Large Language Models (LLMs) have enabled diverse AI applications. However, LLMs inference impose unprecedented computational and memory overhead, creating an inherent trade-off between latency Service Level Objectives (SLOs) and resource constraints. Serverless computing, with on-demand provisioning and pay-as-you-go billing, is becoming a promising paradigm for LLM serving. But existing solutions fail to integrate state-of-the-art inference optimizations, resulting in suboptimal GPU utilization and prolonged latency. While Prefill-Decode (PD) disaggregation combined with continuous batching has resolved such inefficiencies in traditional cloud deployments, migrating these techniques to serverless makes two challenges particularly pronounced: (1) SLO-constrained resource provisioning for independently scaling prefill and decode phase functions, and (2) function lifespan management to mitigate resource waste from continuous batching-induced prolonged instance lifespans. To tackle these issues, we propose CELLServe , an SLO-aware and cost-efficient serverless LLM serving system that pioneers integrating PD disaggregation and continuous batching into serverless platforms. CELLServe formalizes SLO-constrained joint resource provisioning as an optimization problem with a dedicated algorithm, and introduces an opportunistic instance merging strategy for decode phase functions to reclaim fragmented resources. Comprehensive evaluations on five mainstream LLMs and real-world traces show that CELLServe achieves 1.85×–1.92×higher request throughput than baselines under identical SLOs and GPU budgets, while sustaining high resource efficiency under dynamic workloads.
Wang et al. (2026) studied this question.