# uptime Published articles for uptime. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Solana: Building, Proving and Earning Trust in Public DevFeed: [Solana: Building, Proving and Earning Trust in Public]() Original publisher: [Read original article]() Author: Jacob Creech Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [Solana News Feed]() Topics: [Network](), [Critical Infrastructure](), [incident](), [networking](), [Transactions](), [Amazon Web Services]() Tags: [2026](), [aws](), [blockchain](), [blockchain-technology](), [building](), [critical-infrastructure](), [crypto](), [crypto-news](), [cryptocurrency](), [defi](), [ecosystem](), [incident](), [infrastructure](), [network](), [nfts](), [outages](), [podcasts](), [routing](), [solana](), [solana-ecosystem](), [technology](), [transactions](), [uptime](), [web3]() ### AI overview Solana describes how repeated public testing, transparent incident reporting, and corrective engineering have strengthened trust in the network. It highlights a 2026 routing failure that took nearly 29% of network stake offline while blocks and transactions continued, followed by recovery of the infrastructure provider in just over 30 minutes. The article also cites validator capacity improvements, stake-weighted quality of service, and a redesigned transaction scheduler as examples of resilience work. ### Source excerpt Solana has maintained 100% uptime since February 2024, including when a routing failure took nearly 29% of network stake offline. ## From failed check to real user impact: Pairing Synthetic Monitoring and Frontend Observability in Grafana Cloud DevFeed: [From failed check to real user impact: Pairing Synthetic Monitoring and Frontend Observability in Grafana Cloud]() Original publisher: [Read original article]() Author: Mark Meier Published: 2026-09-12T11:22:06.456390Z Content type: tutorial Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [synthetic monitoring](), [Frontend observability](), [Grafana Cloud](), [observability]() Tags: [analytics](), [frontend-observability](), [grafana](), [grafana-cloud](), [logs](), [metrics](), [monitoring](), [observability](), [synthetic-monitoring](), [traces](), [uptime]() ### AI overview This article explains how to combine Grafana Cloud Synthetic Monitoring with Frontend Observability to connect proactive checks with real-user impact. It describes the blind spots of synthetic-only monitoring and presents a combined workflow for faster triage, impact-aware alerts, and tests that evolve with real traffic. ### Source excerpt Say you get a support escalation about a page in the app that won't load. But when you pull up your synthetic checks, they're all green: 100% uptime, probes are passing. Something's not adding up, but which one do you trust? If you've run Grafana Cloud Synthetic Monitoring, you've been on both sides of this. Sometimes it's the ticket: real users hit a wall on the path but your checks pass cleanly. Other times, it's the inverse: a check is failing, you're in a panic, and you start trying to reproduce things for 30 minutes--only to find it was a blip from a single region, with minimal impact to real users. Neither the green dashboard nor the red alert were lying, they just weren't answering the correct question. This ends up being the root problem. Synthetic Monitoring is exceptionally good at telling you if something broke. It can not, however, tell you who it happened to, how bad it was, or why it matters. This is not a flaw in Synthetic Monitoring; it's the boundary of what a controlled, scheduled test can know. Grafana Cloud Frontend Observability helps to close this gap. Synthetic Monitoring gives you a proactive, outside-in signal; Frontend Observability gives you the real-user, inside-out signal. Together they form a closed loop: synthetic alerts end up getting some real user context, and real user data can make your synthetic tests smart. In this post, we'll look at why a synthetic-only strategy can leave blind spots, what Frontend Observability adds, and walk through practical workflows for running them together in Grafana Cloud. Along the way, you'll learn that the payoff is concrete: faster triage, alerts that carry blast-radius context, and a check suite that evolves with real traffic instead of aging against it. Green checks don't mean happy users Synthetic Monitoring is an active signal. You script a journey or declare a target, run it on a schedule from known probe locations, and in return get clean consistent results. This precise control of variables i ## Coherence, Connections, and... Spacetime Crystals DevFeed: [Coherence, Connections, and... Spacetime Crystals]() Original publisher: [Read original article]() Author: Art Anthony Published: 2026-09-09T07:00:00Z Content type: article Language: en Sources: [Nordic APIs]() Topics: [API](), [observability](), [Statistics]() Tags: [2026](), [ai-agents](), [api](), [api-architecture](), [api-ecosystem](), [api-governance](), [api-management](), [api-platform](), [api-strategy](), [artificial-intelligence](), [blog](), [governance](), [observability](), [statistics](), [uptime]() ### AI overview The article discusses API coherence and how API estates can align with organizational goals and change. It covers business gardening, observability, governance, uptime, and usage statistics in the context of API performance and organizational intent. ### Source excerpt Ahead of his Nordic APIs Summit 2026 talk on API coherence, London Stock Exchange Group's Gareth Faull joins us to talk about the art of aligning APIs with organizational intent. Measuring the performance of an API is a relatively straightforward process: ensure observability is in place, follow governance best practices, measure uptime, track usage statistics, ... ## Muse Image now available on AI Gateway DevFeed: [Muse Image now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-08-26T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [Meta](), [vercel ai sdk](), [API](), [superintelligence](), [Inference]() Tags: [ai](), [ai-gateway](), [api](), [image](), [inference](), [meta](), [meta-muse](), [model](), [models](), [muse](), [pricing](), [sdk](), [superintelligence](), [uptime]() ### AI overview Muse Image, Meta Superintelligence Labs' first image model, is now available through Vercel's AI Gateway. It supports both image generation from prompts and instruction-based image editing, including reference images, through the AI SDK. AI Gateway also provides unified model access, usage and cost tracking, failover, routing, reporting, budgets, and performance optimizations without markup or platform fees on inference. ### Source excerpt Muse Image from Meta Superintelligence Labs is now available on AI Gateway. It is their first image model and a separate family from Muse Spark, returning images rather than text. Send a prompt and get an image back, or send an image with an instruction and get it changed. One model does both, so you don't switch models to move from generating to editing. To use Muse Image, set model to meta/muse-image-1.0 and call generateImage from the AI SDK: To steer the result toward art you already have, pass reference images in prompt.images alongside the text, and the model blends them into what it draws. Editing Pass the image you want changed in prompt.images with an instruction, and the model changes what you asked for and leaves the rest: Try Muse Image in the model playground. AI Gateway provides a unified API for calling models, tracking usage and cost, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. You can view all image models available on AI Gateway. Read more ## The Gremlin app for Dynatrace: resilience testing and reliability scoring, built on the observability you already trust DevFeed: [The Gremlin app for Dynatrace: resilience testing and reliability scoring, built on the observability you already trust]() Original publisher: [Read original article]() Author: Ryan Detwiller Published: 2026-07-28T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Resilience](), [dynatrace](), [observability](), [Instrumentation](), [distributed-systems]() Tags: [announcements](), [distributed-systems](), [dynatrace](), [engineering](), [gremlin](), [incident](), [instrumentation](), [metrics](), [observability](), [real-time](), [resilience](), [safety](), [systems](), [testing](), [uptime]() ### AI overview The Gremlin app for Dynatrace adds resilience testing and reliability scoring to Dynatrace workflows. Teams can run reliability tests, observe their impact in real time, and track service-level reliability scores using existing Dynatrace metrics, alerts, instrumentation, and health checks. ### Source excerpt With the Gremlin app for Dyantrace, you get resilience testing and reliability scoring built on the observability you already trust. ## Observability isn't enough to tell if your platform is healthy DevFeed: [Observability isn't enough to tell if your platform is healthy]() Original publisher: [Read original article]() Author: Adora Nwodo Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [Developer experience](), [observability](), [Availability](), [Deployment]() Tags: [2025](), [availability](), [dashboards](), [developer](), [developer-experience](), [metrics](), [observability](), [onboarding](), [platform](), [platform-engineering](), [technical](), [uptime]() ### AI overview The article argues that observability and uptime alone cannot determine whether a platform is healthy. Platform health should be evaluated through utility, developer adoption, onboarding time, self-service rates, developer experience, and qualitative feedback. A healthy platform earns usage because it makes work faster and safer, while technically sound platforms can fail when developers bypass them. ### Source excerpt Rethink platform health: why uptime isn't enough. Learn how adoption, developer experience, reliability, and toil reveal your platform's real impact. ## Zero Downtime Database Migrations: Safe Schema Changes DevFeed: [Zero Downtime Database Migrations: Safe Schema Changes]() Original publisher: [Read original article]() Author: Animesh Pathak Published: 2026-07-16T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [Database](), [Database Migration](), [migration](), [Availability](), [Code]() Tags: [availability](), [blog](), [compatibility](), [consistency](), [database](), [database-migration](), [learn](), [migrations](), [patterns](), [production](), [schema](), [strategy](), [uptime]() ### AI overview This tutorial explains zero-downtime database migrations through backward-compatible schema changes, the expand-and-contract pattern, dual writes, and phased rollouts. It emphasizes preserving application availability, data consistency, and compatibility while old and new application versions run together. ### Source excerpt Learn zero downtime database migration strategies using backward-compatible schema changes, dual writes, and safe rollout patterns. | Blog ## How a status page can show a site at its best (and 10 examples) DevFeed: [How a status page can show a site at its best (and 10 examples)]() Original publisher: [Read original article]() Author: James Konik Published: 2026-07-15T07:00:00Z Content type: article Language: en Sources: [Honeybadger]() Topics: [incident](), [Tool](), [data]() Tags: [article](), [communication](), [customer](), [developer-tools](), [devops](), [devops-articles](), [incident](), [maintenance](), [metrics](), [services](), [tool](), [uptime]() ### AI overview This article explains why public status pages help build customer trust, communicate incidents and scheduled maintenance, and demonstrate service reliability. It outlines principles for effective status pages, including clarity and useful metrics, and presents examples before showing how to set one up with Honeybadger. ### Source excerpt A status page is an essential tool for keeping users updated, but how can you make sure yours is the best possible? In this article you'll see how to make your status page perfect, and how Honeybadger can help you do that. ## Inkling from Thinking Machines is now available on AI Gateway DevFeed: [Inkling from Thinking Machines is now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-15T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Models](), [SDKs]() Tags: [agentic](), [ai](), [ai-gateway](), [api](), [api-keys](), [audio](), [coding](), [cost](), [platform](), [playground](), [reasoning](), [routing](), [sdk](), [tokens](), [uptime](), [vision]() ### AI overview Inkling from Thinking Machines is now available through Vercel's AI Gateway. It is a broad generalist model covering agentic, reasoning, coding, instruction-following, factuality, vision, and audio tasks, with controllable thinking effort. The AI Gateway provides unified model access, usage and cost tracking, retries, failover, routing, reporting, Zero Data Retention support, API-key budgets, and provider pricing without markup or platform inference fees. ### Source excerpt Inkling from Thinking Machines is now available on AI Gateway. Inkling is a broad generalist model, trained across agentic, reasoning, coding, instruction-following, factuality, vision, and audio tasks rather than optimized for a single domain. The model also supports controllable thinking effort. To use Inkling, set model to thinkingmachines/inkling in the AI SDK: AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Try Inkling in the model playground. Read more ## Eliminate Reliability Blind Spots in AWS, Azure, and GCP DevFeed: [Eliminate Reliability Blind Spots in AWS, Azure, and GCP]() Original publisher: [Read original article]() Author: Andre Newman Published: 2026-07-14T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Cloud](), [Kubernetes](), [Resilience](), [Amazon Web Services](), [Azure](), [Chaos Engineering]() Tags: [availability](), [aws](), [azure](), [containers](), [deployment](), [experimentation](), [features](), [gcp](), [gremlin](), [kubernetes](), [orchestration](), [outages](), [resilience](), [uptime]() ### AI overview Gremlin's expanded Detected Risks feature automatically identifies high-priority reliability risks across AWS, Azure, GCP, and Kubernetes environments. It is designed to reveal issues such as misconfigured deployments, crash-looping containers, missing readiness probes, and poor pod distribution before they cause outages. ### Source excerpt Discover reliability risks without running a single test. See how Gremlin identifies high-priority reliability risks across AWS, Azure, and GCP to prevent outages. ## Seedream 5.0 Pro is now available on AI Gateway DevFeed: [Seedream 5.0 Pro is now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-11T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Models](), [API](), [model-deployment](), [SDKs]() Tags: [ai](), [ai-gateway](), [api](), [api-keys](), [cost](), [generation](), [image](), [images](), [inference](), [models](), [performance](), [routing](), [tokens](), [uptime]() ### AI overview Seedream 5.0 Pro is now available through Vercel AI Gateway as an image generation and editing model. It can create text-to-image outputs with accurate text rendering, typographic formatting, dense infographics, and realistic imagery. The article also describes AI Gateway features for model access, usage and cost tracking, retries, failover, reporting, API key budgets, routing, and model discovery through a playground and leaderboard. ### Source excerpt Seedream 5.0 Pro is now available on AI Gateway. Seedream 5.0 Pro is an image generation and editing model. It generates images from text, rendering text without spelling errors and following typographic rules, and produces dense infographics with charts, timelines, and layouts alongside realistic imagery. To use Seedream 5.0 Pro, set model to bytedance/seedream-5.0-pro in the AI SDK: AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Try Seedream 5.0 Pro in the model playground. Read more ## Announcing no-code application fault injection DevFeed: [Announcing no-code application fault injection]() Original publisher: [Read original article]() Author: Andre Newman Published: 2026-06-02T00:00:00Z Content type: release Language: en Sources: [Gremlin Blog]() Topics: [Failure Flags](), [Serverless](), [Chaos Engineering](), [Testing](), [Availability](), [Latency](), [observability]() Tags: [availability](), [failure-flags](), [gremlin](), [health-checks](), [infrastructure](), [latency](), [no-code](), [observability](), [performance](), [serverless](), [testing](), [uptime]() ### AI overview Gremlin announces Failure Flags by proxy, a no-code application fault injection solution for serverless and managed applications. The sidecar proxy enables reliability tests such as simulating outages, adding latency, and generating exceptions without code changes. Intelligent Health Checks automatically monitor network throughput, latency, and error rate during tests. ### Source excerpt Gremlin announces Failure Flags by proxy, a no-code application fault injection solution for serverless and managed applications. Learn more in our latest blog post. ## Opus 4.8 on AI Gateway DevFeed: [Opus 4.8 on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-05-28T07:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Claude](), [Anthropic Claude](), [Vercel](), [vercel ai sdk](), [API](), [coding](), [Latency](), [model-deployment]() Tags: [agentic](), [ai](), [ai-gateway](), [anthropic](), [anthropic-claude](), [api](), [claude](), [coding](), [cost](), [latency](), [leaderboard](), [performance](), [playground](), [pricing](), [retention](), [sdk](), [uptime](), [vercel]() ### AI overview Claude Opus 4.8 is now available through Vercel AI Gateway and the AI SDK. It is designed for long-horizon agentic execution, complex multi-step coding tasks, and knowledge work. AI Gateway offers unified model access, usage and cost tracking, retries, failover, performance optimization, reporting, Zero Data Retention support, and provider selection based on latency and cost without adding inference fees. ### Source excerpt Claude Opus 4.8 is now available on Vercel AI Gateway. Claude Opus 4.8 is built for long-horizon agentic execution and handles complex, multi-step coding tasks like refactors that previously required human correction mid-task. The model also produces clearer, less hedgy prose for knowledge work like drafting documents, analyzing data, and building presentations. To use Opus 4.8, set model to anthropic/claude-opus-4.8 in the AI SDK. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, dynamic provider sorting by latency & cost, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Learn more about AI Gateway, view the AI Gateway model leaderboard or try it in our model playground. Read more ## incident.io launches PagerDuty Rescue Program DevFeed: [incident.io launches PagerDuty Rescue Program]() Original publisher: [Read original article]() Author: Tom Wentworth Published: 2026-05-13T13:30:00Z Content type: release Language: en Sources: [The incident.io Blog]() Topics: [incident](), [Tooling](), [migration](), [Availability](), [Artificial Intelligence]() Tags: [ai](), [availability](), [dependency](), [engineering](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [migration](), [on-call](), [outage](), [post-mortem](), [report](), [slack-incident](), [switching](), [tooling](), [uptime]() ### AI overview incident.io announced the PagerDuty Rescue Program for companies switching their on-call tooling. The program offers contract buyouts, AI-powered migration assistance, built-in migration tooling, a 99.99% uptime guarantee, and AI-first alert investigation. ### Source excerpt incident.io just launched the PagerDuty Rescue Program, making it easier than ever for engineering teams to ditch their decade-old on-call tooling. The program includes a contract buyout (up to a year free), AI-powered white glove migration, a 99.99% uptime SLA, and AI-first on-call that investigates alerts autonomously the moment they fire. ## Grok 4.3 on AI Gateway DevFeed: [Grok 4.3 on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-04-30T07:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [vercel ai sdk](), [API](), [context window](), [long-context](), [Routing (disambiguation)](), [Vercel]() Tags: [ai](), [ai-gateway](), [api](), [context-window](), [cost](), [leaderboard](), [model](), [models](), [observability](), [performance](), [playground](), [routing](), [sdk](), [tool](), [uptime](), [vercel]() ### AI overview Grok 4.3 is now available through Vercel AI Gateway, offering a 1M-token context window and improvements in accuracy, tool calling, and instruction following. The article explains that AI Gateway provides a unified API, usage and cost tracking, retries, failover, provider routing, observability, custom reporting, and Bring Your Own Key support. ### Source excerpt Grok 4.3 is now available on Vercel AI Gateway. The model has a 1M token context window and improvements in accuracy, tool calling, and instruction following. To use Grok 4.3, set model to xai/grok-4.3 in the AI SDK. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, observability, Bring Your Own Key support, and intelligent provider routing with automatic retries. Learn more about AI Gateway, view the AI Gateway model leaderboard or try it in our model playground. Read more ## Building Resilient Fintech Infrastructure for Scale DevFeed: [Building Resilient Fintech Infrastructure for Scale]() Original publisher: [Read original article]() Author: David Weiss Published: 2026-04-02T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Resilience](), [Data Infrastructure](), [Database](), [CockroachDB](), [legacy]() Tags: [cockroach-labs](), [cockroachdb](), [compliance](), [fintech](), [fraud-detection](), [infrastructure](), [legacy](), [outages](), [postgresql](), [resilience](), [uptime]() ### AI overview The article argues that fintech and quant firms need resilient data infrastructure to handle growth, real-time payments, instant settlement, AI-driven fraud detection, and cross-border compliance. It describes the limits of legacy database architectures and presents SumUp's migration from PostgreSQL to CockroachDB as an example of improved resilience and near-zero downtime. ### Source excerpt The fintech companies and quant firms that define the next decade aren't just building better products. They're succeeding with more resilient fintech infrastructure. ## NVIDIA GTC 2026 Highlighted the Shift to Production Inference DevFeed: [NVIDIA GTC 2026 Highlighted the Shift to Production Inference]() Original publisher: [Read original article]() Author: Meghan Grady Published: 2026-03-27T19:27:04Z Content type: opinion Language: en Sources: [DigitalOcean]() Topics: [AI Inference](), [Nvidia](), [Inference Performance](), [Digital Ocean](), [Artificial Intelligence](), [Cloud](), [datacenter]() Tags: [2026](), [agentic](), [agents](), [ai](), [ai-inference](), [ai-ml](), [cost](), [data-center](), [deployment](), [digitalocean](), [infrastructure](), [latency](), [nvidia](), [orchestration](), [product-updates](), [production](), [scale](), [uptime]() ### AI overview This commentary argues that NVIDIA GTC 2026 highlighted a shift from AI model training toward production inference. It discusses the infrastructure needed to operate AI at scale, including latency, reliability, cost per token, time to first token, orchestration, uptime, and broader system optimization beyond accelerators. ### Source excerpt Last week at NVIDIA GTC 2026, one message was clear: AI has moved beyond the training era and into the era of production inference. The conversation was no longer just about building faster chips and smarter models; it was about what it takes to run AI at scale with the latency, reliability, and economics real products demand. Reuters called it an "inference boom," and even the CPU became part of the conversation again as inference workloads push the industry to optimize the full system, not just the accelerator. That shift matters because inference is where AI becomes a business. Training ushered in this wave of AI innovation; inference is what turns that innovation into real products and real customer experiences. It is where cost per token, time to first token, orchestration, and uptime start to matter just as much as model quality. GTC made it clear that the industry is moving beyond chips to the broader operating infrastructure architecture required to support AI-native companies. As inference becomes the operational layer of AI, the conversation has moved toward a cohesive system spanning chips, platforms, models and applications, which maps directly to what customers are asking us for today. Rather than making isolated infrastructure decisions, businesses are seeking ways to run AI in production that manage latency, improve token economics, and reduce operational complexity. This need is especially critical as AI agents evolve from a new application pattern into a core infrastructure requirement, demanding fast, secure systems capable of supporting constant activity and real-world workloads at scale. That is the backdrop for what we announced with NVIDIA last week and the vision for the DigitalOcean Agentic Inference Cloud. Across infrastructure, platform, and deployment, the focus was the same: help AI builders move from experimentation to production with less friction. We introduced a new Richmond data center purpose-built for AI inference, featuring NVIDIA ## 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 Service Level Objectives Align Developers and Product Managers DevFeed: [How Service Level Objectives Align Developers and Product Managers]() Original publisher: [Read original article]() Author: Anis Khan Site Reliability Engineering is my role Cost optimization is my goal GitHub profile Linkedin profile Published: 2026-02-02T00:00:00Z Content type: opinion Language: en Sources: [Trivago]() Topics: [Availability](), [User Experience](), [data]() Tags: [availability](), [developer](), [engineering-culture](), [maintainability](), [opinions](), [outages](), [performance](), [site-reliability-engineering](), [technical](), [uptime]() ### AI overview This article argues that Service Level Objectives (SLOs) can reduce conflict between developers and product managers by creating a shared, data-driven agreement about reliability and user experience. It explains how a 99.9% availability target creates a 43.2-minute monthly downtime budget and shows how that budget can guide decisions about releasing features versus restoring stability. ### Source excerpt It's a scenario developers relate to a little too well. The product manager always comes with more and more feature requests. They also want to release fast by giving a tight deadline. While you... ## Reliability Resolutions: How to build effective reliability programs that won't fade away DevFeed: [Reliability Resolutions: How to build effective reliability programs that won't fade away]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2026-01-21T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Chaos Engineering](), [Availability](), [systems](), [data]() Tags: [availability](), [chaos-engineering](), [data](), [engineering](), [reliability-management](), [systems](), [testing](), [uptime]() ### AI overview This article explains how to build reliability and Chaos Engineering programs that produce lasting results. It recommends aligning reliability work with company goals, assigning ownership, creating repeatable processes, identifying data gaps, and testing specific failure modes on critical systems. Progress can be demonstrated through evidence such as validated failover and achievement of uptime targets. ### Source excerpt We're already almost through January. How are your reliability resolutions faring? Check out these key questions to help you follow-through and build an effective reliability program. ## Reliability lessons from the 2025 Microsoft Azure Front Door outage DevFeed: [Reliability lessons from the 2025 Microsoft Azure Front Door outage]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2025-11-17T00:00:00Z Content type: opinion Language: en Sources: [Gremlin Blog]() Topics: [Azure](), [Microsoft](), [Cloud](), [cloud-infrastructure]() Tags: [2025](), [after-the-retrospective](), [azure](), [backup](), [best-practices](), [cloud](), [infrastructure](), [microsoft](), [outage](), [outages](), [recovery](), [uptime](), [xbox]() ### AI overview This article examines reliability lessons from the October 2025 global outage affecting Microsoft Azure Front Door, Azure Content Delivery Network, and related Microsoft services. It emphasizes customer responsibility for uptime, redundancy, tested failover, and active-active infrastructure. ### Source excerpt In October 2025, Microsoft Azure Front Door and Content Delivery Network suffered a global outage. Find out what your team can do to minimize the impact of similar outages in the future. ## Chaos Engineering works, but it has to scale DevFeed: [Chaos Engineering works, but it has to scale]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2025-10-07T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Chaos Engineering](), [Resilience](), [Testing](), [distributed-systems](), [Availability](), [site-reliability-engineering]() Tags: [availability](), [chaos-engineering](), [outages](), [resilience](), [sre](), [testing](), [tests](), [uptime]() ### AI overview Chaos Engineering can uncover failure modes and help prevent outages, but organization-wide adoption may stall when expertise is concentrated in a small number of teams. The article recommends scaling the practice through standards, validation testing, and reporting so that reliability improvements extend beyond critical services. ### Source excerpt Chaos Engineering effectively improves the reliability of systems, but it can run into snags when you try to scale. Build on Chaos Engineering with these key actions. ## Fix issues faster with Recommended Remediations DevFeed: [Fix issues faster with Recommended Remediations]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2025-08-22T00:00:00Z Content type: release Language: en Sources: [Gremlin Blog]() Topics: [Chaos Engineering](), [Testing]() Tags: [chaos-engineering](), [features](), [gremlin](), [recommendations](), [uptime]() ### AI overview Gremlin's Recommended Remediation analyzes fault-injection test results to identify likely failure causes and suggest ways to address reliability issues. It builds on Experiment Analysis by combining test data, metrics, health checks, and key events, then applies reliability expertise to produce tailored recommendations. ### Source excerpt Recommended Remediation speeds up teams with tailored suggestions to help you address reliability risks before they cause failures. ## Understanding SLAs, SLOs, and SLIs in Service Reliability DevFeed: [Understanding SLAs, SLOs, and SLIs in Service Reliability]() Original publisher: [Read original article]() Author: Abhimanyu Saharan Published: 2025-07-02T00:00:00Z Content type: tutorial Language: en Sources: [Abhimanyu Saharan]() Topics: [site-reliability-engineering](), [Monitoring](), [Latency]() Tags: [examples](), [metrics](), [monitoring](), [performance](), [performance-monitoring](), [software-engineering](), [uptime]() ### AI overview This article explains the differences between SLAs, SLOs, and SLIs using real-world examples, with emphasis on service reliability metrics for uptime, latency, and performance monitoring. ### Source excerpt Learn the difference between SLA, SLO, and SLI with real-world examples. Master key service reliability metrics for uptime, latency, and performance monitoring. [Next page]()