Cloud Monitoring for AWS, Azure, and GCP with OpenObserve

Cloud monitoring has become one of those things every developer cares about yet few actually enjoy setting up. The cloud gives flexibility, speed, and scale, but it also brings complexity. Applications today are made up of hundreds of managed services, APIs, and distributed components that live across multiple layers of the stack.

And while every provider offers a built-in way to monitor resources AWS CloudWatch, Azure Monitor, GCP Operations Suite developers often end up asking the same question:

“How do I see everything that matters, in one place, without juggling three dashboards?”

That’s where OpenObserve fits in. It gives you a way to collect, query, and visualize logs, metrics, and traces from any cloud using a single, developer-friendly platform.

What Cloud Monitoring Actually Means

Cloud monitoring goes beyond simple dashboards and metric charts. It's about gaining insight into how your entire system operates and understanding the real impact on your end users.

Broadly, it covers three layers:

1. Infrastructure Monitoring

This is where most developers start. You track your compute instances, containers, storage volumes, and load balancers the physical and virtual backbone of your application.

Typical signals include:

• CPU, memory, and disk utilization
• Network throughput and packet loss
• Scaling events and health checks

These metrics show you whether your underlying infrastructure can handle your application’s load.

2. Application Monitoring

Once your infrastructure is stable, the next focus is on how the code performs. You track:

• Request rates and latencies (P95/P99)
• Error counts and response statuses
• Throughput and dependency timing

This layer tells you if your users are getting a fast, reliable experience.

3. Service-Level Monitoring

This includes managed services : databases, message queues, APIs, serverless functions, and Kubernetes clusters. Here, you focus on:

• Query durations
• Connection counts and timeouts
• Autoscaling events and cold starts

When viewed together, these signals form a complete picture of your system’s health.

Common Challenges in Cloud Monitoring

Despite access to sophisticated tools, engineering teams consistently encounter several obstacles:

1. Fragmented Data Sources

Your observability stack becomes siloed by design. Logs stream to one platform, metrics flow to another dashboard, and distributed traces live in a third system. Piecing together a complete incident timeline means toggling between multiple interfaces, each with its own query language and authentication flow.

2. Cloud-Specific Tooling

Each cloud provider implements monitoring differently. AWS CloudWatch, Azure Monitor, and Google Cloud Operations use distinct APIs, data formats, and metric namespaces. Multi-cloud deployments multiply this complexity; you end up maintaining separate instrumentation code and learning different mental models for essentially the same task.

3. Alert Fatigue

When everything generates alerts, nothing feels urgent. Teams configure thresholds for dozens or hundreds of metrics, resulting in a constant stream of notifications. Critical issues get buried under minor fluctuations, and engineers start ignoring alerts altogether. The signal-to-noise ratio degrades until monitoring becomes background noise.

4. Limited Correlation

Tracing a single user request as it traverses your microservices architecture remains surprisingly difficult. A failed checkout might involve your API gateway, three backend services, two databases, and a payment provider. Without proper correlation IDs and unified tracing, you see isolated failures in each component but miss the causal chain connecting them.

The core problem isn't lack of visibility, it's absence of context. You can see what happened and when, but understanding why requires manually connecting the dots across fragmented data sources.

Cloud-Native Monitoring: Capabilities and Constraints

AWS CloudWatch

CloudWatch monitors 80+ AWS services natively but shows limitations in cross-service correlation. Key specifications:

• Query engine: CloudWatch Logs Insights with 10,000 events return limit
• Retention: 1 day to indefinite (user-configured), charged per GB stored
• Metric resolution: 1-minute standard, 1-second high-resolution (additional cost)
• Cost structure: $0.50/GB ingested, $0.03/GB stored, $0.30 per custom metric

Architectural constraint: CloudWatch Insights queries scan raw logs rather than indexed data, causing P95 query latencies exceeding 30 seconds for datasets over 1TB. Multi-region deployments require separate dashboards per region, no unified query interface exists.

Azure Monitor

Azure Monitor integrates with 100+ Azure services and supports hybrid cloud scenarios through Arc agents. Technical specifications:

• Query language: Kusto Query Language (KQL) with 500,000 row limit
• Default retention: 30 days (workspace), 90 days (Application Insights)
• Metric granularity: 1-minute for platform metrics, custom intervals for applications
• Pricing: $0.30/GB ingested (first 5GB free), $0.12/GB retention after 31 days

KQL provides faster aggregations than CloudWatch Insights; P95 latency under 5 seconds for queries spanning 100GB. However, Azure Monitor workspaces are region-bound; cross-region analysis requires workspace federation with increased query complexity.

GCP Operations Suite

GCP Operations handles logs, metrics, and traces through Cloud Logging and Cloud Monitoring. Architecture details:

• Query interface: Logs Explorer with MQL (Monitoring Query Language)
• Free tier: First 50GB per project per month
• Retention: 30 days default (logs), 400 days maximum, 6 weeks for metrics
• Cost: $0.50/GB beyond free tier, $0.01/GB archived to Cloud Storage

Operations Suite excels at GKE (Google Kubernetes Engine) observability with automatic trace correlation. Limitation: cross-project queries require BigQuery export, adding $5/TB processing costs and 10-15 minute data lag.

Comparison Table: Cloud-Native Monitoring Tools

Cloud	Built-in Tool	Query Language	Retention	Cross-Region Support	Limitation
AWS	CloudWatch	Logs Insights	1–∞ days	❌	Slow queries >1TB
Azure	Monitor	KQL	30–90 days	⚠️ Federation only	Region-bound
GCP	Operations Suite	MQL	30–400 days	⚠️ via BigQuery	Export lag

Note on Pricing and Figures:

The pricing figures ($0.50/GB,$0.30/GB$, etc.) and constraints (e.g., query limits, performance latencies) are based on the most common Pay-As-You-Go tiers and US-East regions published in the respective cloud provider's public documentation (AWS, Azure, GCP) as of late 2025. These figures are subject to change and should be considered general estimates for comparative analysis. Actual costs will vary significantly based on your organization's specific pricing agreements, region, and commitment tiers.

How OpenObserve Simplifies Cloud Monitoring

OpenObserve is built to make cloud monitoring less painful and more developer-centric.

It acts as a unified observability layer, bringing together all your telemetry whether it comes from AWS, Azure, or your on-prem systems. Everything can be queried using SQL, which makes it intuitive for developers.

Here’s what that means in practice:

• Unified View Across Clouds: Native tools are siloed, CloudWatch only sees AWS, Azure Monitor only sees Azure.OpenObserve ingests from all sources (Firehose, Event Hub, Pub/Sub) and visualizes everything in one dashboard, so teams can compare AWS, Azure, and GCP metrics side by side.
• One Query Language-SQL : Instead of juggling KQL, MQL, and Logs Insights, OpenObserve uses standard SQL for all telemetry: logs, metrics, and traces. Developers don’t need to learn new DSLs or context-switch between tools.
• Faster Queries on Large Datasets: Cloud-native tools often struggle at scale, CloudWatch scans raw logs, and GCP adds export lag. OpenObserve’s indexed columnar storage keeps queries fast even on terabytes of data.
• Cross-Region & Cross-Service Correlation: Native dashboards are region-bound and separate metrics, logs, and traces. OpenObserve links all three with trace IDs, giving you end-to-end visibility across services and geographies.
• Predictable Pricing and Retention: Instead of unpredictable costs per query or GB scanned, OpenObserve offers flat pricing based on data stored with flexible retention per data type (logs, metrics, traces).
• Simple, Vendor-Neutral Setup: Skip multiple agents and configurations. OpenObserve accepts direct streams from any source, works on any cloud, and avoids vendor lock-in , ideal for hybrid and multi-cloud teams.

Quick Comparison: Native Tools vs. OpenObserve

Feature / Capability	Native Cloud Tools (CloudWatch, Azure Monitor, GCP Ops Suite)	OpenObserve
Multi-Cloud Visibility	❌ Limited to their own cloud	✅ Unified dashboard for AWS, Azure, and GCP
Query Language	⚠️ Different DSLs (KQL, MQL, Logs Insights)	✅ One SQL language for all telemetry
Performance at Scale	⚠️ Slows down on large datasets	✅ Columnar indexing ensures fast queries
Cross-Region Correlation	❌ Region-bound dashboards	✅ Full trace correlation across services and applications
Pricing Model	⚠️ Pay-per-query and GB scanned	✅ Transparent, flat-rate storage pricing
Vendor Lock-In	⚠️ Tied to a specific cloud	✅ Works across clouds or on-prem

Monitoring AWS with OpenObserve

AWS users can stream logs and metrics directly into OpenObserve using Firehose, Lambda, or S3.

Once data starts flowing, you can visualize EC2 CPU usage, Lambda duration, or VPC Flow Logs in real time. You can also build SQL-based dashboards for API Gateway latency or RDS query errors ,metrics that otherwise stay buried in CloudWatch.

The best part is that AWS data can live beside your Azure and GCP telemetry, making cross-cloud comparisons effortless.

Monitoring Azure with OpenObserve

Azure users often rely on Azure Monitor and App Insights, but both stop at the platform boundary. With OpenObserve, you can go further.

Logs can be streamed through Azure Diagnostic Settings or Event Hubs, and metrics from Application Insights can be ingested alongside them. Once inside OpenObserve, you can correlate Azure function latency with external dependencies, or view CPU and request patterns.

Monitoring GCP with OpenObserve

For GCP, integration happens via Pub/Sub or Cloud Logging sinks.Once connected, your GCP metrics and logs appear just like any other data source letting you visualize BigQuery performance, API error rates, or instance utilization together with AWS and Azure data.

No translation layers, no extra storage, just one clean observability pipeline.

Explore Related Guides

If you want to go deeper into specific setups, check out:

• Analyzing AWS VPC Flow logs
• Monitor AWS EC2 Logs and Metrcis
• Ingesting AWS CloudWatch Logs into OpenObserve
• Monitoring AWS Cloud Resources with OpenObserve
• Monitoring Azure with OpenObserve
• Streaming GCP Logs Using Pub/Sub
• Ingesting and Analyzing Logs from GoogleCloud Run

Each of these covers real setup steps and sample queries for their respective platforms.

Final Thoughts

Cloud monitoring shouldn’t feel like a chore. Developers deserve clear, unified visibility into how their systems behave, not just dashboards full of disconnected metrics.

OpenObserve makes this easier by connecting all your telemetry into a single, queryable platform. You don’t need to learn three tools or jump between consoles just one space where your logs, metrics, and traces all make sense together.

Whether you’re running on Azure, AWS, or anywhere else ; monitoring should feel simple, not scattered.

Get Started with OpenObserve Today! Sign up for a free cloud trial.

FAQs: Cloud Monitoring with OpenObserve

What is cloud monitoring?

Cloud monitoring is the practice of collecting and analyzing logs, metrics, and traces from your cloud infrastructure and applications to ensure availability, performance, and reliability. It helps you detect issues early and understand their root cause across distributed systems.

Why is unified cloud monitoring important?

Most teams use multiple clouds AWS for compute, Azure for identity, GCP for data. Native monitoring tools like CloudWatch, Azure Monitor, and Cloud Operations work well individually, but they don’t talk to each other. A unified solution like OpenObserve gives you one place to analyze all telemetry, eliminating blind spots and reducing context-switching.

Can I monitor AWS, Azure, and GCP together using OpenObserve?

Yes. OpenObserve lets you ingest and visualize data from all major cloud providers. You can forward logs via AWS Firehose, Azure Event Hubs, or GCP Pub/Sub and query everything using SQL.

How is OpenObserve different from CloudWatch or Azure Monitor?

OpenObserve isn’t tied to any single cloud. Unlike CloudWatch or Azure Monitor, it provides a unified query layer, supports all telemetry types (logs, metrics, traces), and uses SQL instead of custom query languages. That means less time learning DSLs and more time analyzing data.