect them to your internal APIs 📈 Observability: Log all interactions, use large-scale LLM quality evaluations 🦺 Guardrails: Precise and reliable LLM outputs with pre-built validators, detection of sensitive content, and data leak prevention 📻 Fine-tuning: Fine-tune proprietary LLM models to make them your ownStarting Price: $125/month -
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OpenPipe
OpenPipe
OpenPipe provides fine-tuning for developers. Keep your datasets, models, and evaluations all in one place. Train new models with the click of a button. Automatically record LLM requests and responses. Create datasets from your captured data. Train multiple base models on the same dataset. We serve your model on our managed endpoints that scale to millions of requests. Write evaluations and compare model outputs side by side. Change a couple of lines of code, and you're good to go. Simply replace your Python or Javascript OpenAI SDK and add an OpenPipe API key. Make your data searchable with custom tags. Small specialized models cost much less to run than large multipurpose LLMs. Replace prompts with models in minutes, not weeks. Fine-tuned Mistral and Llama 2 models consistently outperform GPT-4-1106-Turbo, at a fraction of the cost. We're open-source, and so are many of the base models we use. Own your own weights when you fine-tune Mistral and Llama 2, and download them at any time.Starting Price: $1.20 per 1M tokens -
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Unsloth
Unsloth
Unsloth is an open source platform designed to accelerate and optimize the fine-tuning and training of Large Language Models (LLMs). It enables users to train custom models, such as ChatGPT, in just 24 hours instead of the typical 30 days, achieving speeds up to 30 times faster than Flash Attention 2 (FA2) while using 90% less memory. Unsloth supports both LoRA and QLoRA fine-tuning techniques, allowing for efficient customization of models like Mistral, Gemma, and Llama versions 1, 2, and 3. Unsloth's efficiency stems from manually deriving computationally intensive mathematical steps and handwriting GPU kernels, resulting in significant performance gains without requiring hardware modifications. Unsloth delivers a 10x speed increase on a single GPU and up to 32x on multi-GPU systems compared to FA2, with compatibility across NVIDIA GPUs from Tesla T4 to H100, and portability to AMD and Intel GPUs.Starting Price: Free -
31
Bakery
Bakery
Easily fine-tune & monetize your AI models with one click. For AI startups, ML engineers, and researchers. Bakery is a platform that enables AI startups, machine learning engineers, and researchers to fine-tune and monetize AI models with ease. Users can create or upload datasets, adjust model settings, and publish their models on the marketplace. The platform supports various model types and provides access to community-driven datasets for project development. Bakery's fine-tuning process is streamlined, allowing users to build, test, and deploy models efficiently. The platform integrates with tools like Hugging Face and supports decentralized storage solutions, ensuring flexibility and scalability for diverse AI projects. The bakery empowers contributors to collaboratively build AI models without exposing model parameters or data to one another. It ensures proper attribution and fair revenue distribution to all contributors.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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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 -
34
Amazon EC2 Trn1 Instances
Amazon
Amazon Elastic Compute Cloud (EC2) Trn1 instances, powered by AWS Trainium chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and latent diffusion models. Trn1 instances offer up to 50% cost-to-train savings over other comparable Amazon EC2 instances. You can use Trn1 instances to train 100B+ parameter DL and generative AI models across a broad set of applications, such as text summarization, code generation, question answering, image and video generation, recommendation, and fraud detection. The AWS Neuron SDK helps developers train models on AWS Trainium (and deploy models on the AWS Inferentia chips). It integrates natively with frameworks such as PyTorch and TensorFlow so that you can continue using your existing code and workflows to train models on Trn1 instances.Starting Price: $1.34 per hour -
35
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 -
36
Pipeshift
Pipeshift
Pipeshift is a modular orchestration platform designed to facilitate the building, deployment, and scaling of open source AI components, including embeddings, vector databases, large language models, vision models, and audio models, across any cloud environment or on-premises infrastructure. The platform offers end-to-end orchestration, ensuring seamless integration and management of AI workloads, and is 100% cloud-agnostic, providing flexibility in deployment. With enterprise-grade security, Pipeshift addresses the needs of DevOps and MLOps teams aiming to establish production pipelines in-house, moving beyond experimental API providers that may lack privacy considerations. Key features include an enterprise MLOps console for managing various AI workloads such as fine-tuning, distillation, and deployment; multi-cloud orchestration with built-in auto-scalers, load balancers, and schedulers for AI models; and Kubernetes cluster management. -
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ReByte
RealChar.ai
Action-based orchestration to build complex backend agents with multiple steps. Working for all LLMs, build fully customized UI for your agent without writing a single line of code, serving on your domain. Track every step of your agent, literally every step, to deal with the nondeterministic nature of LLMs. Build fine-grain access control over your application, data, and agent. Specialized fine-tuned model for accelerating software development. Automatically handle concurrency, rate limiting, and more.Starting Price: $10 per month -
38
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 -
39
Tune AI
NimbleBox
Leverage the power of custom models to build your competitive advantage. With our enterprise Gen AI stack, go beyond your imagination and offload manual tasks to powerful assistants instantly – the sky is the limit. For enterprises where data security is paramount, fine-tune and deploy generative AI models on your own cloud, securely. -
40
vishwa.ai
vishwa.ai
vishwa.ai is an AutoOps platform for AI and ML use cases. It provides expert prompt delivery, fine-tuning, and monitoring of Large Language Models (LLMs). Features: Expert Prompt Delivery: Tailored prompts for various applications. Create no-code LLM Apps: Build LLM workflows in no time with our drag-n-drop UI Advanced Fine-Tuning: Customization of AI models. LLM Monitoring: Comprehensive oversight of model performance. Integration and Security Cloud Integration: Supports Google Cloud, AWS, Azure. Secure LLM Integration: Safe connection with LLM providers. Automated Observability: For efficient LLM management. Managed Self-Hosting: Dedicated hosting solutions. Access Control and Audits: Ensuring secure and compliant operations.Starting Price: $39 per month -
41
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 -
42
Sync
Sync Computing
Sync Computing offers Gradient, an AI-powered compute optimization engine designed to enhance data infrastructure efficiency. By leveraging advanced machine learning algorithms developed at MIT, Gradient provides automated optimization for organizations running data workloads on cloud-based CPUs or GPUs. Users can achieve up to 50% cost savings on their Databricks compute expenses while consistently meeting runtime service level agreements (SLAs). Gradient's continuous monitoring and fine-tuning capabilities ensure optimal performance across complex data pipelines, adapting seamlessly to varying data sizes and workload patterns. The platform integrates with existing data tools and supports multiple cloud providers, offering a comprehensive solution for managing and optimizing data infrastructure. -
43
SambaNova
SambaNova Systems
SambaNova is the leading purpose-built AI system for generative and agentic AI implementations, from chips to models, that gives enterprises full control over their model and private data. We take the best models, optimize them for fast tokens and higher batch sizes, the largest inputs and enable customizations to deliver value with simplicity. The full suite includes the SambaNova DataScale system, the SambaStudio software, and the innovative SambaNova Composition of Experts (CoE) model architecture. These components combine into a powerful platform that delivers unparalleled performance, ease of use, accuracy, data privacy, and the ability to power every use case across the world's largest organizations. We give our customers the optionality to experience through the cloud or on-premise. -
44
Cerebrium
Cerebrium
Deploy all major ML frameworks such as Pytorch, Onnx, XGBoost etc with 1 line of code. Don't have your own models? Deploy our prebuilt models that have been optimised to run with sub-second latency. Fine-tune smaller models on particular tasks in order to decrease costs and latency while increasing performance. It takes just a few lines of code and don't worry about infrastructure, we got it. Integrate with top ML observability platforms in order to be alerted about feature or prediction drift, compare model versions and resolve issues quickly. Discover the root causes for prediction and feature drift to resolve degraded model performance. Understand which features are contributing most to the performance of your model.Starting Price: $ 0.00055 per second -
45
Riku
Riku
Fine-tuning happens when you take a dataset and build out a model to use with AI. It isn't always easy to do this without code so we built a solution into RIku which handles everything in a very simple format. Fine-tuning unlocks a whole new level of power for AI and we're excited to help you explore it. Public Share Links are individual landing pages that you can create for any of your prompts. You can design these with your brand in mind in terms of colors and adding a logo and your own welcome text. Share these links with anyone publicly and if they have the password to unlock it, they will be able to make generations. A no-code writing assistant builder on a micro scale for your audience! One of the big headaches we found with projects using multiple large language models is that they all return their outputs slightly differently.Starting Price: $29 per month -
46
Gradient
Gradient
Fine-tune and get completions on private LLMs with a simple web API. No infrastructure is needed. Build private, SOC2-compliant AI applications instantly. Personalize models to your use case easily with our developer platform. Simply define the data you want to teach it and pick the base model - we take care of the rest. Put private LLMs into applications with a single API call, no more dealing with deployment, orchestration, or infrastructure hassles. The most powerful OSS model available—highly generalized capabilities with amazing narrative and reasoning capabilities. Harness a fully unlocked LLM to build the highest quality internal automation systems for your company.Starting Price: $0.0005 per 1,000 tokens -
47
Mistral Forge
Mistral AI
Mistral AI’s Forge platform enables enterprises to build customized AI models tailored to their internal data, workflows, and domain expertise. It provides end-to-end model development capabilities, covering everything from pre-training and synthetic data generation to reinforcement learning and evaluation. Organizations can integrate proprietary datasets and decision frameworks to create models that align closely with their business needs. Forge supports flexible deployment options, allowing companies to run models on-premises, in private cloud environments, or through Mistral infrastructure. The platform emphasizes security and governance, ensuring strict data isolation and compliance with enterprise policies. It also includes advanced evaluation tools that measure performance based on business-specific KPIs rather than generic benchmarks. By managing the full AI lifecycle in one system, Forge helps companies transform institutional knowledge into high-performing AI. -
48
AWS Neuron
Amazon Web Services
It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP). -
49
Cerbrec Graphbook
Cerbrec
Construct your model directly as a live, interactive graph. Preview data flowing through your visualized model architecture. View and edit your visualized model architecture down to the atomic level. Graphbook provides X-ray transparency with no black boxes. Graphbook live checks data type and shape with understandable error messages, making your model debugging quick and easy. Abstracting out software dependencies and environment configuration, Graphbook allows you to focus on model architecture and data flow with the handy computing resources needed. Cerbrec Graphbook is a visual IDE for AI modeling, transforming cumbersome model development into a user-friendly experience. With a growing community of machine learning engineers and data scientists, Graphbook helps developers work with their text and tabular data to fine-tune language models such as BERT and GPT. Everything is fully managed out of the box so you can preview your model exactly as it will behave. -
50
Stochastic
Stochastic
Enterprise-ready AI system that trains locally on your data, deploys on your cloud and scales to millions of users without an engineering team. Build customize and deploy your own chat-based AI. Finance chatbot. xFinance, a 13-billion parameter model fine-tuned on an open-source model using LoRA. Our goal was to show that it is possible to achieve impressive results in financial NLP tasks without breaking the bank. Personal AI assistant, your own AI to chat with your documents. Single or multiple documents, easy or complex questions, and much more. Effortless deep learning platform for enterprises, hardware efficient algorithms to speed up inference at a lower cost. Real-time logging and monitoring of resource utilization and cloud costs of deployed models. xTuring is an open-source AI personalization software. xTuring makes it easy to build and control LLMs by providing a simple interface to personalize LLMs to your own data and application.
