AI Inference-as-a-Service market snapshot: 2035 growth forecast (22-30% CAGR)

AI Inference-as-a-Service market snapshot是這篇文章討論的核心
The global AI Inference-as-a-Service market is projected to grow rapidly through 2035, with estimates around **USD 18–85 billion in 2025** and forecasts of **USD 23–28 billion for 2026**, expanding at roughly **22–30% CAGR** over the next decade (e.g., Precedence Research: ~$23.4B in 2026; Fortune Business Insights: AI-as-a-Service ~$28.81B in 2026). Key drivers include the surge in generative AI/LLM usage, demand for real-time inference, and a shift of AI compute spend from training to serving/serving-layer margins.

Serverless inference is becoming central to this trend because it removes instance and scaling management, making it ideal for intermittent or unpredictable traffic. Major providers include:
– **AWS** – Amazon SageMaker Serverless Inference, and serverless LLM integration patterns using AWS Lambda.
– **Databricks** – Model Serving / AI Runtime for real-time and batch inference on serverless GPU compute.
– **Modal, Fireworks AI** – managed APIs / serverless hosted model catalogs cited as examples in Ramp’s AI infrastructure spending research.
– **CoreWeave** – Serverless Inference delivered via W&B Inference.
– **Cerebras** and other dedicated inference silicon vendors, with reports noting ~$8.3B flowing into dedicated inference silicon in 2026.

Notable market trends include cost declines in AI inference, FinOps discipline for agentic AI workloads, multi-vendor cost arbitrage, and inference absorbing an estimated **67% of AI compute** as the compute economy flips from training to serving.

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