Cloud Asset Inventory
What Is Cloud Observability? A Complete Guide
What Is Cloud Observability? #
Cloud observability is the ability to understand what's happening inside your cloud infrastructure by analyzing external signals: metrics, logs, and traces. Where monitoring tells you something broke, observability helps you figure out why it broke and what else might be affected.
The distinction matters in practice. Traditional monitoring works well when you know what to watch for. You set a threshold on CPU usage, and you get an alert when it spikes. That approach falls apart in distributed cloud environments where you don't always know which question to ask.
Consider a single user request that passes through an API gateway, hits three microservices, queries two databases, and writes to a message queue. When that request times out, monitoring tells you the request failed. Observability lets you trace the request across every service it touched and identify that the bottleneck was a cold start in a downstream Lambda function.
The three core signals that power cloud observability are:
- Metrics - Numerical measurements collected over time: request latency, error rates, CPU usage, memory consumption. Metrics tell you the "what" at a glance.
- Logs - Timestamped records of discrete events. Every time a service processes a request, throws an error, or completes a task, it writes a log entry. Logs give you the "when" and the "where."
- Traces - Records of a request's path through your distributed system. A trace connects the dots between services, showing you exactly which component introduced latency or errors.
These three signals work together. Metrics surface the symptoms, logs provide context, and traces map the path. Without all three, you're working with partial information. We've written a deep technical breakdown of each signal in our guide to cloud observability pillars, technologies, and practices.
In this article:
Why Does Cloud Observability Matter? #
Cloud observability matters because the infrastructure you need to troubleshoot might not exist by the time you start looking. Resources spin up and down automatically. Containers live for minutes. Serverless functions exist only during execution. A Kubernetes cluster might scale from 10 pods to 200 pods in the span of a traffic spike, and those original 10 pods might already be gone when you investigate the incident.
Cloud observability captures enough context while resources are alive that you can still reconstruct what happened after they're terminated. That's a capability traditional monitoring tools were never designed to provide.
The business case is straightforward. According to Gartner, the average cost of IT downtime ranges from $5,600 to over $300,000 per hour depending on the industry. Observability reduces mean time to detection (MTTD) and mean time to resolution (MTTR) by giving teams the data they need to find root causes without guessing.
Beyond incident response, observability supports capacity planning, performance optimization, and cost management. When you can see which services consume the most compute or which API endpoints have the highest latency, you can make informed decisions about where to invest engineering time. We've seen teams cut their cloud spend by 15-20% after gaining real visibility into resource utilization patterns they couldn't see before.
How Is Cloud Observability Different from Cloud Monitoring? #
Cloud monitoring detects known problems; cloud observability helps you investigate unknown ones. The two concepts overlap, but they serve different purposes.
Monitoring is a subset of observability. You still need dashboards, alerts, and health checks. But in a cloud-native environment with dozens of services, monitoring alone leaves gaps. A service might be "healthy" according to its health check endpoint while silently dropping 2% of requests due to a misconfigured connection pool. Observability surfaces those kinds of issues because it lets you ask arbitrary questions about system behavior rather than relying on pre-defined checks.
You don't need to choose one or the other. Most teams start with monitoring (it's where the immediate fires are) and layer in observability practices as their architecture grows more complex. The cloud observability tools available today often combine both capabilities in a single platform.
What Are the Key Components of Cloud Observability? #
A complete cloud observability practice goes beyond the three core signals (metrics, logs, traces) and includes several supporting components.
Instrumentation #
Observability starts with instrumentation: getting your applications and infrastructure to emit the right signals. This means adding tracing libraries to your application code, configuring structured logging, and exposing metrics endpoints. OpenTelemetry has become the de facto standard for instrumentation. It provides vendor-neutral APIs and SDKs for generating and exporting telemetry data in most popular languages.
The practical benefit of OpenTelemetry is portability. If you instrument with OTel, you can send data to Datadog today and switch to Grafana Cloud next quarter without re-instrumenting your code. That flexibility matters more than most teams realize when they're first getting started.
Context Enrichment and Asset Visibility #
Raw telemetry data is only half the picture. Knowing that
pod-xyz-123 had a memory spike doesn't help much if you don't know which service runs in that pod, which team owns it, or which cloud account it sits in. Context enrichment ties telemetry to infrastructure metadata: resource tags, ownership, region, account, service dependencies.This is where cloud asset management and observability intersect. An accurate, up-to-date inventory of your cloud resources provides the context layer that makes raw telemetry actionable. When your observability platform can correlate a latency spike with a recent deployment in a specific account owned by a specific team, you've gone from "something is slow" to "here's who needs to look at this and here's what changed."
Correlation and Analysis #
Collecting data from many sources isn't useful without the ability to correlate it. A good observability setup lets you start from an alert, jump to the relevant traces, see the associated logs, and overlay infrastructure metrics, all in a single investigation flow. This is the difference between spending 20 minutes switching between tabs in three different tools and finding the root cause in five minutes.
Correlation also spans across environments. If your organization runs workloads in AWS and Google Cloud (and most enterprises do), you need your observability data normalized and queryable across both providers. Our guide on multi-cloud observability covers the specific challenges and strategies for this.
Dashboards and Alerting #
Visualization ties everything together. Dashboards should surface the metrics that matter for each team: SLOs for the platform team, error rates for the application team, cost trends for FinOps. Alerting should be tuned carefully. Too many alerts and the team ignores them; too few and real issues slip through.
The best observability setups we've seen use SLO-based alerting rather than threshold-based. Instead of alerting when CPU hits 80%, alert when the error budget for your 99.9% availability SLO is being consumed faster than expected. This approach reduces noise and focuses the team on what actually affects users.
How Do You Implement Cloud Observability? #
There's no single right way to do this, but the general progression we've seen work well follows a pattern.
Start with what you have. Most cloud providers include basic observability tooling: AWS CloudWatch, Google Cloud Monitoring, Azure Monitor. These tools provide metrics and logs out of the box for managed services. If you haven't centralized these yet, that's step one.
Add structured logging. Unstructured log messages ("Error processing request") are nearly useless at scale. Switch to structured JSON logging with consistent fields:
timestamp, service, request_id, user_id, error_code. This one change makes your logs searchable and lets you correlate them with traces and metrics.Instrument for tracing. Start with your most critical user-facing flows. If you run a SaaS platform, instrument the login flow, the main CRUD operations, and payment processing first. OpenTelemetry's auto-instrumentation can get you basic coverage with minimal code changes.
Centralize and correlate. Pick a platform (or build your own with Prometheus + Grafana + Jaeger) that lets you correlate metrics, logs, and traces. The tool itself matters less than having everything in one place.
Build your asset inventory. Parallel to the above, build and maintain an inventory of your cloud resources. Know what's running, where, and who owns it. This provides the context layer that makes the rest of your observability data useful. Tagging your cloud resources consistently is a prerequisite for making this work at scale.
Iterate. Observability is not a one-time project. Every incident should teach you something about what signals were missing. Add instrumentation as you learn where the gaps are.
What Are the Biggest Challenges? #
Data Volume and Cost #
Cloud applications generate an enormous amount of telemetry. A moderately sized Kubernetes cluster can produce gigabytes of logs per day. Storing and querying all of that data gets expensive fast. Teams need to make intentional decisions about retention policies, sampling rates, and which data to keep at full fidelity versus aggregated.
Tool Sprawl #
The average enterprise uses somewhere between 10 and 20 monitoring and observability tools. Each team picks the tool that fits their use case, and suddenly you have Datadog for APM, CloudWatch for infrastructure, PagerDuty for alerting, a custom Grafana setup for the data team, and nobody can trace a request end to end.
Alert Fatigue #
When everything is instrumented, everything can alert. Without careful tuning, teams drown in notifications and start ignoring them, which defeats the purpose entirely. Moving to SLO-based alerting and ensuring each alert has a clear owner and runbook helps, but it requires ongoing discipline.
Multi-Cloud Complexity #
Each cloud provider exposes telemetry differently. AWS CloudWatch metrics use different naming conventions than Google Cloud Monitoring, and Azure Monitor has its own schema. Normalizing this data into a consistent format that you can query across providers is a real engineering challenge. The alternative is maintaining separate observability stacks per cloud, which creates exactly the kind of siloed visibility that observability is supposed to fix.
Closing the Observability Gap with CloudQuery #
Every observability practice has the same blind spot: you can't observe what you don't know exists. Teams instrument the services they know about, monitor the resources they remember deploying, and build dashboards for the infrastructure they think they have. The gap between what's actually running in your cloud and what's being observed is where incidents hide.
CloudQuery closes that gap. By syncing configuration data from AWS, Google Cloud, Azure, and 70+ other Sources into a SQL-queryable database, CloudQuery gives your team a complete picture of what's deployed across every account and region. That inventory becomes the foundation for answering observability-specific questions:
- Which EC2 instances are running without a monitoring agent installed? A SQL query across your CloudQuery inventory can surface unmonitored compute resources in seconds.
- Which services were deployed in the last 48 hours without proper resource tags? Untagged resources are invisible to your observability platform's cost attribution and ownership mapping.
- Are there load balancers, databases, or queues that don't appear in any of your dashboards? If a resource isn't in your dashboards, it's not being watched. CloudQuery finds what your monitoring missed.
CloudQuery syncs this data into PostgreSQL, Snowflake, BigQuery, or the destination your team already uses, so you can query it alongside your existing metrics and operational data. Combined with CloudQuery Policies, you can set up continuous checks that catch observability gaps as they appear: a new RDS instance without CloudWatch alarms, an EKS cluster missing its Prometheus scrape configuration, or a Lambda function with no tracing enabled.
The result is an observability practice that doesn't depend on engineers remembering to instrument every new resource. CloudQuery surfaces the gaps automatically, so your team can fix them before they cause incidents.
See how CloudQuery works with your cloud or browse our Source integrations to check coverage for your stack.
Cloud Observability FAQ #
What are the three pillars of cloud observability? #
The three pillars are metrics, logs, and traces. Metrics are numerical measurements (like CPU usage or request latency), logs are timestamped event records, and traces track a request's path across distributed services. Together, they provide the data needed to understand system behavior. For a deep technical breakdown of each pillar, see our guide to cloud observability pillars and practices.
How is cloud observability different from cloud monitoring? #
Monitoring detects known problems using predefined checks and thresholds. Observability lets you investigate unknown problems by correlating metrics, logs, and traces to answer open-ended questions about system behavior. Monitoring tells you that something is wrong; observability helps you understand why.
What is OpenTelemetry and why does it matter for cloud observability? #
OpenTelemetry (OTel) is a vendor-neutral open standard for generating and collecting telemetry data. It provides APIs and SDKs for most programming languages. The key benefit is portability: instrument your code once with OTel, and you can send data to any compatible backend (Datadog, Grafana, New Relic, etc.) without re-instrumenting if you switch tools.
What tools are used for cloud observability? #
Common tools include Datadog, Grafana Cloud, New Relic, Dynatrace, Splunk Observability, Elastic Observability, and Prometheus (open source). Cloud providers also offer native tooling: AWS CloudWatch, Google Cloud Monitoring, and Azure Monitor. Most organizations use a combination. See our full comparison of the top cloud observability tools.
How does cloud asset management relate to observability? #
Cloud asset management provides the context layer that makes observability data actionable. Knowing that a pod is using high memory is only useful if you also know which service it belongs to, which team owns it, and which cloud account it runs in. An accurate asset inventory ensures your observability coverage doesn't have blind spots from unknown or unmonitored resources.
What is the difference between observability and APM? #
Application Performance Monitoring (APM) focuses on application-level metrics: response times, error rates, transaction traces, and code-level profiling. Observability is broader, covering infrastructure, networking, and cross-service behavior in addition to application performance. APM is one input to an observability practice, not a replacement for it.
How do you handle observability in multi-cloud environments? #
Multi-cloud observability requires normalizing telemetry data from different providers into a consistent format. Each cloud uses different metric names, log structures, and API schemas. Tools that support OpenTelemetry and multi-cloud ingestion help, but the data normalization challenge remains significant. We cover this in depth in our guide to multi-cloud observability.
How much does cloud observability cost? #
Costs vary widely based on data volume, retention, and tooling choices. Most commercial observability platforms price by data ingestion (per GB of logs, per host for metrics, per span for traces). A mid-sized deployment (50-100 services) can range from $5,000 to $50,000+ per month depending on the vendor and data volume. Open-source stacks (Prometheus + Grafana + Jaeger) reduce licensing costs but require engineering time to operate.