DeepInfra

DeepInfra's September 29, 2026 release quotes LiveKit's inference lead stating that DeepInfra helped LiveKit scale to many billions of tokens per day. The statement describes requirements of time-to-first-token below 500ms and more than 100 tokens per second. This named customer's production workload is distinct from the existing company-wide token-growth observation.

DeepInfra's May 2026 announcement reports that processed token volume grew 25 times since its Series A. This disclosed workload growth is distinct from the historical broad open-model catalog and developer API availability observation.

DeepInfra provides production inference for a broad catalog of open models through developer APIs.

DeepInfra documents deploying Hugging Face LoRA adapters on supported hosted base models and serving them through its OpenAI-compatible API under a deployment-specific model name. This concrete adapter-serving architecture is distinct from recorded prompt KV caching; documentation establishes capability rather than independent performance superiority.

DeepInfra documents automatic prompt caching on supported models that reuses key-value cache entries when requests share an identical initial prefix, avoiding recomputation of cached input. This concrete serving mechanism is independent of the historical generic optimized-inference infrastructure description.

DeepInfra develops optimized model-serving and inference infrastructure across multiple model families.

DeepInfra identifies its founding team and reports prior responsibility for imo messenger backend infrastructure, then describes applying that experience to its vertically operated GPU-to-API inference stack.

DeepInfra announced on April 22, 2025 that it had raised an $18 million Series A led by Felicis and Georges Harik.

DeepInfra operates a multi-model inference platform with developer-facing reliability and infrastructure operations.

DeepInfra documents custom LLM deployment with dedicated GPU allocation, configurable batching, minimum and maximum instances, scale-to-zero and OpenAI-compatible endpoints. This concrete deployment configuration supports inferred portable model-serving skills.

Model serving, GPU inference and API infrastructure skills transfer broadly across AI infrastructure employers.

Model serving, GPU inference and API infrastructure skills transfer broadly across AI infrastructure employers.