Alternatives to Oumi
Compare Oumi alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Oumi in 2026. Compare features, ratings, user reviews, pricing, and more from Oumi competitors and alternatives in order to make an informed decision for your business.
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1
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
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 -
3
Microsoft Foundry Models
Microsoft
Microsoft Foundry Models is a unified model catalog that gives enterprises access to more than 11,000 AI models from Microsoft, OpenAI, Anthropic, Mistral AI, Meta, Cohere, DeepSeek, xAI, and others. It allows teams to explore, test, and deploy models quickly using a task-centric discovery experience and integrated playground. Organizations can fine-tune models with ready-to-use pipelines and evaluate performance using their own datasets for more accurate benchmarking. Foundry Models provides secure, scalable deployment options with serverless and managed compute choices tailored to enterprise needs. With built-in governance, compliance, and Azure’s global security framework, businesses can safely operationalize AI across mission-critical workflows. The platform accelerates innovation by enabling developers to build, iterate, and scale AI solutions from one centralized environment. -
4
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 -
5
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 -
6
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. -
7
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 -
8
Tülu 3
Ai2
Tülu 3 is an advanced instruction-following language model developed by the Allen Institute for AI (Ai2), designed to enhance capabilities in areas such as knowledge, reasoning, mathematics, coding, and safety. Built upon the Llama 3 Base, Tülu 3 employs a comprehensive four-stage post-training process: meticulous prompt curation and synthesis, supervised fine-tuning on a diverse set of prompts and completions, preference tuning using both off- and on-policy data, and a novel reinforcement learning approach to bolster specific skills with verifiable rewards. This open-source model distinguishes itself by providing full transparency, including access to training data, code, and evaluation tools, thereby closing the performance gap between open and proprietary fine-tuning methods. Evaluations indicate that Tülu 3 outperforms other open-weight models of similar size, such as Llama 3.1-Instruct and Qwen2.5-Instruct, across various benchmarks.Starting Price: Free -
9
Alibaba Cloud Model Studio
Alibaba
Model Studio is Alibaba Cloud’s one-stop generative AI platform that lets developers build intelligent, business-aware applications using industry-leading foundation models like Qwen-Max, Qwen-Plus, Qwen-Turbo, the Qwen-2/3 series, visual-language models (Qwen-VL/Omni), and the video-focused Wan series. Users can access these powerful GenAI models through familiar OpenAI-compatible APIs or purpose-built SDKs, no infrastructure setup required. It supports a full development workflow, experiment with models in the playground, perform real-time and batch inferences, fine-tune with tools like SFT or LoRA, then evaluate, compress, accelerate deployment, and monitor performance, all within an isolated Virtual Private Cloud (VPC) for enterprise-grade security. Customization is simplified via one-click Retrieval-Augmented Generation (RAG), enabling integration of business data into model outputs. Visual, template-driven interfaces facilitate prompt engineering and application design. -
10
Qwen2.5-Max
Alibaba
Qwen2.5-Max is a large-scale Mixture-of-Experts (MoE) model developed by the Qwen team, pretrained on over 20 trillion tokens and further refined through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). In evaluations, it outperforms models like DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also demonstrating competitive results in other assessments, including MMLU-Pro. Qwen2.5-Max is accessible via API through Alibaba Cloud and can be explored interactively on Qwen Chat.Starting Price: Free -
11
LFM2.5
Liquid AI
Liquid AI’s LFM2.5 is the next generation of on-device AI foundation models designed to deliver high-performance, efficient AI inference on edge devices such as phones, laptops, vehicles, IoT systems, and embedded hardware without relying on cloud compute. It extends the previous LFM2 architecture by significantly increasing the pretraining scale and reinforcement learning stages, yielding a family of hybrid models around 1.2 billion parameters that balance instruction following, reasoning, and multimodal capabilities for real-world agentic use cases. The LFM2.5 family includes Base (for fine-tuning and customization), Instruct (general-purpose instruction-tuned), Japanese-optimized, Vision-Language, and Audio-Language variants, all optimized for fast, on-device inference under tight memory constraints and available as open-weight models deployable via frameworks like llama.cpp, MLX, vLLM, and ONNX.Starting Price: Free -
12
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 -
13
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 -
14
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. -
15
Protopia AI
Protopia AI
Protopia AI’s Stained Glass Transform (SGT) offers a cutting-edge solution to secure sensitive data in AI workloads by preventing data exposure during processing and inference. It enables enterprises to maximize the value of their data by breaking down silos while retaining full ownership and security. SGT supports deployment across diverse environments, including on-premises, hybrid, and multi-tenant clouds, optimizing GPU use for performance. It runs up to 14,000 times faster than traditional cryptographic methods, ensuring AI inference adds only minimal latency. The platform is designed to meet the needs of industries with strict data privacy requirements, such as finance, defense, and healthcare. Protopia’s technology integrates with AWS Marketplace and partners like Lambda and vLLM to provide comprehensive, high-performance, secure AI inference solutions. -
16
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 -
17
Parasail
Parasail
Parasail is an AI deployment network offering scalable, cost-efficient access to high-performance GPUs for AI workloads. It provides three primary services, serverless endpoints for real-time inference, Dedicated instances for private model deployments, and Batch processing for large-scale tasks. Users can deploy open source models like DeepSeek R1, LLaMA, and Qwen, or bring their own, with the platform's permutation engine matching workloads to optimal hardware, including NVIDIA's H100, H200, A100, and 4090 GPUs. Parasail emphasizes rapid deployment, with the ability to scale from a single GPU to clusters within minutes, and offers significant cost savings, claiming up to 30x cheaper compute compared to legacy cloud providers. It supports day-zero availability for new models and provides a self-service interface without long-term contracts or vendor lock-in.Starting Price: $0.80 per million tokens -
18
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 -
19
Qwen-7B
Alibaba
Qwen-7B is the 7B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-7B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-7B, we release Qwen-7B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. The features of the Qwen-7B series include: Trained with high-quality pretraining data. We have pretrained Qwen-7B on a self-constructed large-scale high-quality dataset of over 2.2 trillion tokens. The dataset includes plain texts and codes, and it covers a wide range of domains, including general domain data and professional domain data. Strong performance. In comparison with the models of the similar model size, we outperform the competitors on a series of benchmark datasets, which evaluates natural language understanding, mathematics, coding, etc. And more.Starting Price: Free -
20
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. -
21
Qwen2.5-1M
Alibaba
Qwen2.5-1M is an open-source language model developed by the Qwen team, designed to handle context lengths of up to one million tokens. This release includes two model variants, Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, marking the first time Qwen models have been upgraded to support such extensive context lengths. To facilitate efficient deployment, the team has also open-sourced an inference framework based on vLLM, integrated with sparse attention methods, enabling processing of 1M-token inputs with a 3x to 7x speed improvement. Comprehensive technical details, including design insights and ablation experiments, are available in the accompanying technical report.Starting Price: Free -
22
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 -
23
Llama Stack
Meta
Llama Stack is a modular framework designed to streamline the development of applications powered by Meta's Llama language models. It offers a client-server architecture with flexible configurations, allowing developers to mix and match various providers for components such as inference, memory, agents, telemetry, and evaluations. The framework includes pre-configured distributions tailored for different deployment scenarios, enabling seamless transitions from local development to production environments. Developers can interact with the Llama Stack server using client SDKs available in multiple programming languages, including Python, Node.js, Swift, and Kotlin. Comprehensive documentation and example applications are provided to assist users in building and deploying Llama-based applications efficiently.Starting Price: Free -
24
Intel Gaudi Software
Intel
Intel’s Gaudi software gives developers access to a comprehensive set of tools, libraries, containers, model references, and documentation that support creation, migration, optimization, and deployment of AI models on Intel® Gaudi® accelerators. It helps streamline every stage of AI development including training, fine-tuning, debugging, profiling, and performance optimization for generative AI (GenAI) and large language models (LLMs) on Gaudi hardware, whether in data centers or cloud environments. It includes up-to-date documentation with code samples, best practices, API references, and guides for efficient use of Gaudi solutions such as Gaudi 2 and Gaudi 3, and it integrates with popular frameworks and tools to support model portability and scalability. Users can access performance data to review training and inference benchmarks, utilize community and support resources, and take advantage of containers and libraries tailored to high-performance AI workloads. -
25
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 -
26
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 -
27
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. -
28
Llama Guard
Meta
Llama Guard is an open-source safeguard model developed by Meta AI to enhance the safety of large language models in human-AI conversations. It functions as an input-output filter, classifying both prompts and responses into safety risk categories, including toxicity, hate speech, and hallucinations. Trained on a curated dataset, Llama Guard achieves performance on par with or exceeding existing moderation tools like OpenAI's Moderation API and ToxicChat. Its instruction-tuned architecture allows for customization, enabling developers to adapt its taxonomy and output formats to specific use cases. Llama Guard is part of Meta's broader "Purple Llama" initiative, which combines offensive and defensive security strategies to responsibly deploy generative AI models. The model weights are publicly available, encouraging further research and adaptation to meet evolving AI safety needs. -
29
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. -
30
LMCache
LMCache
LMCache is an open source Knowledge Delivery Network (KDN) designed as a caching layer for large language model serving that accelerates inference by reusing KV (key-value) caches across repeated or overlapping computations. It enables fast prompt caching, allowing LLMs to “prefill” recurring text only once and then reuse those stored KV caches, even in non-prefix positions, across multiple serving instances. This approach reduces time to first token, saves GPU cycles, and increases throughput in scenarios such as multi-round question answering or retrieval augmented generation. LMCache supports KV cache offloading (moving cache from GPU to CPU or disk), cache sharing across instances, and disaggregated prefill, which separates the prefill and decoding phases for resource efficiency. It is compatible with inference engines like vLLM and TGI and supports compressed storage, blending techniques to merge caches, and multiple backend storage options.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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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 -
33
Evoke
Evoke
Focus on building, we’ll take care of hosting. Just plug and play with our rest API. No limits, no headaches. We have all the inferencing capacity you need. Stop paying for nothing. We’ll only charge based on use. Our support team is our tech team too. So you’ll be getting support directly rather than jumping through hoops. The flexible infrastructure allows us to scale with you as you grow and handle any spikes in activity. Image and art generation from text to image or image to image with clear documentation with our stable diffusion API. Change the output's art style with additional models. MJ v4, Anything v3, Analog, Redshift, and more. Other stable diffusion versions like 2.0+ will also be included. Train your own stable diffusion model (fine-tuning) and deploy on Evoke as an API. We plan to have other models like Whisper, Yolo, GPT-J, GPT-NEOX, and many more in the future for not only inference but also training and deployment.Starting Price: $0.0017 per compute second -
34
GLM-5
Zhipu AI
GLM-5 is Z.ai’s latest large language model built for complex systems engineering and long-horizon agentic tasks. It scales significantly beyond GLM-4.5, increasing total parameters and training data while integrating DeepSeek Sparse Attention to reduce deployment costs without sacrificing long-context capacity. The model combines enhanced pre-training with a new asynchronous reinforcement learning infrastructure called slime, improving training efficiency and post-training refinement. GLM-5 achieves best-in-class performance among open-source models across reasoning, coding, and agent benchmarks, narrowing the gap with leading frontier models. It ranks highly on evaluations such as Vending Bench 2, demonstrating strong long-term planning and operational capabilities. The model is open-sourced under the MIT License.Starting Price: Free -
35
Olmo 2
Ai2
Olmo 2 is a family of fully open language models developed by the Allen Institute for AI (AI2), designed to provide researchers and developers with transparent access to training data, open-source code, reproducible training recipes, and comprehensive evaluations. These models are trained on up to 5 trillion tokens and are competitive with leading open-weight models like Llama 3.1 on English academic benchmarks. Olmo 2 emphasizes training stability, implementing techniques to prevent loss spikes during long training runs, and utilizes staged training interventions during late pretraining to address capability deficiencies. The models incorporate state-of-the-art post-training methodologies from AI2's Tülu 3, resulting in the creation of Olmo 2-Instruct models. An actionable evaluation framework, the Open Language Modeling Evaluation System (OLMES), was established to guide improvements through development stages, consisting of 20 evaluation benchmarks assessing core capabilities. -
36
Qwen3.5-Plus
Alibaba
Qwen3.5-Plus is a high-performance native vision-language model designed for efficient text generation, deep reasoning, and multimodal understanding. Built on a hybrid architecture that combines linear attention with a sparse mixture-of-experts design, it delivers strong performance while optimizing inference efficiency. The model supports text, image, and video inputs and produces text outputs, making it suitable for complex multimodal workflows. With a massive 1 million token context window and up to 64K output tokens, Qwen3.5-Plus enables long-form reasoning and large-scale document analysis. It includes advanced capabilities such as structured outputs, function calling, web search, and tool integration via the Responses API. The model supports prefix continuation, caching, batch processing, and fine-tuning for flexible deployment. Designed for developers and enterprises, Qwen3.5-Plus provides scalable, high-throughput AI performance with OpenAI-compatible API access.Starting Price: $0.4 per 1M tokens -
37
Ludwig
Uber AI
Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Build custom models with ease: a declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Optimized for scale and efficiency: automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. Expert level control: retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations. Modular and extensible: experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning. -
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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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39
Llama 2
Meta
The next generation of our open source large language model. This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters. Llama 2 pretrained models are trained on 2 trillion tokens, and have double the context length than Llama 1. Its fine-tuned models have been trained on over 1 million human annotations. Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests. Llama 2 was pretrained on publicly available online data sources. The fine-tuned model, Llama-2-chat, leverages publicly available instruction datasets and over 1 million human annotations. We have a broad range of supporters around the world who believe in our open approach to today’s AI — companies that have given early feedback and are excited to build with Llama 2.Starting Price: Free -
40
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 -
41
Airtrain
Airtrain
Query and compare a large selection of open-source and proprietary models at once. Replace costly APIs with cheap custom AI models. Customize foundational models on your private data to adapt them to your particular use case. Small fine-tuned models can perform on par with GPT-4 and are up to 90% cheaper. Airtrain’s LLM-assisted scoring simplifies model grading using your task descriptions. Serve your custom models from the Airtrain API in the cloud or within your secure infrastructure. Evaluate and compare open-source and proprietary models across your entire dataset with custom properties. Airtrain’s powerful AI evaluators let you score models along arbitrary properties for a fully customized evaluation. Find out what model generates outputs compliant with the JSON schema required by your agents and applications. Your dataset gets scored across models with standalone metrics such as length, compression, coverage.Starting Price: Free -
42
Phi-4-mini-reasoning
Microsoft
Phi-4-mini-reasoning is a 3.8-billion parameter transformer-based language model optimized for mathematical reasoning and step-by-step problem solving in environments with constrained computing or latency. Fine-tuned with synthetic data generated by the DeepSeek-R1 model, it balances efficiency with advanced reasoning ability. Trained on over one million diverse math problems spanning multiple levels of difficulty from middle school to Ph.D. level, Phi-4-mini-reasoning outperforms its base model on long sentence generation across various evaluations and surpasses larger models like OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1. It features a 128K-token context window and supports function calling, enabling integration with external tools and APIs. Phi-4-mini-reasoning can be quantized using Microsoft Olive or Apple MLX Framework for deployment on edge devices such as IoT, laptops, and mobile devices. -
43
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 -
44
Mistral 7B
Mistral AI
Mistral 7B is a 7.3-billion-parameter language model that outperforms larger models like Llama 2 13B across various benchmarks. It employs Grouped-Query Attention (GQA) for faster inference and Sliding Window Attention (SWA) to efficiently handle longer sequences. Released under the Apache 2.0 license, Mistral 7B is accessible for deployment across diverse platforms, including local environments and major cloud services. Additionally, a fine-tuned version, Mistral 7B Instruct, demonstrates enhanced performance in instruction-following tasks, surpassing models like Llama 2 13B Chat.Starting Price: Free -
45
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 -
46
Tencent Cloud TI Platform
Tencent
Tencent Cloud TI Platform is a one-stop machine learning service platform designed for AI engineers. It empowers AI development throughout the entire process from data preprocessing to model building, model training, model evaluation, and model service. Preconfigured with diverse algorithm components, it supports multiple algorithm frameworks to adapt to different AI use cases. Tencent Cloud TI Platform delivers a one-stop machine learning experience that covers a complete and closed-loop workflow from data preprocessing to model building, model training, and model evaluation. With Tencent Cloud TI Platform, even AI beginners can have their models constructed automatically, making it much easier to complete the entire training process. Tencent Cloud TI Platform's auto-tuning tool can also further enhance the efficiency of parameter tuning. Tencent Cloud TI Platform allows CPU/GPU resources to elastically respond to different computing power needs with flexible billing modes. -
47
AI Fiesta
AI Fiesta
AI Fiesta is a unified AI workspace that brings together the world's leading large language models under a single roof. With one subscription, users unlock access to ChatGPT, Google Gemini, Anthropic Claude, Perplexity AI, DeepSeek, Grok, Kimi, Qwen, Llama, Seedream, and 25+ more models. Features include Super Fiesta Mode (auto model selection), side-by-side model comparison, Consensus Feature (synthesized multi-model answers), AI Avatars, Deep Research, Image Studio, Document Generation, Promptbook, Projects, and a Community. At $12/month, AI Fiesta is the most cost-effective way to access the world's best AI with no API keys required.Starting Price: $12/month/user -
48
Snorkel AI
Snorkel AI
AI today is blocked by lack of labeled data, not models. Unblock AI with the first data-centric AI development platform powered by a programmatic approach. Snorkel AI is leading the shift from model-centric to data-centric AI development with its unique programmatic approach. Save time and costs by replacing manual labeling with rapid, programmatic labeling. Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets. Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data. Version and audit data like code, leading to more responsive and ethical deployments. Incorporate subject matter experts' knowledge by collaborating around a common interface, the data needed to train models. Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators. -
49
Athina AI
Athina AI
Athina is a collaborative AI development platform that enables teams to build, test, and monitor AI applications efficiently. It offers features such as prompt management, evaluation tools, dataset handling, and observability, all designed to streamline the development of reliable AI systems. Athina supports integration with various models and services, including custom models, and ensures data privacy through fine-grained access controls and self-hosted deployment options. The platform is SOC-2 Type 2 compliant, providing a secure environment for AI development. Athina's user-friendly interface allows both technical and non-technical team members to collaborate effectively, accelerating the deployment of AI features.Starting Price: Free -
50
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.
