# Continuous profiling at Mercado Libre: Turning flamegraphs into fixes

DevFeed: [Continuous profiling at Mercado Libre: Turning flamegraphs into fixes](<https://devfeed.tech/articles/continuous-profiling-at-mercado-libre-turning-flamegraphs-into-fixes-22550.md>)

Original publisher: [Read original article](<https://medium.com/mercadolibre-tech/continuous-profiling-at-mercado-libre-turning-flamegraphs-into-fixes-2ee371c32bfd?source=rss----5011f85401f0---4>)

Author: Elton Hoffmann

Published: 2026-03-11T01:27:33Z

Content type: article

Language: en

Sources: [Mercado Libre Tech](<https://devfeed.tech/sources/mercado-libre-tech.md>)

Topics: [observability](<https://devfeed.tech/topics/observability.md>), [Tooling](<https://devfeed.tech/topics/tooling.md>), [Microservices](<https://devfeed.tech/topics/microservices.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [article](<https://devfeed.tech/tags/article.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [end-user-experience](<https://devfeed.tech/tags/end-user-experience.md>), [flamegraph](<https://devfeed.tech/tags/flamegraph.md>), [garbage-collection](<https://devfeed.tech/tags/garbage-collection.md>), [latency](<https://devfeed.tech/tags/latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [microservices](<https://devfeed.tech/tags/microservices.md>), [observability](<https://devfeed.tech/tags/observability.md>), [performance](<https://devfeed.tech/tags/performance.md>), [performance-engineering](<https://devfeed.tech/tags/performance-engineering.md>), [platform-engineering](<https://devfeed.tech/tags/platform-engineering.md>), [profiling](<https://devfeed.tech/tags/profiling.md>), [uptime](<https://devfeed.tech/tags/uptime.md>)

## AI overview

Mercado Libre describes building a continuous profiling platform for about 35,000 microservices. The platform treats profiling as an observability signal alongside metrics, logs, and traces, with the goal of helping teams detect performance issues and automate fixes for certain issue classes.

## Source excerpt

We continue our series on Performance Engineering at Mercado Libre. In previous articles, we discussed how we crafted an observability culture across the company, how performance relates to other observability signals, and how the Performance Engineering team partners with Business Units to enhance platform efficiency. Today, we'll show how we built a continuous profiling platform for 35,000 microservices and how we started turning profiles into automated fixes. Context Our goal in the Performance Engineering team is threefold: to ensure uptime; to improve the end-user experience by reducing latency; to reduce computing costs. As a cross-functional team, we usually work with application owners to achieve these goals. In our two years of existence, our joint efforts have taken us through countless incidents and bottlenecks. Each problem is unique, but over time, we've seen some common patterns: high memory allocation, lack of garbage collection (GC) tuning, thread pool saturation, blocking I/O, and heavy workloads processing repeated tasks. Image 1: Typical flamegraph of high CPU usage replacing string patterns Trained eyes spot these signals quickly. But two eyeballs won't scale to thousands of services. Mercado Libre is an ever-growing company, with about 35,000 microservices, 30,000 deploys per day, and more than 16,000 people in IT roles. What are the odds that a performance issue found on one microservice doesn't exist in another? Or at least a similar issue? We often encounter systems with issues that are easy to fix but hard to detect (unless you have the right tools). It became clear we had to scale. We needed a platform-level solution that would let teams self-diagnose and self-tune. By the end of this article, you'll see how we built that and how we now automate both detection and fixes for certain classes of issues. Continuous profiling After setting our goal to improve performance tooling, we developed our own continuous profiling solution. Why profiling,