AWS CloudWatch is a widely used observability tool that comes built into AWS. It provides easy access to logs, metrics, and alarms, making it a convenient choice for teams monitoring AWS workloads.
But while CloudWatch offers a lot of power, many teams unknowingly misconfigure or misuse it, leading to unexpected costs, limited visibility, and operational challenges.
Here are some common pitfalls we see—and how to avoid them.
Table of Contents
1. Cost Complexity & Unexpected Bills
CloudWatch pricing is often misunderstood, and many teams turn it on and forget about it—until they see a spike in their AWS bill.
- Custom metrics get expensive fast – CloudWatch charges $0.30 per metric per month, and each additional dimension (e.g., InstanceId, Region) counts as a separate metric. For microservices-based architectures, this can add up quickly.
- Log ingestion and storage fees scale unpredictably – AWS charges $0.50 per GB ingested, plus additional costs for indexing, archival, and retrieval. Without proper retention settings, storage costs can spiral out of control.
- Query execution costs sneak up – Running queries isn’t free. AWS charges $0.005 per query in Logs Insights, which adds up fast in high-traffic environments
💡 How to use CloudWatch more efficiently:
- Regularly audit your custom metrics – Ensure you’re only tracking the dimensions you truly need.
- Optimize log retention – Move old logs to an archival/storage option more suitable for long term retention or apply retention filters to avoid keeping unnecessary data.
- Monitor query execution costs – Depending on the Log analysis use case, consider using Athena on archived logs instead of running expensive live queries.
2. Limited Query & Analysis Capabilities
CloudWatch Logs Insights offers basic querying, but it has limitations when it comes to deep analysis and troubleshooting.
- Query language is proprietary – Unlike solutions like OpenSearch, CloudWatch uses a custom syntax, making complex queries harder to write.
- Limited multi-log stream correlation – CloudWatch queries are scoped per log group, making it difficult to trace issues across multiple services.
- Query performance slows with scale – Large-scale queries take longer to execute, especially with high log volumes.
💡 How to improve CloudWatch querying:
- Use structured logging – Standardize logs so they’re easier to parse and filter.
- Pre-filter logs before ingestion – AWS charges per GB, so avoid ingesting low-value logs (e.g., debug logs in production).
- Use CloudTrail for API-level auditing – It’s often more efficient than querying logs for AWS event tracking.
3. UI & Dashboard Limitations
CloudWatch’s dashboarding capabilities aren’t as flexible as other observability tools.
- Limited customization – Unlike Grafana, CloudWatch dashboards have fixed layouts that can feel restrictive.
- Delayed metric updates – Standard metrics refresh every 1-5 minutes, making it difficult to monitor real-time incidents.
- Cross-service views require manual setup – Correlating logs, metrics, and traces requires additional AWS services like X-Ray.
💡 How to improve CloudWatch dashboards:
- Use Grafana for visualization – AWS provides a CloudWatch Data Source for Grafana, which gives more flexibility.
- Enable high-resolution metrics (HRM) – If you need sub-minute granularity, consider enabling 1-second intervals (note: AWS charges extra).
- Use AWS Lambda for real-time alerts – Push critical events to a custom monitoring setup for better responsiveness.
4. Metric Granularity & Data Latency
CloudWatch is not built for real-time monitoring, which can slow down incident response.
- Standard metrics are 1-minute granularity – This is often too high-level for teams that need real-time insights.
- Delays in metric updates – Some delays occur when pulling data, affecting alerting speed.
- Logs can take seconds to minutes to appear, making it slower for real-time log-based troubleshooting. If your incident response relies on log searches, the delay in CloudWatch Logs could slow things down compared to log management solutions like Logz.io.
💡 How to get more granular data in CloudWatch:
- Use Custom Metrics with lower intervals – AWS supports 1-second intervals (but at an additional cost).
- Stream logs to Kinesis – If you need real-time alerting, consider sending data to a more responsive system.
5. Cross-Region & Multi-Account Monitoring is a Challenge
CloudWatch is region-specific by default, which can be challenging for multi-account and multi-region organizations.
- No built-in cross-region views by default – AWS requires manual aggregation via EventBridge, Lambda, or S3.
- Log consolidation takes extra setup – Organizations need to manually configure central log collection.
💡 How to simplify Multi-Region monitoring:
- Use AWS Organizations – Consolidate logs into a central monitoring account.
- Forward logs to S3 – Store logs centrally and query them using Athena.
- If needed, AWS provides Cross-Region and Cross-Account Dashboards to enable multi-region monitoring.
6. Alarm Configuration Challenges
Configuring alarms in CloudWatch can be challenging due to static thresholds, which may lead to false positives or missed incidents.
- Static thresholds don’t adapt to traffic spikes – Many teams struggle with excessive noise from rigid alerting rules.
- No built-in anomaly detection – Requires manual tuning or additional AWS services.
💡 How to reduce alert noise in CloudWatch:
- Use CloudWatch Anomaly Detection – This feature automatically adjusts thresholds based on historical data.
- Group alerts by service – Helps avoid alert fatigue by combining related notifications.
7. Retention Policies & Data Management
CloudWatch provides built-in log retention controls, which work well for many teams. However, managing retention efficiently requires careful configuration—especially for teams with compliance or long-term storage needs.
- Logs auto-purge based on retention settings – AWS automatically deletes logs after the configured time, which can be problematic for compliance or security audits.
- Longer retention = higher costs. Unlike other observability platforms, CloudWatch does not offer cost-effective long-term storage.
💡 How to manage log retention efficiently:
- Move older logs to S3 or Glacier – Provides lower-cost archival while keeping logs accessible.
- Use log lifecycle policies – Configure automatic tiering of logs to reduce costs.
8. Fragmented Observability Requires Additional AWS Services
To get full observability, teams often need to integrate multiple AWS services, each with its own pricing.
- AWS X-Ray for tracing – But traces are separate from CloudWatch logs and metrics, requiring manual correlation for full observability.
- AWS CloudTrail for security – But querying historical logs incurs additional costs.
- Amazon QuickSight for visualization – But it has separate user-session pricing.
💡 How to reduce Observability fragmentation in AWS:
- Centralize logs, metrics, and traces in one tool – Either build your own aggregation solution or explore third-party platforms.
- Use OpenTelemetry – Standardizes telemetry collection across AWS services.
When to Consider an Alternative to CloudWatch
AWS CloudWatch is a powerful tool when configured correctly. If your team closely monitors costs, fine-tunes retention policies, and understands AWS’s pricing structure, it can be a great solution.
However, if you’re struggling with visibility gaps, high query costs, or fragmented observability, it might be worth exploring a more integrated approach.
Logz.io provides a unified observability platform that combines logs, metrics, and traces, helping teams troubleshoot faster, optimize costs, and simplify operations.
Curious to see if it’s the right fit for your team? Explore it yourself by navigating through our demo videos, interactive walkthroughs, and in-depth resources – or, schedule your demo today!
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