Together AI

Together AI describes itself as "the AI native cloud" — a full-stack platform spanning the entire AI development lifecycle from early experimentation through production deployment, built specifically around open-source models rather than proprietary ones. Who it's for: companies and research teams building on open models who need serious infrastructure — inference at scale, GPU compute, and fine-tuning — rather than a lightweight API wrapper. Together AI names customers including Cursor, Decagon, Cohere, ElevenLabs, Arcee, and Salesforce, spanning both AI-native startups and larger enterprises with research and commercial workloads. Strengths: the platform covers three distinct needs under one roof. Inference offers multiple deployment shapes — serverless for variable workloads, batch processing for large offline jobs, provisioned throughput for predictable capacity, and dedicated model/container inference for full isolation. Compute provides accelerated GPU clusters ranging from self-serve instant setups up to thousands of GPUs, including on-demand access to NVIDIA's B200 GPUs, plus sandboxes and managed storage. Model Shaping covers fine-tuning open-source models using current research techniques rather than treating them as fixed black boxes. Together AI cites concrete performance claims — 2x faster inference through research-optimized techniques, 60% lower costs via workload-specific optimization, and 90% faster pre-training using its own "Together Kernel Collection." Weaknesses and limitations: no specific pricing is laid out on the main marketing page beyond a general mention of token-based pricing for provisioned throughput, so serious cost evaluation requires a deeper look at the platform's own pricing documentation or a sales conversation for larger workloads. Together AI's focus on open models and heavier infrastructure also makes it a less natural fit for a small team that just wants a simple, low-commitment API call — the platform is built for teams with real infrastructure needs, not casual experimentation. Real-world scenarios: a startup that needs dedicated GPU capacity for training or fine-tuning a custom model without building its own data center, a company running high-volume inference that benefits from Together's cost and speed optimizations at scale, and research teams that need access to cutting-edge GPU hardware (like B200s) on demand rather than through a multi-month procurement cycle. Compared to a lighter-weight aggregator like OpenRouter, Together AI is positioned further up the stack — closer to genuine cloud infrastructure than a simple API router.