Together AI

Together AI launched in 2022 with a specific thesis: the future of AI inference would be dominated by open-source models, and the bottleneck was not model quality but access to fast, affordable GPU infrastructure to run them. The platform offers inference endpoints for leading open-source models — Llama, Mistral, Qwen, DeepSeek, and others — along with fine-tuning capabilities and an API that is deliberately designed to be compatible with the OpenAI client library format, making migration straightforward for developers already using standard tooling.

The inference backend is built on Together's own distributed GPU cluster, optimized for throughput and low latency across models of varying sizes. The platform uses techniques like FlashAttention and continuous batching to maximize GPU utilization, enabling high-concurrency serving that keeps per-token costs low compared to proprietary API providers. Developers interact through the Together API using standard REST calls or the Python client, specifying model names from the supported catalog along with temperature, max tokens, and other sampling parameters. Together also provides a Playground for interactive testing and an API key management console.

When the platform goes down or degrades, the consequences are acute for applications built on open-source model inference. Generation requests return 5xx errors or time out, and because many Together users have migrated workloads from other providers specifically for cost efficiency, there's often no hot fallback configured. Streaming responses drop mid-completion, leaving end users with incomplete outputs. Fine-tuning jobs in progress stall without clear status updates. Developers using Together's OpenAI-compatible endpoint as a drop-in replacement find that their entire application stack breaks simultaneously, since the same client configuration covers multiple use cases. During partial degradations, some models remain available while others return capacity errors.

Outage.gg tracks Together AI's service status in real time so development teams can quickly confirm whether inference failures are platform-wide. Check the live status page before switching model configurations.