---
title: "How We Cut Kafka Consumer Deployment Costs by 83% · triva..."
url: https://daily.dev/posts/how-we-cut-kafka-consumer-deployment-costs-by-83-triva--65gowghhb
source_url: https://tech.trivago.com/post/2026-06-12-how-we-cut-kafka-consumer-deployment-costs-by-83
type: article
source: "Tech at trivago"
published: 2026-06-17T11:37:51.563Z
updated: 2026-06-17T11:38:11.544Z
tags: ["performance", "kafka", "grpc", "reactive-programming"]
reading_time: 6
upvotes: 0
comments: 0
language: en
---

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# How We Cut Kafka Consumer Deployment Costs by 83% · triva...

**[Tech at trivago](https://daily.dev/sources/trivago)** · 6 min read · 0 upvotes · 0 comments

## Summary

A backend engineer at Trivago shares how they cut Kafka consumer deployment costs by 83% and resolved 19 P1 incidents by diagnosing a multi-layered performance problem in their PSE-kafka microservice. The root cause turned out to be three compounding issues: an in-house KafkaReceiverFlux that stopped calling poll() under load, a Mono.delayElement() ceiling tied to flatMap concurrency, and an undersized gRPC thread pool. Each fix in isolation had no measurable effect; only addressing all three together — migrating to reactor-kafka, removing the in-memory delay via an IsFinished flag, and tuning the gRPC thread pool — reduced pod count from 60 to 6, eliminated consumer lag, and cut pod startup time from ~60s to ~10s.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://tech.trivago.com/post/2026-06-12-how-we-cut-kafka-consumer-deployment-costs-by-83>

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Tags: [#performance](https://daily.dev/tags/performance), [#kafka](https://daily.dev/tags/kafka), [#grpc](https://daily.dev/tags/grpc), [#reactive-programming](https://daily.dev/tags/reactive-programming)

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