# Microservices Microservices is an architecture for building applications as independently deployable, loosely coupled services. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## What is AIOps? DevFeed: [What is AIOps?]() Original publisher: [Read original article]() Author: Databricks Staff Published: 2026-09-17T16:49:43Z Content type: tutorial Language: en Sources: [Databricks]() Topics: [AIOps](), [Artificial Intelligence](), [Monitoring](), [Grafana](), [Microservices](), [telemetry](), [Cloud]() Tags: [ai](), [aiops](), [cloud](), [data-plus-ai-foundations](), [devops](), [logs](), [machine-learning](), [microservices](), [monitoring](), [network](), [observability]() ### AI overview This guide explains AIOps, which applies AI and machine learning to IT operations to detect anomalies, correlate events, identify root causes, and trigger responses. It describes how AIOps analyzes logs, traces, events, and network topology, while complementing observability, DevOps, and human judgment. ### Source excerpt Artificial Intelligence for IT Operations (AIOps) applies AI and machine learning to IT operations to detect anomalies... ## AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity DevFeed: [AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity]() Original publisher: [Read original article]() Author: Spotify Engineering Published: 2026-09-16T19:13:53Z Content type: article Language: en Sources: [Spotify Engineering](), [Spotify Engineering Blog]() Topics: [Monitoring](), [reliability](), [Microservices](), [Data pipelines](), [Artificial Intelligence](), [Job]() Tags: [ai](), [bug](), [data-pipelines](), [jobs](), [microservices](), [monitoring](), [reliability]() ### AI overview Spotify describes how rapid change, content-processing weaknesses, capacity limits, and a scheduling bug contributed to delays in publishing episodes. It reports adding end-to-end monitoring, fixing the scheduler, lowering batch-job priority, and increasing capacity. ### Source excerpt Quality and reliability have always been a point of pride for Spotify. We run an extraordinarily complex... The post AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity appeared first on Spotify Engineering. ## Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time DevFeed: [Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time]() Original publisher: [Read original article]() Author: Egor Klimov Published: 2026-09-16T12:34:47Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [OpenTelemetry](), [Microservices](), [observability](), [telemetry](), [Traces](), [ide]() Tags: [all-things-web](), [backstage](), [environment-variables](), [goland](), [ide](), [idea](), [intellij-idea](), [intellij-platform](), [jetbrains](), [microservices](), [observability](), [opentelemetry](), [performance-optimization](), [plugin-development](), [plugin-highlights](), [plugins](), [pycharm](), [research](), [rider](), [telemetry](), [traces](), [webstorm]() ### AI overview This article explains how the JetBrains OpenTelemetry Plugin generates a service map from runtime telemetry. It describes using logs, metrics, and especially standardized trace spans to visualize how microservices communicate, along with the plugin's lightweight local OpenTelemetry backend. ### Source excerpt We've all been there: you join a new project, and the first thing you ask for is the architecture diagram. You're handed a diagram that looks great, but after a week of debugging, you realize it's six months out of date. Service A hasn't talked to Service B since the spring, and there's a new [...] ## Built for Reliability: How American Express Processes Payments at Scale DevFeed: [Built for Reliability: How American Express Processes Payments at Scale]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-08T18:31:03Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [Architecture & Design](), [Microservice](), [Microservices](), [Network](), [Server]() Tags: [account-balance](), [architecture](), [bank](), [card](), [cell-based-architecture](), [microservices](), [network](), [payments](), [scale](), [server](), [services]() ### AI overview The article explains how American Express processes payments through a cell-based architecture. Transactions move through independent processing units and microservices, with isolation designed to limit disruption when services fail. ### Source excerpt In this article, we will try to understand how the transaction runs through such a cell-based architecture and how the payments are processed even when some services are failing. ## Five Years of Kafka at Razorpay's UPI Switch DevFeed: [Five Years of Kafka at Razorpay's UPI Switch]() Original publisher: [Read original article]() Author: Kshitij Nawandar Published: 2026-09-07T09:09:58Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [Kafka](), [Architecture & Design](), [event driven](), [Amazon Simple Queue Service (SQS)](), [distributed-systems](), [Microservices](), [Amazon Web Services]() Tags: [aws](), [aws-sns](), [distributed-systems](), [event-driven](), [kafka](), [microservices](), [razorpay](), [redshift](), [sns](), [sqs]() ### AI overview Razorpay describes five years of evolution in its UPI Switch, from a monolith using AWS SQS to Kafka-based infrastructure. The article covers architectural decisions, operational challenges, and optimization work affecting payment-processing performance, reliability, and scale. ### Source excerpt Preface The UPI Switch at Razorpay has evolved significantly in the five years since we started building it. The Switch is the platform that enables real-time payment processing with NPCI. When the team began, it was little more than an idea. Today it powers more than 70% of Razorpay's total UPI volume. Because UPI is inherently asynchronous, a messaging system sits at the heart of the Switch and has a direct impact on performance, reliability, and scale. What began as a straightforward queue became the core of the system, shaping how every new feature was designed and delivered. This post covers that evolution: the decisions that enabled growth, the ones that slowed us down, the operational issues that forced us to rethink our assumptions, and the optimizations that ultimately stabilized our Kafka-based infrastructure. This is the story of what we got right, what we got wrong, and how we eventually built something stable enough to grow on. The First Version: Monolith and SQS When we began building the UPI Switch, we weren't thinking about massive scale, distributed systems, or elegant event routing. So we built Switch v1 as a monolith. No microservices, no distributed orchestration: just one solid block of code doing everything. That was the right call. We needed to move fast, experiment, and learn, and we followed the Keep It Simple, Stupid (KISS) principle deliberately. For messaging, we picked AWS SQS: reliable, managed, and low on cognitive load. We didn't need ordering guarantees at the time, so a standard queue worked fine. We started with just two queues, and this setup held its ground. It handled a peak of 400 TPS during the IPL. The limitations showed up as the ecosystem grew. A single event, like a successful payment, needed to fan out into multiple workflows: Update NPCI with an API call Send callbacks to merchants about payment status Push structured data into our warehouse (AWS Redshift) To handle this, we started bolting on AWS SNS plus SQS for fan-ou ## API Mocking and Testing With Microcks DevFeed: [API Mocking and Testing With Microcks]() Original publisher: [Read original article]() Author: Andrei Branza Published: 2026-08-22T23:20:24Z Content type: tutorial Language: en Sources: [Baeldung]() Topics: [Mocking](), [Testcontainers](), [API](), [OpenAPI Specification](), [Microservices](), [Docker Compose](), [Docker](), [Kafka](), [Amazon Simple Queue Service (SQS)](), [GraphQL](), [gRPC](), [Jenkins]() Tags: [api](), [compose](), [docker](), [how-to](), [kafka](), [kubernetes](), [mocking](), [openapi](), [popular](), [testcontainers](), [testing](), [testing-popular-testcontainers]() ### AI overview This tutorial explains how to use Microcks with Testcontainers to generate API mocks from contracts and run conformance tests against real implementations. It covers importing an OpenAPI contract, starting Microcks in a JUnit 5 test, calling generated mock endpoints, and integrating mocking and testing into delivery workflows. ### Source excerpt Learn how to use Testcontainers integration to spin up Microcks inside a JUnit5 test. The post API Mocking and Testing With Microcks first appeared on Baeldung. ## When Microservice Decomposition Is the Wrong Default DevFeed: [When Microservice Decomposition Is the Wrong Default]() Original publisher: [Read original article]() Author: Philipp Lehmann (philipp.lehmann@gruppe.ai) Published: 2026-08-21T07:00:00Z Content type: opinion Language: en Sources: [Philipp Lehmann]() Topics: [Microservice](), [Architecture & Design](), [Microservices](), [FastAPI]() Tags: [api](), [architecture](), [circuit](), [docker](), [microservices](), [migration](), [modular-monolith](), [monolith](), [openapi](), [tracing]() ### AI overview The article argues that splitting a small system into microservices should not be the default. It recommends keeping responsibilities in a modular monolith unless differences in scaling needs, organizational boundaries, or failure isolation justify a network boundary, whose costs include contracts, compatibility management, retries, timeouts, circuit breaking, and distributed tracing. ### Source excerpt import-linter forbidden contract as a module boundary: a FastAPI modular monolith instead of early microservices, and when a network split pays off. ## Show, Don't Tell: What Evo Continuous Offensive Security Found in a Real Enterprise SaaS DevFeed: [Show, Don't Tell: What Evo Continuous Offensive Security Found in a Real Enterprise SaaS]() Original publisher: [Read original article]() Author: Nuno Loureiro; Luis Grangeia Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [Vulnerabilities](), [Authorization](), [Software as a service](), [Microservice](), [Microservices](), [Front end](), [API](), [App](), [Artificial Intelligence](), [Trustworthy AI]() Tags: [agent](), [ai](), [api](), [application-security](), [applications](), [attacks](), [autonomous](), [awareness](), [blog](), [customer](), [customer-featured](), [cybersecurity](), [devops](), [enterprise](), [frontend](), [interest](), [microservices](), [saas](), [security](), [vulnerabilities](), [vulnerability-insights](), [web]() ### AI overview Evo Continuous Offensive Security (COS) is presented as an autonomous offensive-security system combining AI pentesting, agent red teaming, and dynamic testing. The article reports a real assessment of a multi-tenant enterprise SaaS application that uncovered 33 confirmed vulnerabilities, including tenant-wide compromise and critical authorization flaws. ### Source excerpt A real Evo Continuous Offensive Security assessment uncovered 33 confirmed vulnerabilities in a multi-tenant enterprise SaaS, including tenant-wide compromise and critical authorization flaws. ## The Pulse: Bending Spoons' Acquisition Strategy DevFeed: [The Pulse: Bending Spoons' Acquisition Strategy]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-08-05T11:45:13Z Content type: opinion Language: en Sources: [The Pragmatic Engineer]() Topics: [migration](), [Architecture & Design](), [Microservices](), [Java](), [virtual machines](), [Cloud](), [Google Cloud Platform (GCP)](), [legacy application]() Tags: [architecture](), [backend](), [cloud](), [google-cloud](), [java](), [legacy](), [microservices](), [migration](), [performance](), [provisioning](), [virtual-machines]() ### AI overview The article examines Bending Spoons' acquisition strategy, including its announced $1.285 billion cash purchase of Airtable and its approach to operating struggling products with smaller teams. It also describes the company's reported modernization of Evernote from a Java 11 monolith running across 750 manually provisioned virtual machines on Google Cloud to a managed-database and microservices architecture in about six months, with reported improvements in performance, reliability, operating cost, and on-call load. ### Source excerpt In only 5 years, Hopin went from zero to a $7.7B valuation, and back to zero again. Also: Bending Spoons' startup acquisition model. ## Using Backstage's C4 Model adaptation to visualize software DevFeed: [Using Backstage's C4 Model adaptation to visualize software]() Original publisher: [Read original article]() Author: Daniel Sinai Published: 2026-07-30T10:43:36Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Backstage](), [internal developer portal](), [Software](), [Architecture & Design](), [Microservices](), [API]() Tags: [api](), [architecture](), [backstage](), [internal-developer-portal](), [microservices](), [software](), [software-catalog]() ### AI overview The article explains how Backstage's adaptation of the C4 model can visualize software through a software catalog. It describes cataloging microservices, deployment resources, deployments, relationships, dependencies, ownership, and lifecycles, and presents a SaaS-based internal developer portal as an alternative implementation approach. ### Source excerpt Using Backstage's C4 Model Adaptation to Visualize Software - Creating a Software Catalog in Port. Read more here. ## A field guide to splitting systems, keeping data correct, and surviving failure in production DevFeed: [A field guide to splitting systems, keeping data correct, and surviving failure in production]() Original publisher: [Read original article]() Author: Neo Kim Published: 2026-07-23T10:24:07Z Content type: tutorial Language: en Sources: [System Design Newsletter]() Topics: [Microservice](), [Microservices](), [Architecture & Design](), [systems]() Tags: [data](), [microservices](), [patterns](), [production](), [systems]() ### AI overview This field guide covers microservices design patterns for splitting systems, maintaining data correctness, and handling failures in production. ### Source excerpt #164: A field guide to splitting systems, keeping data correct, and surviving failure in production ## 10 observability tools platform engineers should evaluate in 2026 DevFeed: [10 observability tools platform engineers should evaluate in 2026]() Original publisher: [Read original article]() Author: Sam Barlien Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [observability](), [Platform Engineering](), [OpenTelemetry](), [telemetry](), [distributed-systems](), [Kubernetes](), [Microservices](), [Instrumentation](), [Monitoring](), [CI/CD]() Tags: [ci-cd](), [developer](), [instrumentation](), [integration](), [kubernetes](), [metrics](), [microservices](), [observability](), [opentelemetry](), [operational](), [platform](), [platform-engineering](), [telemetry](), [traces]() ### AI overview This article evaluates observability tools for platform engineers in 2026. It focuses on the dual requirement of operational visibility and developer self-service, with evaluation criteria including native OpenTelemetry support, cost optimization, transparent pricing, and integration with internal developer platforms. It also discusses observability for Kubernetes, microservices, distributed systems, and shared infrastructure. ### Source excerpt Discover the 10 best observability tools for platform engineers in 2026. Learn how to meet the dual mandate of operational visibility and developer self-service by prioritizing OpenTelemetry support, transparent pricing, and seamless integration with your platform. ## A Step-by-Step Guide to Feature Flag Implementation in CI/CD DevFeed: [A Step-by-Step Guide to Feature Flag Implementation in CI/CD]() Original publisher: [Read original article]() Author: Aaron Newcomb Published: 2026-07-23T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [CI/CD](), [feature flags](), [GitOps](), [Automation](), [Microservices](), [Deployment]() Tags: [automation](), [ci-cd](), [deployment](), [feature-flags](), [gitops](), [governance](), [guide](), [microservices](), [pipelines](), [policy-as-code](), [rollback]() ### AI overview A step-by-step tutorial on implementing feature flags in enterprise CI/CD pipelines. It explains how governance, policy as code, GitOps workflows, automation, verification, gradual rollouts, and rollback capabilities can help teams manage releases across many services while maintaining control and compliance. ### Source excerpt Discover how to implement Feature Flags in CI/CD pipelines using governance, automation, and AI-driven delivery. Speed up your releases while keeping them safe. | Blog ## Announcing Upvest as Confluent's 2026 EMEA Data Streaming Startup of the Year DevFeed: [Announcing Upvest as Confluent's 2026 EMEA Data Streaming Startup of the Year]() Original publisher: [Read original article]() Author: Tim Graczewski Published: 2026-07-16T00:07:01Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Streaming](), [real-time](), [Resilience](), [event driven](), [Scalability](), [Cloud](), [data-governance](), [Microservices](), [Architecture & Design](), [Disaster Recovery]() Tags: [2026](), [cloud](), [cloud-native](), [compliance](), [confluent](), [confluent-cloud](), [data](), [data-governance](), [disaster-recovery](), [emea](), [event-driven](), [fintech](), [infrastructure](), [microservices](), [real-time](), [resilience](), [revolut](), [scalability](), [startup](), [streaming]() ### AI overview Confluent recognizes Berlin-based Upvest as its inaugural EMEA Data Streaming Startup of the Year. The article describes Upvest's real-time, event-driven fintech infrastructure, which supports embedded investments, client onboarding, data governance, disaster recovery, scalability, and regulatory resilience for major financial institutions. ### Source excerpt Confluent for Startups provides an easy on-ramp to Confluent Cloud for early stage startups with great data streaming use cases. ## A Good System Design Tackles Down the Hot Path First DevFeed: [A Good System Design Tackles Down the Hot Path First]() Original publisher: [Read original article]() Author: Raul Junco Published: 2026-06-13T11:31:25Z Content type: tutorial Language: en Sources: [System Design Classroom]() Topics: [Architecture & Design](), [Microservices](), [Caching]() Tags: [cache](), [microservices]() ### AI overview The article argues that system design should begin by identifying the system's hot paths--the specific parts experiencing pressure--before adding caches, replicas, queues, or microservices. It warns that scaling decisions made without understanding traffic patterns can target the wrong bottleneck. ### Source excerpt Before you add cache, replicas, queues, or microservices, understand where the system actually feels pressure. ## ClickHouse achieves AWS Retail Competency DevFeed: [ClickHouse achieves AWS Retail Competency]() Original publisher: [Read original article]() Author: Aditya Chidurala Published: 2026-06-12T21:17:44Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Amazon Web Services](), [real-time](), [data](), [Amazon S3](), [Compression](), [Kafka](), [Microservices](), [observability](), [Traces](), [Cloud](), [networking]() Tags: [amazon-s3](), [aws](), [batch](), [business](), [clickhouse](), [cloud](), [compression](), [data](), [e-commerce](), [kafka](), [microservices](), [observability](), [personalization](), [real-time](), [retail](), [s3](), [storage](), [traces]() ### AI overview ClickHouse has achieved the AWS Retail Competency in the Advanced Data Insights category, recognizing its validated expertise in real-time retail analytics and customer success on AWS. ### Source excerpt ClickHouse has achieved the AWS Retail Competency, joining a select group of AWS Partners recognized for deep expertise in helping retailers turn live operational data into real-time decisions. ## From Co-Pilot to Full Automation: How Wix Is Embedding AI Agents Across an Engineering Org at Scale DevFeed: [From Co-Pilot to Full Automation: How Wix Is Embedding AI Agents Across an Engineering Org at Scale]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-05-20T07:28:09Z Content type: article Language: en Sources: [Wix Engineering]() Topics: [ai-coding](), [agentic-coding](), [monorepo](), [Microservices](), [Claude](), [codex](), [cursor](), [GitHub Copilot](), [OpenClaw]() Tags: [agentic-coding](), [agents](), [ai](), [ai-coding](), [claude-code](), [codex](), [coding-agents](), [cursor](), [github-copilot](), [microservices](), [monorepo](), [openclaw]() ### AI overview Wix describes how agentic coding can be organized across an engineering organization with more than 2,500 microservices, thousands of engineers, strong service boundaries, and a large monorepo. The article presents a three-tier spectrum of cloud agents, coding agents or harnesses, and agents with computer access, identifying the harness tier as the practical sweet spot for software development as of 2025-2026. ### Source excerpt When you have over 2,500 microservices and thousands of engineers working across a massive multi-product platform, "just use AI coding tools" is not a strategy. It's a starting point - and not a particularly useful one. The question we kept coming back to at Wix wasn't whether to adopt AI in our engineering workflow. That ship had sailed. The question was: what does agentic coding actually look like when you have strong domain boundaries between services, a monorepo with years of... ## What Agentic AI Can Learn from Microservices Architecture DevFeed: [What Agentic AI Can Learn from Microservices Architecture]() Original publisher: [Read original article]() Author: Cornelia Davis Published: 2026-04-23T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [Microservices](), [AI Agent](), [Architecture & Design](), [Orchestration]() Tags: [agents](), [analysis](), [architecture](), [microservices](), [orchestration](), [temporal-voices]() ### AI overview This opinion article argues that many production problems in AI agent systems resemble problems previously addressed by cloud-native and microservices architectures. It introduces parallels involving decomposition and orchestration, while noting that AI systems also have genuinely new aspects such as LLM APIs. ### Source excerpt The microservices era already solved the problems AI agents face in production. Read this nuanced analysis of EDA, event sourcing, and orchestration for agentic AI. ## Top 10 API Gateway Use Cases in System Design DevFeed: [Top 10 API Gateway Use Cases in System Design]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-04-12T12:00:32Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Amazon API Gateway](), [Architecture & Design](), [Microservices](), [API]() Tags: [api-gateway](), [microservices](), [monolith](), [system-design](), [use-cases]() ### AI overview An article about ten API gateway use cases in system design, particularly as systems evolve from monoliths to microservices. The supplied evidence does not detail the individual use cases. ### Source excerpt As your system evolves from a monolith to microservices, a pattern quickly emerges: every service starts rebuilding the same things. ## Razorpay's Linked Payments Architecture for Combining Payment Methods DevFeed: [Razorpay's Linked Payments Architecture for Combining Payment Methods]() Original publisher: [Read original article]() Author: Vatsal Mehta Published: 2026-04-08T09:56:17Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [Architecture & Design](), [Microservices](), [Transactions](), [Database]() Tags: [architectures](), [authorization](), [fintech](), [microservices](), [monolithic-architecture](), [payment](), [payment-gateway](), [payment-processing](), [payments](), [razorpay](), [recovery](), [technical](), [transactions]() ### AI overview Razorpay describes Linked Payments, an architecture that lets customers combine a gift card with card, UPI, or another payment method for one order. The article explains the resulting challenges around authorization sequencing, failure recovery, and split settlement across payment-method microservices. ### Source excerpt You have a $50 gift card. Your cart is $75. The checkout says "pick one payment method." This is a solvable problem. If you've ever tried to use a gift card for an online purchase, you know this frustration intimately. The gift card covers most of it, but not quite all. The checkout forces you to choose: use the gift card and abandon some items, or ignore the gift card and pay the full amount another way. Either choice feels wrong. This isn't a technical limitation of payment processing. It's an architectural one. Most payment systems treat each method as an isolated, complete transaction. You pay with a card OR UPI OR a gift card. The concept of composing multiple methods to fulfill a single order simply doesn't exist in traditional payment gateway architectures. At Razorpay, this limitation was costing merchants real money. Gift card redemption rates suffered because customers abandoned partial-value cards. Average order values stayed lower because customers couldn't combine store credit with additional payment. The business case for solving this was clear. That's why we built Linked Payments, a system that treats payment methods as composable building blocks. Customers can now use a gift card for $50, then cover the remaining $25 via card, UPI, or any other method. The system handles authorization sequencing, failure recovery, and settlement splitting automatically. The Complexity Hidden in "Just Combine Them" The challenge sounds simple until you consider what payment systems actually do. Traditional payment flows are beautifully simple. Customer initiates payment. System authorizes the full amount from one method. If authorization succeeds, capture the funds. Settle to the merchant. Either the payment worked or it didn't. One authorization, one capture, one settlement. Linked payments shatter this simplicity. Now you have multiple authorizations for a single order. Sequential dependencies where the second payment only happens if the first succeeds. Partial fail ## Unifying Internal APIs: A Different Use Case for GraphQL Gateways DevFeed: [Unifying Internal APIs: A Different Use Case for GraphQL Gateways]() Original publisher: [Read original article]() Author: Angel Svirkov Full-Stack Software Engineer @ trivago GitHub profile Linkedin profile Published: 2026-03-27T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [GraphQL](), [gateway](), [Tooling](), [Architecture & Design](), [Microservices]() Tags: [admin](), [agents](), [apis](), [architecture](), [artificial-intelligence](), [backend](), [crud](), [engineering](), [gateway](), [graphql](), [tooling]() ### AI overview This article describes how trivago used GraphQL Mesh to unify fragmented internal APIs into a gateway for administration, CRUD operations, reporting, and data exploration. It covers the architecture, implementation challenges encountered over six years in production, and possible future uses involving AI agents. ### Source excerpt Most GraphQL Gateway discussions focus on public-facing APIs and multi-client architectures. This article explores a different axis--using GraphQL Mesh to stitch internal services into a unified gateway powering admin tooling. We share implementation details, honest challenges from six years in production, and a forward-looking perspective on how AI agents could leverage the unified graph. ## Breaking the release monolith: How OutSystems platform engineering restored trust in delivery DevFeed: [Breaking the release monolith: How OutSystems platform engineering restored trust in delivery]() Original publisher: [Read original article]() Published: 2026-03-19T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Platform Engineering](), [Cloud Native Ecosystem](), [Continuous Delivery (CD)](), [Microservices](), [Kubernetes]() Tags: [chainguard-assemble](), [chainguard-containers](), [cloud-native](), [continuous-delivery](), [kubernetes](), [microservices](), [outsystems](), [outsystems-pegasus](), [platform-engineering](), [release]() ### AI overview The article describes how OutSystems transformed a slow, synchronized software release process through platform engineering. Its cloud-native OutSystems Developer Cloud used microservices and Kubernetes, but releases still followed an older release-train model. The company's journey included building the Pegasus CD platform and restoring trust in delivery. ### Source excerpt At Chainguard Assemble 2026, Outsystems shared how it transformed its release process through platform engineering. ## Continuous profiling at Mercado Libre: Turning flamegraphs into fixes DevFeed: [Continuous profiling at Mercado Libre: Turning flamegraphs into fixes]() Original publisher: [Read original article]() Author: Elton Hoffmann Published: 2026-03-11T01:27:33Z Content type: article Language: en Sources: [Mercado Libre Tech]() Topics: [observability](), [Tooling](), [Microservices](), [Latency](), [systems]() Tags: [ai](), [article](), [efficiency](), [end-user-experience](), [flamegraph](), [garbage-collection](), [latency](), [memory](), [microservices](), [observability](), [performance](), [performance-engineering](), [platform-engineering](), [profiling](), [uptime]() ### 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, ## How to Secure Microservices with SPIFFE and Istio DevFeed: [How to Secure Microservices with SPIFFE and Istio]() Original publisher: [Read original article]() Author: info@goteleport.com (Jeff Ellin, Boris Kurktchiev) Published: 2026-02-20T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [istio](), [Microservices](), [SPIFFE](), [Zero Trust](), [Authorization](), [Architecture & Design](), [Secrets Management](), [certificates]() Tags: [architecture](), [authorization](), [backend](), [certificates](), [cloud-native](), [firewalls](), [how-to](), [istio](), [kubernetes](), [microservices](), [policies](), [secrets-management](), [security](), [service-mesh](), [spiffe](), [zero-trust]() ### AI overview This guide explains how to secure microservices with SPIFFE identities, Istio service-mesh mTLS, short-lived certificates, and Zero Trust authorization policies. It addresses the limits of network-based trust and long-lived certificates in dynamic Kubernetes environments. ### Source excerpt Learn how to deploy a secure microservices application, configure default-deny authorization policies, and rebuild service connectivity with SPIFFE-based allow rules. [Next page]()