3 Best Open Source Together AI Alternatives
A curated collection of the 3 best open source and self-hosted alternatives to Together AI in 2026.
Looking for a free, privacy-friendly, or self-hosted replacement for Together AI in 2026? The top open-source alternative is Ollama — Run open-source LLMs locally or in the cloud.
Other highly rated open-source Together AI alternatives include llama.cpp and LocalAI.
Run open-source LLMs locally or in the cloud
Ollama is an open-source platform that simplifies running large language models (LLMs) locally or in the cloud while keeping your data private. It provides a seamless way to deploy frontier AI models with enterprise-grade performance and security. - Frontier Open Models: Access the latest open-source LLMs with performance comparable to closed alternatives at a fraction of the cost. - Local & Cloud Deployment: Run models locally for maximum privacy or deploy in the cloud across US, Europe, and Singapore for scalability. - High Performance: Dedicated infrastructure ensures reliable throughput even with multiple concurrent agents. - Easy Integration: Launch popular coding agents (Claude Code, Codex, OpenCode) with a single command and switch models without changing workflows. - Data Privacy: Prompts are never tracked or trained on by providers, ensuring complete data ownership. - Open Source: Fully open-source with open weights, allowing for transparency and customization. - Cross-Platform Support: Works seamlessly across macOS, Windows, Linux, and Docker. Ollama is open-source and can be self-hosted on any infrastructure. The platform supports Docker for easy deployment and provides comprehensive documentation for local and cloud setups. All cloud-hosted models are deployed in compliant regions (US, Europe, Singapore) to meet data residency requirements. View documentation | GitHub repository
Run frontier AI entirely on your machine
llama.cpp is an open-source framework designed to run large language models entirely on your local machine. It eliminates the need for API keys, telemetry, or external dependencies, ensuring complete privacy and control over your data. - Local AI Execution: Run frontier AI models entirely on your machine without relying on cloud services or API keys. - Privacy-First: No telemetry, no data collection, and no external dependencies—your conversations and models stay private. - Hardware Agnostic: Optimized for any hardware, from laptops to clusters, with the same binary and models across all platforms. - Hand-Tuned Kernels: Performance-optimized kernels for every GPU and CPU, including Apple Silicon, NVIDIA RTX, AMD Radeon, and Intel Arc. - Model Compatibility: Supports a wide range of models, including Qwen 3.8, Gemma 4, GPT-OSS, and Gemma 3. - Local Coding Agent Integration: Pair with local coding agents like Pi for agentic workflows without leaving your machine. - Easy Installation: Install via package managers (Brew, Winget) or build from source with detailed instructions. - No Limits: No usage restrictions, no rate limits, and no dependency on external services. llama.cpp is designed for self-hosting and can be deployed on any hardware, from consumer laptops to enterprise-grade clusters. It supports a variety of models and is optimized for performance across different architectures. The framework is open-source and can be self-hosted without any external dependencies.
Run any AI model locally on any hardware
LocalAI is an open-source AI engine designed to make AI accessible on any hardware, from laptops to GPU clusters. It provides a unified runtime for running text, voice, vision, image, video, 3D, and agent-based models locally, eliminating the need for cloud-based AI services. - Multi-Modal Support: Run text, voice, vision, images, video, 3D, and agents from a single runtime. - Hardware Agnostic: Works on CPUs, GPUs (CUDA, ROCm, Metal, Vulkan), and ARM64 without requiring specialized hardware. - OpenAI-Compatible API: Seamlessly integrates with existing clients using OpenAI, Anthropic, Ollama, or ElevenLabs APIs. - Dynamic Engine Switching: Swap between backends (llama.cpp, vLLM, MLX, etc.) with a single line in the model's config. - Realtime Capabilities: Enable real-time voice interactions, transcription, and speech synthesis over WebRTC without a GPU. - Distributed AI: Scale across multiple machines with smart routing, VRAM-aware placement, and failover support. - Privacy-First: All processing happens locally; no data leaves your machine. - 1,255+ Pre-Configured Models: One-click installation for a wide range of models. - 19 Custom Engines: Built from scratch for specific tasks like real-time voice, vision, and 3D reconstruction. - Agent & MCP Support: Run autonomous agents, RAG, and interactive tools locally. - Self-Hosted: Fully self-hostable on any hardware, including edge devices. - Open Source: MIT-licensed, available on GitHub. - Cross-Platform: Supports x8664, ARM64, and various GPU frameworks (CUDA, ROCm, Metal, Vulkan). - Scalable: From a single laptop to a distributed cluster of machines. - Documentation: Comprehensive docs for setup, configuration, and advanced use cases.
About Together AI
Together AI is a full-stack AI platform designed for developers and enterprises to build, deploy, and scale AI applications. The platform provides high-performance inference, fine-tuning, and GPU clusters, all powered by cutting-edge research and a vast library of open-source models.
Key Features of Together AI
- Serverless Inference: High-performance inference APIs for real-time applications.
- Batch Inference: Efficient inference for batch workloads and large-scale processing.
- Provisioned Throughput: Token-based capacity with SLAs for predictable performance.
- Dedicated Model Inference: Custom hardware for specialized inference needs.
- Dedicated Container Inference: Deploy and run custom models in isolated environments.
- GPU Clusters: Reliable and scalable GPU infrastructure for AI workloads.
- AI Factory: Custom infrastructure solutions for frontier-scale AI projects.
- Sandbox: Build and test development environments tailored for AI applications.
- Managed Storage: Secure storage for model weights and data.
- Custom Training: Advanced training capabilities from reinforcement learning to full control.
- Fine-Tuning: Shape models using your own data for improved performance.
- Evaluations: Measure and optimize model quality with built-in evaluation tools.
- Research & Publications: Access to cutting-edge AI research and publications.
Tech / Self-Hosting
Together AI is a cloud-native platform and does not currently offer self-hosting options. The platform is designed for cloud deployment and leverages GPU clusters and managed services for optimal performance. Developers can access the platform via APIs and SDKs for integration into their applications.