Understanding System Observability

🚀 Building Observable Infrastructure: Why Automation + Instrumentation = Production Excellence and Customer Success After building our platform's infrastructure and application automation pipeline, I wanted to share why combining Infrastructure as Code with deep observability isn't optional—it's foundational as shown in screenshots implemented on Google Cloud. The Challenge: Manual infrastructure provisioning and application onboarding creates consistency gaps, slow deployments, and zero visibility into what's actually happening in production. When something breaks at 3 AM, you're debugging blind. The Solution: Modular Terraform + OpenTelemetry from Day One with our approach centered on three principles: 1️⃣ Modular, Well architected Terraform modules as reusable building blocks. Each service (Argo CD, Rollouts, Sonar, Tempo) gets its own module. This means: 1. Consistent deployment patterns across environments 2. Version-controlled infrastructure state 3. Self-service onboarding for dev teams 2️⃣ OpenTelemetry Instrumentation of every application during onboarding as a minimum specification. This allows capturing: 1. Distributed traces across our apps / services / nodes (Graph) 2. Golden signals (latency, traffic, errors, saturation) 3. Custom business metrics that matter. 3️⃣ Single Pane of Glass Observability Our Grafana dashboards aggregate everything: service health, trace data, build pipelines, resource utilization. When an alert fires, we have context immediately—not 50 tabs of different tools. Real Impact: → Application onboarding dropped from days to hours → Mean time to resolution decreased by 60%+ (actual trace data > guessing) → nfrastructure drift: eliminated through automated state management → Dev teams can self-service without waiting on platform engineering Key Learnings: → Modular Terraform requires discipline up front but pays dividends at scale. → OpenTelemetry context propagation consistent across your stack. → Dashboards should tell a story by organising by user journey. → Automation without observability is just faster failure. You need both. The Technical Stack: → Terraform for infrastructure provisioning → ArgoCD for GitOps-based deployments → OpenTelemetry for distributed tracing and metrics → Tempo for trace storage → Grafana for unified visualisation The screenshot shows our command center : → Active services → Full trace visibility → Automated deployments with comprehensive health monitoring. Bottom line: Modern platform engineering isn't about choosing between automation OR observability. It's about building systems where both are inherent to the architecture. When infrastructure is code and telemetry is built-in, you get reliability, velocity, and visibility in one package. Curious how others are approaching this? What's your observability strategy look like in automated environments? #DevOps #PlatformEngineering #Observability #InfrastructureAsCode #OpenTelemetry #SRE #CloudNative

Most engineers obsess over which model to use. GPT-4o or Claude. Benchmarks. Latency. Cost. They ship. The system breaks. And they have no idea why. Because model choice is a one-time decision. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 is what tells you what is actually happening after that. Without traces, 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗴𝘂𝗲𝘀𝘀𝗶𝗻𝗴. You do not know: • if retrieval is returning the wrong chunks • if the model is ignoring context • if certain queries are quietly failing You just see bad outputs. I have seen teams spend days swapping models trying to fix this. The issue is almost never the model. One team I worked with had a broken chunking strategy. Sentences were getting split mid-thought. The model never even saw complete context. With proper tracing, we found it in 20 minutes. That is the difference. Model upgrades change behavior. Sometimes they fix things. Sometimes they just move the failure somewhere else. Without evals and traces, you cannot tell which one happened. The teams that move fastest in production are not the ones picking the “best” model. They are the ones who can: • trace a request end-to-end • inspect retrieval outputs • evaluate whether responses are actually grounded 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗮 𝘀𝘆𝘀𝘁𝗲𝗺. 𝗡𝗼𝘁 𝗮 𝗴𝘂𝗲𝘀𝘀. Observability is not overhead. It is your ability to improve anything after you ship. Pick your model. Then instrument everything. If you are building with LLMs and you do not have this setup yet, 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗱𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗯𝗹𝗶𝗻𝗱. I break this down a lot more in my content.

Observability in Spring Boot & Spring AI: Designing Systems That Explain Themselves Modern applications are no longer simple request-response systems. With Spring Boot + Spring AI, applications interact with APIs, databases, external services, LLMs, and AI agent workflows. The challenge is not only building intelligent systems — it is understanding what happens inside them. How do we answer: 🔹 Why is an API slow? 🔹 Which service increased latency? 🔹 How long did an AI model call take? 🔹 Which agent step failed? 🔹 How are tokens and external calls impacting performance? This is where observability architecture becomes critical. There are multiple ways to integrate observability: 1️⃣ Application-Level Instrumentation Add observability directly into applications using: ✅ Spring Actuator ✅ Micrometer ✅ OpenTelemetry SDK ✅ Datadog Libraries / Java Agent Benefits: • Custom business metrics • AI workflow visibility • API performance tracking • LLM latency and token monitoring Example metrics: Request duration Agent execution time Model response latency External API failures 2️⃣ Micrometer + OpenTelemetry Bridge Spring Boot commonly uses Micrometer as an abstraction layer. Flow: Spring Boot Application ⬇️ Micrometer ⬇️ OpenTelemetry Bridge ⬇️ OpenTelemetry Collector ⬇️ Datadog / Prometheus / Grafana Benefits: ✅ Spring-native integration ✅ Open standards ✅ Flexible monitoring backend 3️⃣ OpenTelemetry SDK A vendor-neutral approach for collecting: ✅ Metrics ✅ Logs ✅ Distributed traces Instrument once and send telemetry to different platforms. 4️⃣ Java Agent Approach Agents like: 🔹 Datadog Java Agent 🔹 OpenTelemetry Java Agent provide automatic instrumentation with minimal code changes. They capture: • HTTP calls • Database operations • JVM metrics • Distributed traces 5️⃣ Sidecar Pattern Observability can run separately beside applications. Example: Spring Boot Service ⬇️ OpenTelemetry Collector Sidecar ⬇️ Datadog / Monitoring Platform Benefits: ✅ Less application code ✅ Centralized configuration ✅ Independent scaling 6️⃣ Kubernetes DaemonSet Pattern In Kubernetes environments, collectors can run as DaemonSets. Each node runs an observability collector to gather telemetry from workloads. Benefits: ✅ Infrastructure visibility ✅ Scalable collection ✅ Consistent cluster monitoring For Spring AI applications: User Request ⬇️ Spring Boot API ⬇️ AI Agent Workflow ⬇️ LLM Call ⬇️ External Tools ⬇️ Response Every step should be traceable and measurable. The goal is not choosing one tool. The goal is creating a flexible observability strategy: Build → Instrument → Observe → Optimize → Scale #SpringBoot #SpringAI #OpenTelemetry #Micrometer #Datadog #Observability #Java #Kubernetes #CloudNative #AIEngineering

Observability is no longer just dashboards, alerts, and colorful graphs. In 2026, the best engineering teams are asking a deeper question: Can our observability stack help us understand, decide, and act faster when systems fail? A strong observability strategy now needs 6 layers: 1. Collect Use OpenTelemetry, exporters, agents, structured logs, metrics, traces, and context propagation. 2. Visualize Give teams dashboards for service health, infrastructure, user experience, latency, errors, saturation, and SLOs. 3. Analyze Use platforms like Datadog, Dynatrace, New Relic, Grafana Cloud, Splunk, Elastic, Honeycomb, or Sentry to detect patterns and root causes. 4. Respond Integrate with ServiceNow, PagerDuty, Opsgenie, Slack, Teams, and incident workflows so alerts become accountable actions. 5. Automate Connect trusted runbooks, approvals, Ansible, GitHub Actions, Azure Automation, or ServiceNow flows to reduce manual toil. 6. Improve Feed learnings back into SLOs, capacity planning, FinOps, resilience engineering, release governance, and platform design. Datadog is strong for cloud-native teams needing broad integrations and fast SaaS adoption. Dynatrace is powerful for enterprises needing dependency mapping, AI RCA, and hybrid estate visibility. New Relic is developer-friendly and useful for teams that want performance insight. Grafana is ideal when open-source flexibility, Prometheus, Loki, Tempo, and cost control matter. Splunk fits organizations where logs, security, IT operations, and SIEM alignment are important. Elastic works well for search-heavy logs and observability plus security use cases. Honeycomb helps teams debug high-cardinality distributed systems. Sentry is excellent for developer error tracking, exceptions, and code-level visibility. My practical view: Do not start with the tool. Start with the operating model. What are your critical services? What SLOs matter? What telemetry do you really need? Who owns incidents? Which runbooks can be automated? Where do humans need approval? How will you control telemetry cost? The best stack is not one product. It is a connected operating system: OpenTelemetry for collection. Observability platform for analysis. ITSM for accountability. Automation for action. SRE practices for improvement. AI for faster reasoning and noise reduction. For enterprise managed services, the winning architecture is: Visibility + Context + Correlation + Workflow + Automation + Governance. When these come together, observability becomes much more than monitoring. It becomes the nervous system of digital operations. The future belongs to teams that observe clearly, understand quickly, act safely, and keep improving. #Observability #AIOps #SRE #DevOps #OpenTelemetry #Grafana #Datadog #Dynatrace #NewRelic #Splunk #Elastic #CloudNative #PlatformEngineering #AIEngineering #ReliabilityEngineering #IncidentManagement #Automation #TechLeadership

𝐌𝐮𝐥𝐭𝐢-𝐂𝐥𝐨𝐮𝐝 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐢𝐬 𝐰𝐡𝐞𝐫𝐞 𝐦𝐨𝐬𝐭 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 𝐟𝐚𝐢𝐥.  Here's the cheatsheet that saved our team from monitoring chaos. Managing AWS, Azure, and GCP isn't about using each cloud's native tools. It's strategic standardization where it matters. 𝐌𝐲 𝐛𝐚𝐭𝐭𝐥𝐞-𝐭𝐞𝐬𝐭𝐞𝐝 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡: 𝟏. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: • Native: Lambda, Azure Functions, Cloud Functions • Unified: Jenkins, Ansible for cross-cloud pipelines 𝟐. 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧: • Native: CloudWatch, Azure Monitor, Cloud Logging for infrastructure • Unified: Prometheus, Fluentd for applications • Critical: Route native metrics to central systems, don't duplicate 𝟑. 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: • Grafana for everything—stop using different dashboards per cloud • Tableau, Metabase for business intelligence 𝟒. 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: • Terraform for multi-cloud IaC—non-negotiable • Native DevOps services or Jenkins for consistency 𝟓. 𝐀𝐥𝐞𝐫𝐭𝐢𝐧𝐠: • Native alerts for infrastructure • PagerDuty or Slack for unified incident response 𝟔. 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: • Kibana + Grafana for correlation across clouds • Native tools for cloud-specific deep dives My Recommendations: DO: • Use native tools for infrastructure monitoring • Centralize application observability (Prometheus + Grafana) • Route all alerts through single platform • Standardize on Terraform for IaC DON'T: • Build custom observability platforms • Ignore cloud-native capabilities • Use different dashboards per cloud • Replicate data—use query federation Truth: Multi-cloud observability fails when teams standardize everything OR use only native tools. Winning strategy is hybrid—native for infrastructure, unified for applications. What's your Multi-Cloud stack? ♻️ Repost if you found it valuable ➕ Follow Jaswindder for more insights on Cloud Strategy, DevOps, and AI-led Engineering. #DevOps #CloudEngineering #Observability 

Here's what I've learned from building 5 LLM-related AI projects in 12 months: Architecting the observability pipeline is harder than building the product. But it’s absolutely critical. When you tweak a prompt, add a new feature, or fix a bug, you need to be certain you haven’t broken anything else. Constantly testing this manually is a huge waste of time and energy. So, what's the workaround? In the PhiloAgents project, we tackled this challenge by building a robust observability pipeline that unifies monitoring and evaluation into a single, cohesive system. We use Opik (by Comet) to: • Monitor prompt usage and versioning live • Capture detailed traces of user inputs, agent actions, tool calls, and outputs • Track key latency metrics like Time to First Token and Tokens per Second • Run offline evaluations with LLM-as-a-Judge on curated datasets • Store evaluation metadata for tracking performance over time The pipeline consists of two main parts: 𝗢𝗻𝗹𝗶𝗻𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲: Gives real-time visibility into agent behavior, helping us quickly identify bugs, regressions, or performance issues during production. 𝗢𝗳𝗳𝗹𝗶𝗻𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲: Runs systematic, reproducible tests to measure overall agent quality, track trends, and validate improvements before deployment. Together, these pipelines create a feedback loop that supports both immediate debugging and long-term continuous improvement. If you want to build AI agents that survive beyond proofs of concept and scale reliably in production, mastering observability is non-negotiable. For a practical deep dive into how we architected this system, check out Lesson 5 of the PhiloAgents course. 🔗 Here's the link: https://lnkd.in/dRYgHyid

Buying an observability platform doesn't give you observability. Just like buying a gym membership doesn't make you fit. Tools matter, but observability is a system made up of people, processes, and instrumentation. It requires consistency, conventions, and collaboration across teams. Observability becomes a system when you have: 🔹 Instrumentation discipline: Services emit structured, meaningful telemetry and not whatever each developer prefers. 🔹 Semantic conventions: Attributes, span names, and error formats are consistent across services. 🔹 A reliable pipeline: OpenTelemetry Collectors route data predictably and safely. 🔹 Operational workflows: Engineers know how to investigate outages, not just where to click. 🔹 Ownership: Teams maintain what they instrument and review observability as part of delivery. Without these pieces, even the best tool becomes little more than a data sink. 🧩 Example: When Observability Fails as a Tool Imagine a company buys a premium observability platform. They hook up a few logs and metrics. Dashboards are created. Alerts are set. Then an incident happens. Engineers jump into dashboards and see CPU spikes but no correlated traces. They search logs, but every service logs differently. They pull up metrics, but have no context for which user flows are impacted. Everyone spends hours guessing. Why? Because they bought a tool, but never built a system. 🧩 Example: When Observability Works as a System Another team invests in: • Consistent OTel instrumentation across services • Shared semantic conventions • A unified collector pipeline • Playbooks for incident response • Regular observability reviews in sprint cycles When something breaks, engineers instantly see: • The failing service • The impacted user flows • The exact span where latency spikes began • Related logs with matching attributes • Recent deployments that touched that code path They don't just detect the issue, they understand it. That's observability as a system. 🎯 Bottom Line Observability isn't what you buy. It's what you build over time. Tools give you capabilities. Systems give you outcomes. 💬 How have you built observability beyond just tools in your organization? #Observability #OpenTelemetry #PlatformEngineering #SRE #O11yEngineering

Circa 2012-14, at a FAANG company (can’t pin-point for obvious reason 😉), we once faced a choice that could have cost MILLIONS in downtime… 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐰𝐞 𝐝𝐢𝐝. A critical system update was set to go live. Everything was tested, reviewed, and ready. Until a last-minute test showed an unusual error. 𝐍𝐨𝐰 𝐰𝐞 𝐡𝐚𝐝 𝐭𝐰𝐨 𝐨𝐩𝐭𝐢𝐨𝐧𝐬: ↳ Push ahead and risk an outage that could cost millions per minute. ↳ Roll back and delay a major feature for weeks. 𝐍𝐞𝐢𝐭𝐡𝐞𝐫 𝐟𝐞𝐥𝐭 𝐫𝐢𝐠𝐡𝐭. So we took a smarter approach. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐰𝐞 𝐝𝐢𝐝: ➡️ 1. Instead of an all-or-nothing launch, we released to 0.1% of our traffic first. If things went sideways, we could shut it down in real time. ➡️ 2. Pre-prod tests only catch what they’re designed to catch—but production is unpredictable. We used synthetic traffic to simulate real-user behavior in a controlled environment. ➡️ 3. We didn’t just have one rollback plan — 𝐰𝐞 𝐡𝐚𝐝 𝐭𝐡𝐫𝐞𝐞: App-layer toggle – Immediate rollback for end-user impact. Traffic rerouting – Redirecting requests to stable older versions if needed. DB versioning – Avoiding schema lock-in with backwards-compatible updates. ➡️ 4. We set up live telemetry dashboards tracking error rates, latencies, and key business metrics—so we weren’t reacting blindly. ➡️ 5. Before the rollout, we ran a “what-if” drill: If this update fails, how will it fail? This helped us build mitigation paths before they were needed. 𝐖𝐡𝐚𝐭 𝐇𝐚𝐩𝐩𝐞𝐧𝐞𝐝? The anomaly we caught in testing never materialized in production. If we had rolled back, we’d have wasted weeks fixing a non-issue. Most teams still launch software with an “all or nothing” mindset. But controlled rollouts, kill switches, and real-time observability can let you ship fast and safe—without breaking everything. How does your team handle high-risk deployments? Would love to hear that 🙂

I recently had the opportunity to work with a large financial services organization implementing OpenTelemetry across their distributed systems. The journey revealed some fascinating insights I wanted to share. When they first approached us, their observability strategy was fragmented – multiple monitoring tools, inconsistent instrumentation, and slow MTTR. Sound familiar? Their engineering teams were spending hours troubleshooting issues rather than building new features. They had plenty of data but struggled to extract meaningful insights. Here's what made their OpenTelemetry implementation particularly effective: 1️⃣ They started small but thought big. Rather than attempting a company-wide rollout, they began with one critical payment processing service, demonstrating value quickly before scaling. 2️⃣ They prioritized distributed tracing from day one. By focusing on end-to-end transaction flows, they gained visibility into previously hidden performance bottlenecks. One trace revealed a third-party API call causing sporadic 3-second delays. 3️⃣ They standardized on semantic conventions across teams. This seemingly small detail paid significant dividends. Consistent naming conventions for spans and attributes made correlating data substantially easier. 4️⃣ They integrated OpenTelemetry with Elasticsearch for powerful analytics. The ability to run complex queries across billions of spans helped identify patterns that would have otherwise gone unnoticed. The results? Mean time to detection dropped by 71%. Developer productivity increased as teams spent less time debugging and more time building. They could now confidently answer "what's happening in production right now?" Interestingly, their infrastructure costs decreased despite collecting more telemetry data. The unified approach eliminated redundant collection and storage systems. What impressed me most wasn't the technology itself, but how this organization approached the human elements of the implementation. They recognized that observability is as much about culture as it is about tools. Have you implemented OpenTelemetry in your organization? What unexpected challenges or benefits did you encounter? If you're still considering it, what's your biggest concern about making the transition? #OpenTelemetry #DistributedTracing #Observability #SiteReliabilityEngineering #DevOps

You’re not ready for K8s observability until you separate logs from metrics. Most teams jump into Prometheus + Grafana and wonder why things feel noisy, slow, or expensive. TL;DR that actually works in prod: Two problems, two pipelines. • Logs = what happened (events, errors) • Metrics = how it’s performing (rates, latency, saturation) Logs pipeline (scale-friendly): Pod → collector (Fluent Bit/Otel) → CloudWatch (landing) → Lambda (normalize/enrich) → Kinesis Firehose (batch) → OpenSearch (hot, 7d) → S3 (cold, years) Why: fast search + sensible cost + compliance. Metrics pipeline (reliable by design): App exposes /metrics → Prometheus scrapes via ServiceMonitors (not push) → Grafana visualizes (mix with CloudWatch/OpenSearch for one pane). Keep Prom retention short; archive with Thanos if you need long history. What to get right: Instrument your app (counters, histograms) before you chase dashboards. Use ServiceMonitors for auto-discovery; 30s scrape is a sane default. Treat CloudWatch as the ingest/bridge, not your search engine at scale. Define SLOs (latency, availability) and tie alerts to error budget burn, not single noisy metrics. Start with community Grafana dashboards, then customize for your domain. Starter checklist: Std log format (ts, level, svc, trace_id). Sidecar or node collector—pick one and stick to it. Normalize logs before index (Lambda/Otel proc). Hot (OpenSearch 7d) / Warm (CloudWatch 30d) / Cold (S3) retention. App metrics: req rate, errors, duration (p50/p95/p99), queue depth, saturation. Alerts on SLO burn, not raw CPU. Question: If you had to cut one thing today to reduce observability cost without losing insight, what would it be—log retention, label cardinality, or scrape frequency? #Kubernetes #Observability #Prometheus #Grafana #OpenSearch #FluentBit #OpenTelemetry #SRE #DevOps #EKS #CloudWatch #Kinesis #Thanos #SLO #ErrorBudgets #PlatformEngineering