Together AI

High-performance cloud platform for inference and fine-tuning of open-source models.

Overview

Together AI is a leading research and cloud infrastructure company focused on making open-source artificial intelligence as capable and accessible as proprietary alternatives. By optimized software-hardware integration, Together AI provides a fast and cost-effective platform for running, fine-tuning, and scaling generative AI models. In the 2026 landscape, where open-weights models rival the performance of frontier closed systems like GPT-5.2 and Claude 4.6, Together AI serves as a critical backbone for teams seeking independence from single-vendor ecosystems.

Standout features

• Together Inference: An ultra-fast API supporting dozens of open-source models (including Llama 4 and Qwen 3 series) with full OpenAI compatibility.
• Together GPU Clusters: Instant access to high-end compute (H200 and Blackwell architectures) for large-scale training and inference without the overhead of traditional cloud providers.
• Together Fine-tuning: A streamlined, API-driven workflow for adapting models to specific domains or private datasets using techniques like LoRA and full-parameter tuning.
• Custom Kernel Optimizations: Proprietary optimizations that significantly reduce latency and increase throughput compared to stock implementations of popular models.

Typical use cases

• Production-Scale Inference: Powering high-traffic applications that require reliable, low-latency responses from the best available open-source LLMs.
• Domain-Specific Fine-tuning: Creating specialized models for industries like law, medicine, or finance where data privacy and specialized knowledge are paramount.
• Hybrid AI Architectures: Using Together AI to run open models alongside proprietary systems to optimize for cost, performance, and redundancy.

Limitations or trade-offs

• Open-Source Specificity: The platform does not host closed-source models; users needing access to GPT or Claude series must use their respective providers.
• Infrastructure Complexity: While the API is simple, managing large-scale GPU clusters or complex fine-tuning jobs requires MLOps expertise.
• Data Privacy Configurations: While secure, enterprises with extreme data residency requirements may need to opt for dedicated instances rather than the shared API.

When to choose this tool

Choose Together AI if you are building on open-source foundations and need the highest possible performance and scalability. It is particularly well-suited for organizations that want to avoid vendor lock-in, require deep control over their model behavior through fine-tuning, or need a high-throughput inference solution that matches the speed of the fastest proprietary engines in 2026.