2026-07-22 04:00 UTCOriginal source2 min readUpdated: 2026-07-22 08:20 UTC

Beyond Accuracy and Cost: Latency-Aware LLM Query Routing for Dynamic Workloads

Modern LLM query routers often ignore generation latency, focusing only on accuracy and cost. This paper introduces a lightweight latency estimator that simulates autoregressive token batch processing to predict time-to-first-token (TTFT), and integrates it into a router that jointly optimizes latency, accuracy, and cost. Experiments show up to 40% improvement in accuracy-cost utility while maintaining the same latency as standard load-balancing approaches.

SourcearXiv AIAuthor: Shivam Patel, Akaash R. Parthasarathy, Ankur Mallick, Gauri Joshi

-->

[Submitted on 13 May 2026]

Title:Beyond Accuracy and Cost: Latency-Aware LLM Query Routing for Dynamic Workloads

View a PDF of the paper titled Beyond Accuracy and Cost: Latency-Aware LLM Query Routing for Dynamic Workloads, by Shivam Patel and 3 other authors

View PDF HTML (experimental)

Abstract:Modern language query routers improve inference efficiency by assigning each query to a model that balances response quality and monetary cost. However, current query routers are largely latency-agnostic and do not consider the generation latency experienced by queries at model instances. In practice, latency is often controlled by load-balancing policies such as round-robin or join-the-shortest-queue, which do not account for model accuracy or inference cost. Incorporating query latency into routing is challenging as it depends not only on the query's prompt length, but also on the current prefill and decode workload at the model instance and the scheduling and batching policy of the serving framework. We design a lightweight latency estimator that simulates autoregressive token batch processing in the serving framework and estimates the time-to-first-token (TTFT) of queries. We incorporate this latency estimator into a latency-aware router that jointly optimizes latency, accuracy, and cost when assigning queries to model instances. Our experimental results indicate that this joint optimization yields up to 40% improvement in accuracy--cost utility while maintaining the same latencies as standard load-balancing approaches.

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.18253 [cs.AI]

(or arXiv:2607.18253v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.18253

arXiv-issued DOI via DataCite

Submission history

From: Shivam Patel [view email] [v1] Wed, 13 May 2026 20:29:09 UTC (1,247 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled Beyond Accuracy and Cost: Latency-Aware LLM Query Routing for Dynamic Workloads, by Shivam Patel and 3 other authors

View PDF

HTML (experimental)

TeX Source

view license

Current browse context:

cs.AI

new | recent | 2026-07

Change to browse by:

References & Citations

NASA ADS

Google Scholar

Semantic Scholar

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)