New Inference Engine by DigitalOcean Enhances AI Workload Efficiency

DigitalOcean has launched Inference Engine, a new set of production AI capabilities that helps builders run, scale, and optimize inference workloads with greater performance and control. Announced ahead of its Deploy conference, the platform includes four core offerings: Inference Router, Batch Inference, Serverless Inference, and Dedicated Inference. Together, they let development teams align workload needs with the right cost and performance profile through one unified inference platform.

According to Precedence Research, the model inference optimization tools market was valued at USD 4.20 billion in 2025 and is projected to grow from USD 5.37 billion in 2026 to approximately USD 48.82 billion by 2035, expanding at a CAGR of 27.80% from 2026 to 2035, driven by increasing AI and IoT in several industries.

A unified platform for optimized production AI workloads

DigitalOcean’s Inference Engine helps teams run and optimize production AI workloads through four core offerings. Inference Router reduces costs and latency by routing each request to the most appropriate model from a defined pool using a purpose-built MoE router model, avoiding expensive overuse of top-tier models and cutting inference costs by over 40% in customer use. Dedicated Inference provides reserved capacity for predictable, high-scale performance. Serverless Inference offers access to dozens of models through one API key, with scale-to-zero elasticity and off-peak pricing. Batch Inference lowers offline AI workload costs by 50% with asynchronous execution, retries, and 24-hour completion.

Engineered for high-throughput, low-latency AI at production scale

DigitalOcean’s Inference Engine is built on three advances: integrations with vLLM, TensorRT, and SGLang for higher token throughput; request-path and model-level optimizations for better unit economics without sacrificing quality; and distributed scaling for bursty production workloads. Artificial Analysis found it delivers 3x faster time-to-first-token and 3x higher output speed than Amazon Bedrock on DeepSeek V3.2, with stronger latency consistency and top-tier positioning versus most providers.

A recent report by Precedence Research highlights that the model inference optimization tools market is benefiting from the rising adoption of the need for cost and latency optimization.