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Fast, low-cost AI inference provider running LLMs on custom LPU chips via GroqCloud's pay-as-you-go API.
Enterprise unified API gateway giving one integration point to 100+ LLMs like Claude, GPT, and Gemini with reliability guarantees.
Native macOS app that unifies 300+ AI models in one private workspace with agents, MCP tools, and one-time licensing.
Ultra-lightweight, self-hosted open-source AI assistant in Go that runs on sub-$10 hardware like Raspberry Pi with under 10MB RAM.
Developer-focused GPU cloud offering on-demand pods, serverless inference and multi-node clusters at per-second pricing for AI workloads.
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- ✦LPU custom inference hardware
- ✦GroqCloud tokens-as-a-service API
- ✦High-speed, low-latency inference
- ✦Pay-as-you-go token pricing
- ✦Free API key to start
- ✦Broad open-model support
- ✦Unified API for 100+ AI models
- ✦Intelligent request routing across models
- ✦AI Model Insurance for quality/reliability guarantees
- ✦Enterprise-focused LLM access layer
- ✦Switch across 300+ hosted and local AI models
- ✦Native macOS app with global shortcut and screenshot-to-answer
- ✦Reusable agents, projects, and forked chats
- ✦Multimodal analysis of PDFs, images, and code
- ✦MCP tools and code execution
- ✦Local chat storage with encryptable API keys
- ✦Single self-contained binary requiring under 10MB RAM
- ✦Sub-1-second startup even on low-power processors
- ✦Support for 16+ chat channels including Telegram, Discord, Slack, WeCom
- ✦Compatibility with multiple LLM providers (OpenAI, Claude, DeepSeek, Gemini, etc.)
- ✦Runs on Raspberry Pi, RISC-V, ARM64, x86_64, Android, and Docker
- ✦Self-hosted design keeping data and configuration local
- ✦Gateway/API mode for connecting to chat platforms via MCP protocol
- ✦On-demand GPU pods across 30+ GPU types and 31 regions
- ✦Serverless GPU endpoints with sub-200ms cold starts
- ✦Zero idle cost billing for inference workloads
- ✦Multi-node clusters for distributed training
- ✦Persistent network storage for full pipelines
- ✦Real-time logs, monitoring and autoscaling from 0 to hundreds of workers
- →Running LLM inference at high speed
- →Cutting inference costs at scale
- →Powering low-latency AI chat apps
- →Serving models via a hosted API
- →Building applications that need failover across multiple LLM providers
- →Consolidating billing/access to many AI models under one API
- →Enterprises requiring guaranteed model output reliability
- →Using multiple AI providers in one place
- →Explaining or fixing on-screen content instantly
- →Building reusable task-specific agents
- →Analyzing documents and screenshots privately
- →Running a private AI assistant on minimal or embedded hardware
- →Local code assistance that keeps proprietary code off the cloud
- →Home automation and personal task scheduling via chat bots
- →Privacy-conscious users wanting self-hosted AI on low-cost devices
- →Renting GPUs for model training and fine-tuning
- →Deploying low-latency real-time inference APIs
- →Running AI agents that need to scale instantly
- →Processing compute-heavy batch or distributed workloads