Prime Inference launches as open AI stack accelerates

Inference is becoming the real competitive layer: model quality, agent reliability, and serving economics are converging. The caveat is that these headline gains remain workload-specific rather than universal production advantages.

• –Prime Inference combines serverless and reserved capacity with OpenAI-compatible APIs, NVIDIA Dynamo, vLLM, Mooncake, and FlashInfer.
• –GPT 6.1 Sol’s $0.57 median task cost shows agent performance is increasingly judged alongside capability, not separately from it.
• –Baseten’s VibeQwen result suggests coding agents can optimize model serving itself, but the 90% gain came from repetitive structured text on a specific GPU and model.
• –Developers should benchmark complete agent traces, including cache reuse, tool calls, prefill latency, and recovery behavior—not just tokens per second.
• –The strategic shift is from general-purpose inference engines toward specialized stacks tuned for one model, workload, and accelerator.