Alternatives to oMLX
Compare oMLX alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to oMLX in 2026. Compare features, ratings, user reviews, pricing, and more from oMLX 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
OpenRouter
OpenRouter
OpenRouter is an AI model routing platform that gives developers access to hundreds of models through a single unified API. It connects users with models from providers such as OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, Qwen, xAI, and many others. The platform supports text, image, video, and audio generation while allowing developers to use one API key and a consistent interface across providers. OpenRouter can route requests based on price, performance, and availability, with fallback options that help maintain service when a provider experiences downtime. It also offers configurable data policies so organizations can control which providers receive prompts and how requests are handled. Developers can purchase credits, choose from more than 500 active models across over 80 providers, and integrate OpenRouter using an OpenAI-compatible API.Starting Price: Free -
3
Photon
Moondream
Photon is Moondream’s official high-performance inference engine, designed to run vision-language models efficiently across cloud, desktop, and edge environments while delivering real-time performance for production AI systems. It is built as a custom inference layer tightly integrated with the Moondream model architecture, using optimized scheduling, native image processing, and purpose-built CUDA kernels to maximize speed and efficiency. This co-designed approach allows Photon to significantly reduce latency compared to traditional VLM setups, enabling responsive interactions on edge devices and real-time throughput on server-grade hardware. It supports deployment across a wide range of NVIDIA GPUs, from embedded systems like Jetson devices to high-end multi-GPU servers, making it adaptable for diverse operational needs. It includes production-ready features such as automatic batching, prefix caching, and memory-efficient attention mechanisms.Starting Price: $300 per month -
4
Run BiOS
UltraSafe AI Inc.
Run BiOS is serverless, OpenAI-compatible inference. Point the OpenAI SDK at the Run BiOS endpoint and keep your code. Six model families — Claude, DeepSeek, GLM, Kimi, MiniMax and Qwen — plus bios-adaptive, which routes each request for quality, speed and budget against a published price ceiling. Prompts and responses live in memory and are discarded when the request completes: no request logs, no content store, no archive. Fine-tuning and dedicated GPU endpoints run from the same account if you later want weights you own, billed per second of GPU time. Pricing is usage-based from a pre-paid balance, published per million tokens, and an endpoint pauses rather than running up a debt if the balance reaches zero. Start with $10 in credit, no card required. -
5
Macyou
Macyou LLC
Macyou rents dedicated Apple Silicon Macs for AI workloads. Users configure a Mac (M4 Mac mini to Mac Studio M3 Ultra with 256 GB unified memory), pick a pre-configured stack — local LLMs via Ollama (Llama, Qwen, Mistral, DeepSeek), agent frameworks (CrewAI, LangGraph), or ML dev environments (MLX, Jupyter) — and get a running deployment in about 5 minutes. Every deployment exposes an OpenAI-compatible API, so existing OpenAI SDK code works by changing base_url; access also includes SSH with root and a browser-based remote desktop. Each customer gets a dedicated physical machine with full-disk encryption and a disk wipe between tenants, hosted in a GDPR-friendly jurisdiction. Pricing is a fixed monthly fee per machine with no per-token charges; Thunderbolt 5 clustering pools unified memory across nodes for larger models. Published, measured inference benchmarks (raw JSON, CC BY 4.0) show real tokens per second per chip.Starting Price: $79/month -
6
BaseRT
Base Compute
BaseRT is a high-performance LLM inference runtime for Apple Silicon that lets developers pull models from Hugging Face, chat with them locally, or serve an OpenAI-compatible API from one CLI. Accelerated by hand-written Metal kernels, it is designed to deliver fast prefill and decode performance on M-series Macs, with published benchmarks showing up to 6.4× faster prefill than llama.cpp, 3.9× faster than MLX, and up to 1.33× faster decode. The basert CLI handles model downloading, conversion, interactive chat, serving, completion, benchmarking, inspection, and bundle signing. Its server supports chat, completions, embeddings, transcription, tool calls, continuous batching, paged KV caching, and prefix caching, while supported models can process text, vision, and audio. BaseRT uses its own .base model format with Q2–Q8 affine quantization, optional AWQ calibration, and signed bundles, and can convert GGUF, Hugging Face, and MLX checkpoints. -
7
WebLLM
WebLLM
WebLLM is a high-performance, in-browser language model inference engine that leverages WebGPU for hardware acceleration, enabling powerful LLM operations directly within web browsers without server-side processing. It offers full OpenAI API compatibility, allowing seamless integration with functionalities such as JSON mode, function-calling, and streaming. WebLLM natively supports a range of models, including Llama, Phi, Gemma, RedPajama, Mistral, and Qwen, making it versatile for various AI tasks. Users can easily integrate and deploy custom models in MLC format, adapting WebLLM to specific needs and scenarios. The platform facilitates plug-and-play integration through package managers like NPM and Yarn, or directly via CDN, complemented by comprehensive examples and a modular design for connecting with UI components. It supports streaming chat completions for real-time output generation, enhancing interactive applications like chatbots and virtual assistants.Starting Price: Free -
8
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 -
9
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 -
10
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 -
11
Cheaper Inference
Keak
Cheaper Inference is an OpenAI-compatible API gateway that provides access to AI models from multiple providers through a single API key, without requiring users to change their request format. Developers can switch by replacing the provider base URL and API key while keeping the same model, messages, tools, streaming settings, and response handling. It supports text and image models, vision-capable chat requests, streaming, prompt caching, reasoning controls, and temporary image uploads for larger vision payloads. Models are selected per request, and the catalog can be filtered by type, vision, reasoning, streaming, or provider. Automatic retries handle network and provider failures, while eligible fallback routes can be tried before a request fails. Every request is visible in History, giving teams a record of request volume, token usage, and operational activity.Starting Price: $0.48 per output -
12
Tensormesh
Tensormesh
Tensormesh is a caching layer built specifically for large-language-model inference workloads that enables organizations to reuse intermediate computations, drastically reduce GPU usage, and accelerate time-to-first-token and latency. It works by capturing and reusing key-value cache states that are normally thrown away after each inference, thereby cutting redundant compute and delivering “up to 10x faster inference” while substantially lowering GPU load. It supports deployments in public cloud or on-premises, with full observability and enterprise-grade control, SDKs/APIs, and dashboards for integration into existing inference pipelines, and compatibility with inference engines such as vLLM out of the box. Tensormesh emphasizes performance at scale, including sub-millisecond repeated queries, while optimizing every layer of inference from caching through computation. -
13
ClinePass
Cline
ClinePass is a subscription for open weight models in Cline, built to give developers generous quotas and reliable access to capable coding models without managing separate provider setup or API keys. It is designed for Cline IDE and CLI. The agent harness is built for open-weight model workflows, so developers can go from signup to coding in minutes; create an account, install Cline, select the ClinePass provider, and start coding. ClinePass includes open weight models from Z.ai, Moonshot AI, DeepSeek, MiniMax, MiMo, and Qwen, including GLM 5.2 for deep reasoning, Kimi K2.7 Code for coding tasks, Kimi K2.6 for agentic workflows, DeepSeek V4 Pro for large changes, DeepSeek V4 Flash for fast iteration, MiniMax M3 for general coding, MiMo V2.5 Pro for pro workloads, MiMo V2.5 for efficient edits, Qwen3.7-Max for heavy workloads, and Qwen3.7-Plus for balanced coding.Starting Price: $4.99 per month -
14
PromptUnit
PromptUnit
PromptUnit is an AI inference proxy that reduces AI costs automatically by sitting between an app and its AI providers with no code changes required. Teams swap the base URL, keep the same SDK, endpoints, response parsing, and error handling, then PromptUnit handles routing, failover, cost tracking, and quality validation. It logs every API call by model, feature, user segment, token count, latency, and cost, giving real-time visibility into where AI spend is going before any routing changes go live. In observation mode, PromptUnit watches traffic, shadow-classifies requests, forecasts savings, and explains routing decisions so teams can see exact savings before enabling live routing. Once enabled, Smart Routing uses task classification to route each request to the cheapest model that clears the configured quality bar. PromptUnit also includes prompt compression, token inflation defense, prompt efficiency scoring, semantic request caching, and multi-model consensus. -
15
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. -
16
Oxlo.ai
Oxlo.ai
Oxlo.ai is a privacy-first inference stack for agents, built to run frontier-class open-source models with unlimited agentic tool calls, secure failover, and zero data retention or training. It gives developers request-based access to curated open models through a unified HTTP API designed for predictable usage, low-latency inference, and clean integration into production systems. Teams can call models through OpenAI-compatible endpoints, switch from another provider by changing the base URL and API key, and keep support for streaming, function calling, JSON mode, vision models, embeddings, and image generation. Oxlo.ai supports more than 40 models across text, chat, reasoning, coding, image generation, audio, embeddings, computer vision, vision-language, speech-to-text, text-to-speech, long-context, and detection workflows.Starting Price: $80 per month -
17
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 -
18
NativeMind
NativeMind
NativeMind is an open source, on-device AI assistant that runs entirely in your browser via Ollama integration, ensuring absolute privacy by never sending data to the cloud. Everything, from model inference to prompt processing, occurs locally, so there’s no syncing, logging, or data leakage. Users can load and switch between powerful open models such as DeepSeek, Qwen, Llama, Gemma, and Mistral instantly, without additional setup, and leverage native browser features for streamlined workflows. NativeMind offers clean, concise webpage summarization; persistent, context-aware chat across multiple tabs; local web search that retrieves and answers queries directly within the page; and immersive, format-preserving translation of entire pages. Built for speed and security, the extension is fully auditable and community-backed, delivering enterprise-grade performance for real-world use cases without vendor lock-in or hidden telemetry.Starting Price: Free -
19
Featherless
Featherless
Featherless is an AI model provider that offers our subscribers access to a continually expanding library of Hugging Face models. With hundreds of new models daily, you need dedicated tools to keep up with the hype. No matter your use case, find and use the state-of-the-art AI model with Featherless. At present, we support LLaMA-3-based models, including LLaMA-3 and QWEN-2. Note that QWEN-2 models are only supported up to 16,000 context length. We plan to add more architectures to our supported list soon. We continuously onboard new models as they become available on Hugging Face. As we grow, we aim to automate this process to encompass all publicly available Hugging Face models with compatible architecture. To ensure fair individual account use, concurrent requests are limited according to the plan you've selected. Output is delivered at a speed of 10-40 tokens per second, depending on the model and prompt size.Starting Price: $10 per month -
20
vLLM
vLLM
vLLM is a high-performance library designed to facilitate efficient inference and serving of Large Language Models (LLMs). Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. It offers state-of-the-art serving throughput by efficiently managing attention key and value memory through its PagedAttention mechanism. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, including integration with FlashAttention and FlashInfer, to enhance model execution speed. Additionally, vLLM provides quantization support for GPTQ, AWQ, INT4, INT8, and FP8, as well as speculative decoding capabilities. Users benefit from seamless integration with popular Hugging Face models, support for various decoding algorithms such as parallel sampling and beam search, and compatibility with NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs, and more. -
21
Canopy Wave
Canopy Wave
Canopy Wave is the best inference platform for open models, built to deliver high-quality, reliable, and secure AI services from infrastructure to build, tune, and scale AI models. Its model platform gives users instant access to advanced open source models optimized for quality, speed, and security through API, with a model library covering different types and fields, so users can call models directly without additional development or adaptation. Canopy Wave’s serverless inference service lets teams run pretrained models through simple API calls without managing infrastructure, with fast response, low latency, no cold start issues, and globally optimized performance powered by next-generation GPUs and edge caching. For production workloads that need stronger control, dedicated endpoints run inference at scale with exceptional speed and reliability on hardware instances dedicated exclusively to the user.Starting Price: $0.07 per GB per month -
22
Mistral Small 3.1
Mistral
Mistral Small 3.1 is a state-of-the-art, multimodal, and multilingual AI model released under the Apache 2.0 license. Building upon Mistral Small 3, this enhanced version offers improved text performance, and advanced multimodal understanding, and supports an expanded context window of up to 128,000 tokens. It outperforms comparable models like Gemma 3 and GPT-4o Mini, delivering inference speeds of 150 tokens per second. Designed for versatility, Mistral Small 3.1 excels in tasks such as instruction following, conversational assistance, image understanding, and function calling, making it suitable for both enterprise and consumer-grade AI applications. Its lightweight architecture allows it to run efficiently on a single RTX 4090 or a Mac with 32GB RAM, facilitating on-device deployments. It is available for download on Hugging Face, accessible via Mistral AI's developer playground, and integrated into platforms likeGemini Enterprise Agent Platform, with availability on NVIDIA NIM.Starting Price: Free -
23
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 -
24
Apache Traffic Server
Apache Software Foundation
Apache Traffic Server™ software is a fast, scalable and extensible HTTP/1.1 and HTTP/2 compliant caching proxy server. Formerly a commercial product, Yahoo! donated it to the Apache Foundation, and currently used by several major CDNs and content owners. Improve your response time, while reducing server load and bandwidth needs by caching and reusing frequently-requested web pages, images, and web service calls. Scales well on modern SMP hardware, handling 10s of thousands of requests per second. Easily add keep-alive, filter or anonymize content requests, or add load balancing by adding a proxy layer. APIs to write your own plug-ins to do anything from modifying HTTP headers to handling ESI requests to writing your own cache algorithm. Handling over 400TB a day at Yahoo! both as forward and reverse proxies, Apache Traffic Server is battle hardened. -
25
MaxClaw
MiniMax
MaxClaw is a managed AI agent deployment environment created by MiniMax that allows users to launch autonomous AI agents instantly without needing to configure servers, infrastructure, or maintenance. It is designed to simplify the process of building and running intelligent agents by providing an always-on environment where agents can execute tasks, interact with tools, and respond to requests continuously. MaxClaw integrates with the broader MiniMax Agent ecosystem, which uses advanced AI models capable of multi-step planning, reasoning, and task execution across complex workflows. Instead of manually deploying agent frameworks or maintaining cloud infrastructure, users can deploy an operational AI agent within seconds, allowing the system to handle tasks such as automation, research, content generation, coding, or data analysis. -
26
Solar Mini
Upstage AI
Solar Mini is a pre‑trained large language model that delivers GPT‑3.5‑comparable responses with 2.5× faster inference while staying under 30 billion parameters. It achieved first place on the Hugging Face Open LLM Leaderboard in December 2023 by combining a 32‑layer Llama 2 architecture, initialized with high‑quality Mistral 7B weights, with an innovative “depth up‑scaling” (DUS) approach that deepens the model efficiently without adding complex modules. After DUS, continued pretraining restores and enhances performance, and instruction tuning in a QA format, especially for Korean, refines its ability to follow user prompts, while alignment tuning ensures its outputs meet human or advanced AI preferences. Solar Mini outperforms competitors such as Llama 2, Mistral 7B, Ko‑Alpaca, and KULLM across a variety of benchmarks, proving that compact size need not sacrifice capability.Starting Price: $0.1 per 1M tokens -
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Spanlens
Spanlens
Spanlens is an open-source (MIT) LLM observability platform that lets developers monitor every call their application makes to OpenAI, Anthropic, Gemini, Mistral, OpenRouter, Azure OpenAI, or a local Ollama model. Integration takes one line: swap your client's baseURL to the Spanlens proxy, or run "npx @spanlens/cli init" and the wizard rewrites your code automatically. From that moment, every request is recorded with its model, token counts, latency, cost, and full prompt and response body, with streaming responses reconstructed automatically. The dashboard turns that raw log into operational insight. Cost tracking breaks spend down per request, per model, and per end user, and parses prompt-cache tokens separately so you see real cache savings rather than sticker price. Agent tracing visualizes multi-step workflows as Gantt waterfalls and node-and-edge graphs, highlighting the critical path so you can find the slowest dependency chain in a fan-out. -
28
LTM-2-mini
Magic AI
LTM-2-mini is a 100M token context model: LTM-2-mini. 100M tokens equals ~10 million lines of code or ~750 novels. For each decoded token, LTM-2-mini’s sequence-dimension algorithm is roughly 1000x cheaper than the attention mechanism in Llama 3.1 405B1 for a 100M token context window. The contrast in memory requirements is even larger – running Llama 3.1 405B with a 100M token context requires 638 H100s per user just to store a single 100M token KV cache.2 In contrast, LTM requires a small fraction of a single H100’s HBM per user for the same context. -
29
DeePhi Quantization Tool
DeePhi Quantization Tool
This is a model quantization tool for convolution neural networks(CNN). This tool could quantize both weights/biases and activations from 32-bit floating-point (FP32) format to 8-bit integer(INT8) format or any other bit depths. With this tool, you can boost the inference performance and efficiency significantly, while maintaining the accuracy. This tool supports common layer types in neural networks, including convolution, pooling, fully-connected, batch normalization and so on. The quantization tool does not need the retraining of the network or labeled datasets, only one batch of pictures are needed. The process time ranges from a few seconds to several minutes depending on the size of neural network, which makes rapid model update possible. This tool is collaborative optimized for DeePhi DPU and could generate INT8 format model files required by DNNC.Starting Price: $0.90 per hour -
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Yandex Cloud CDN
Yandex
Decrease load times for your services' media content by leveraging content caching on geographically distributed CDN servers. CDN servers located all over the world get content from your origins, cache, and deliver it to end users upon request. Group servers hosting the same content to distribute your load more efficiently and deliver content even faster. Set up content caching for CDN servers and browsers, set the lifetime for file copies, and preload large files before they are even requested. In Yandex Cloud CDN, you can analyze metrics of downloaded and sent traffic and the number of requests and errors over the past 30 days. -
31
Cloudflare AI Gateway
Cloudflare
Cloudflare AI Gateway is an intelligent control plane for AI applications, built to connect to any model, dynamically route requests, and manage usage, billing, and logs from one unified gateway. It gives teams visibility and control over AI apps by connecting applications to AI Gateway, gathering insights on how people are using the application through analytics and logging, and controlling how the application scales with caching, rate limiting, request retries, model fallback, and more. AI Gateway helps reduce cost and latency by caching responses and reducing redundant API calls, so frequent requests can be served directly from Cloudflare’s cache instead of the original model provider. It improves reliability with dynamic controls that configure how and when model provider APIs are called based on attributes, fallbacks, latency, cost, or availability, with routing rules that can be adjusted from the dashboard or API without redeployments or downtime.Starting Price: $20 per month -
32
ClaimHit
ClaimHit
ClaimHit is an AI-powered agentic platform for patent infringement intelligence, designed for patent owners, IP attorneys, and corporate IP teams to perform initial infringement screenings in about 60 seconds. Its key innovation is the multi-model consensus engine. Instead of depending on a single AI model, ClaimHit runs nine advanced AI models concurrently - Claude Sonnet, Haiku (Anthropic), GPT-4o, GPT-4o Mini (OpenAI), Gemini 2.5 Flash Lite (Google), DeepSeek, Mistral, and Perplexity Sonar - each working independently without knowing the others' outputs. The results are then combined using a unique four-factor scoring system. The platform is versatile across all domains, supports team collaboration, offers a purely consumption-based pricing model, and provides MCP server integration. You can order either AI Charts, which takes about 90 seconds to generate, or request manually vetted charts right from the platform.Starting Price: $199/scan -
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SquareFactory
SquareFactory
End-to-end project, model and hosting management platform, which allows companies to convert data and algorithms into holistic, execution-ready AI-strategies. Build, train and manage models securely with ease. Create products that consume AI models from anywhere, any time. Minimize risks of AI investments, while increasing strategic flexibility. Completely automated model testing, evaluation deployment, scaling and hardware load balancing. From real-time, low-latency, high-throughput inference to batch, long-running inference. Pay-per-second-of-use model, with an SLA, and full governance, monitoring and auditing tools. Intuitive interface that acts as a unified hub for managing projects, creating and visualizing datasets, and training models via collaborative and reproducible workflows. -
34
Router
Ramp
Router is an LLM gateway built to reduce inference costs by matching each request to the lowest-cost model that still meets performance needs. It provides one endpoint and one API key for accessing multiple closed and open-source AI models from providers such as OpenAI, Anthropic, Grok, Fireworks, and others, helping developers avoid wiring applications to providers one at a time. Requests go through Router first, where usage, model, provider, and cost can be tracked before eligible workloads are routed to a more efficient option when quality will not be affected. Router Strategies let developers define cost and performance priorities for different types of requests or use benchmarked defaults based on real production workloads. It responds to live latency, availability, failures, and rate limits, and eligible requests can be moved to another available model when a provider cannot serve them. -
35
Locally AI
Locally AI
Locally AI is an on-device AI application that allows users to run powerful language models directly on their iPhone, iPad, or Mac without relying on cloud infrastructure or an internet connection. Built on Apple’s MLX framework, it delivers fast, efficient performance while minimizing power usage, enabling a seamless experience for chatting, creating, learning, and exploring AI capabilities across devices. It supports multiple open models such as Llama, Gemma, Qwen, and DeepSeek, allowing users to switch between them and tailor outputs to different tasks. Everything runs entirely offline, meaning no login is required, and no data is collected or transmitted, ensuring complete privacy and control over personal information. Users can interact with AI through natural conversations, analyze documents or images, and generate text in a unified interface designed for simplicity and responsiveness.Starting Price: Free -
36
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 -
37
FriendliAI
FriendliAI
FriendliAI is a generative AI infrastructure platform that offers fast, efficient, and reliable inference solutions for production environments. It provides a suite of tools and services designed to optimize the deployment and serving of large language models (LLMs) and other generative AI workloads at scale. Key offerings include Friendli Endpoints, which allow users to build and serve custom generative AI models, saving GPU costs and accelerating AI inference. It supports seamless integration with popular open source models from the Hugging Face Hub, enabling lightning-fast, high-performance inference. FriendliAI's cutting-edge technologies, such as Iteration Batching, Friendli DNN Library, Friendli TCache, and Native Quantization, contribute to significant cost savings (50–90%), reduced GPU requirements (6× fewer GPUs), higher throughput (10.7×), and lower latency (6.2×).Starting Price: $5.9 per hour -
38
01.AI
01.AI
The 01.AI Super Employee platform transforms enterprise operations with AI agents capable of deep reasoning, task planning, and end-to-end execution. Through its centralized Solution Console, organizations can manage knowledge bases, train custom models, and deploy business-ready AI solutions with ease. Built for enterprise security, it supports on-premise deployment, secure sandboxing, and MCP connectivity for controlled access to legacy systems and external tools. 01.AI offers a comprehensive suite of industry-specific agents—from sales and insurance to supply chain, finance, and government—each designed to automate workflows across browsers, terminals, cloud phones, and interpreters. With native support for leading LLMs like DeepSeek, Qwen, and Yi, businesses gain a flexible and future-ready AI stack. The platform accelerates AI adoption by enabling rapid deployment, continuous evolution, and seamless integration across enterprise environments. -
39
NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.Starting Price: Free
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40
Cachify
Cachify
Cachify optimizes your page loads by caching posts, pages and custom post types as static content. You can choose between caching via database, on the web server’s hard drive (HDD), or—thanks to APC (Alternative PHP Cache)—directly in the web server’s system cache. Whenever a page or post is loaded, it can be pulled directly from the cache. The amount of database queries and PHP requests will dramatically decrease towards zero, depending on the caching method you chose. With an increasing number of dynamic widgets, templates and plugins, a WordPress blog tends to become sluggish. With the load of the visitors ramp accumulates the accesses to the database, the server has to deal with the processing of flexible areas proportionally more. A delay in delivery of the web pages is the result of the load. Cachify has been developed especially for small to medium sized projects: smart, clear cache plugin, which temporarily stores page contents in a static form and delivers performance-gently.Starting Price: Free -
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Mirai
Mirai
Mirai is a developer-focused on-device AI infrastructure platform designed to convert, optimize, and run machine learning models directly on Apple devices with high performance and privacy. It provides a unified pipeline that enables teams to convert and quantize models, benchmark them, distribute them, and execute inference locally. It is built specifically for Apple Silicon and aims to deliver near-zero latency, zero inference cost, and full data privacy by keeping sensitive processing on the user’s device. Through its SDK and inference engine, developers can integrate AI features into applications quickly, using hardware-aware optimizations that unlock the full power of the GPU and Neural Engine. Mirai also includes dynamic routing capabilities that automatically decide whether a request should run locally or in the cloud based on latency, privacy, or workload requirements. -
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Squid
Squid
Squid is a caching proxy for the Web supporting HTTP, HTTPS, FTP, and more. It reduces bandwidth and improves response times by caching and reusing frequently-requested web pages. Squid has extensive access controls and makes a great server accelerator. It runs on most available operating systems, including Windows, and is licensed under the GNU GPL. Squid is used by hundreds of Internet Providers worldwide to provide their users with the best possible web access. Squid optimizes the data flow between client and server to improve performance and caches frequently-used content to save bandwidth. Squid can also route content requests to servers in a wide variety of ways to build cache server hierarchies that optimize network throughput. Thousands of websites around the Internet use Squid to drastically increase their content delivery. Squid can reduce your server load and improve delivery speeds to clients. -
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Lucebox
Lucebox
Lucebox is a plug-and-play computer built for running local AI models and agents at full speed. Inside the custom chassis, a Ryzen AI MAX+ 395 with 128GB of unified LPDDR5X memory is paired with an RTX 3090, and the two work together through an open-source inference engine hand-tuned for exactly this hardware. The architecture is what makes it fast. Large models live in the 128GB unified memory tier, while the 3090's high-bandwidth VRAM acts as a fast tier. Speculative decoding (DFlash) and speculative prefill (PFlash) bridge the two, producing inference speeds up to 10x higher than llama.cpp on the same silicon and beating machines like the Mac Studio and DGX Spark at a fraction of their effective cost.Starting Price: $4,900 - One time payment -
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E2B
E2B
E2B is an open source runtime designed to securely execute AI-generated code within isolated cloud sandboxes. It enables developers to integrate code interpretation capabilities into their AI applications and agents, facilitating the execution of dynamic code snippets in a controlled environment. The platform supports multiple programming languages, including Python and JavaScript, and offers SDKs for seamless integration. E2B utilizes Firecracker microVMs to ensure robust security and isolation for code execution. Developers can deploy E2B within their own infrastructure or utilize the provided cloud service. The platform is designed to be LLM-agnostic, allowing compatibility with various large language models such as OpenAI, Llama, Anthropic, and Mistral. E2B's features include rapid sandbox initialization, customizable execution environments, and support for long-running sessions up to 24 hours.Starting Price: Free -
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MonoQwen-Vision
LightOn
MonoQwen2-VL-v0.1 is the first visual document reranker designed to enhance the quality of retrieved visual documents in Retrieval-Augmented Generation (RAG) pipelines. Traditional RAG approaches rely on converting documents into text using Optical Character Recognition (OCR), which can be time-consuming and may result in loss of information, especially for non-textual elements like graphs and tables. MonoQwen2-VL-v0.1 addresses these limitations by leveraging Visual Language Models (VLMs) that process images directly, eliminating the need for OCR and preserving the integrity of visual content. This reranker operates in a two-stage pipeline, initially, it uses separate encoding to generate a pool of candidate documents, followed by a cross-encoding model that reranks these candidates based on their relevance to the query. By training a Low-Rank Adaptation (LoRA) on top of the Qwen2-VL-2B-Instruct model, MonoQwen2-VL-v0.1 achieves high performance without significant memory overhead. -
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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. -
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Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case. It provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. From low latency (a few milliseconds) and high throughput (hundreds of thousands of requests per second) to long-running inference for use cases such as natural language processing and computer vision, you can use Amazon SageMaker for all your inference needs.
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Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. Features are used repeatedly by multiple teams and feature quality is critical to ensure a highly accurate model. Also, when features used to train models offline in batch are made available for real-time inference, it’s hard to keep the two feature stores synchronized. SageMaker Feature Store provides a secured and unified store for feature use across the ML lifecycle. Store, share, and manage ML model features for training and inference to promote feature reuse across ML applications. Ingest features from any data source including streaming and batch such as application logs, service logs, clickstreams, sensors, etc.
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PaliGemma 2
Google
PaliGemma 2, the next evolution in tunable vision-language models, builds upon the performant Gemma 2 models, adding the power of vision and making it easier than ever to fine-tune for exceptional performance. With PaliGemma 2, these models can see, understand, and interact with visual input, opening up a world of new possibilities. It offers scalable performance with multiple model sizes (3B, 10B, 28B parameters) and resolutions (224px, 448px, 896px). PaliGemma 2 generates detailed, contextually relevant captions for images, going beyond simple object identification to describe actions, emotions, and the overall narrative of the scene. Our research demonstrates leading performance in chemical formula recognition, music score recognition, spatial reasoning, and chest X-ray report generation, as detailed in the technical report. Upgrading to PaliGemma 2 is a breeze for existing PaliGemma users. -
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Substrate
Substrate
Substrate is the platform for agentic AI. Elegant abstractions and high-performance components, optimized models, vector database, code interpreter, and model router. Substrate is the only compute engine designed to run multi-step AI workloads. Describe your task by connecting components and let Substrate run it as fast as possible. We analyze your workload as a directed acyclic graph and optimize the graph, for example, merging nodes that can be run in a batch. The Substrate inference engine automatically schedules your workflow graph with optimized parallelism, reducing the complexity of chaining multiple inference APIs. No more async programming, just connect nodes and let Substrate parallelize your workload. Our infrastructure guarantees your entire workload runs in the same cluster, often on the same machine. You won’t spend fractions of a second per task on unnecessary data roundtrips and cross-region HTTP transport.Starting Price: $30 per month
