Introduction to Amazon CloudWatch

4 MIN READ
MIN READ

Amazon CloudWatch is a log monitoring and management service from Amazon Web Services (AWS). It’s designed to provide DevOps engineers, site reliability engineers (SREs), IT administrators, and software engineers access to metrics, data, and statistics to help understand their operations. 

CloudWatch Features 

Amazon CloudWatch features fall into four primary categories, which we’ll discuss below.

Logs and Metrics Collection and Analytics

Amazon CloudWatch allows users to gather metrics and logs. The types of logs supported by CloudWatch fit into three primary buckets. Vendor logs are the first bucket supported and published by AWS on behalf of the customer. Currently, AWS supports Amazon VPC Flow Logs and Amazon Route 53 logs. The second type of logs supported by CloudWatch are logs published by AWS Services, including AWS Lambda, API Gateway, CloudTrail, and others. The third bucket is custom logs.

CloudWatch also allows users to collect and aggregate pre-ordained and custom metrics. The built-in default metrics pull from AWS services, including AWS Lambda, API Gateway, EC2, S3, ECS, and DynamoDB. Users are not limited to default metrics, they can also collect performance metrics and event tracking metrics.

Monitoring and Views

Amazon CloudWatch offers basic dashboards, alarms, and other monitoring insights that allow users to see, organize, and act upon their logs and metrics.

Dashboards help users to visualize cloud resources and cloud applications. Users can graph data to help diagnose the root causes of problems or contextualize system-wide issues for systems health monitoring and troubleshooting.

While dashboards provide actionable intelligence, Amazon CloudWatch also can be set with alarms to flag specific default or custom-dictated occurrences and anomaly detection to help notify the necessary parties in the event of an issue. Alarms group to reduce noise in the event of a more significant problem and can vary for specific thresholds of metrics based on an environment’s specific resources. 

Insights and dashboards for container monitoring insights and Lambda monitoring insights offer real-time and logged tracing of performance. Tools such as CloudWatch ServiceLens, which visualizes the performance of applications, and CloudWatch Synthetics, which monitors application endpoints, provide visual insights into traffic and infrastructure.

Action and Automation

Amazon CloudWatch allows for the automation of capacity planning through Auto Scaling, where custom-set thresholds can cue responses for scaling that can optimize resource use or minimize costs. CloudWatch Events can automate corrective action through rule-based event matching and automation attached to alarms can automate actions.

Compliance

Depending on the audit periods, data generated through CloudWatch can be retained for set timeframes, assuring that when logs need to be accessed or audited for compliance, the information is there.

CloudWatch Pricing

Amazon CloudWatch offers both a free-tier and a premium paid tier that is calculated based on usage.

Free Tier

Unlimited basic monitoring metrics come at a 5-minute frequency within the free tier, including all non-custom events. Premium and tailored features change at specific thresholds.

Premium/Paid Tier

With the paid tier, users pay for utilization every month. The first 10,000 metrics are billed at $.30/mo, after which the next 240,000 metrics are $.10/mo, the next 750,000 at $.05/mo, and anything upwards of 1,000,000 metrics is billed at $.02/mo. Up to 5 statistics for the same metric in single GetMetricData API requests get included. Afterwards, the bill starts at $.01 per 1,000 metric requests, except GetMetricWidgetImage metrics, which are $.02 per 1,000 requests. Metric streams are available at $.003 per 1,000 metric updates. 

Dashboards cost $3 per dashboard per month. Alarms range in price from $.10 to $.90 per alarm, depending on the resolution and nature of the alarm. 

There is no cost for log data transfer, but data transfer out is priced depending on where and how much data goes. Collection is billed at $.50/GB, Storage at $.03/GB, and Analysis at $.005/GB. Vendor logs are billed at tiers up to 10TB..

Events bill users at a rate of $1 per million events and $1 per million cross-account events.

CloudWatch Contributor Insights for Cloud Watch bill at $.50/rule per month and $.02 per one million log events that match the rule. CloudWatch Contributor Insights for DynamoDB also bills at $.50/rul per month, but events bill at $.03 per one million logs that match the event per month.

Canaries run at $.0012 per canary run, although they may incur additional charges for other AWS services that are also utilizing them.

CloudWatch Limitations

CloudWatch is optimized for AWS logs. Although it supports log ingestion from sources outside of AWS, you can only ingest them using their agent. It doesn’t support ingestion via Syslog, APIs, or code libraries. Depending on your needs, there is a lot of customization work required to set it up, which may require an expert on your team to get started. Once you’ve managed to get your logs into the service, searching them in the CloudWatch user interface (UI) and command line interface (CLI) is tedious and complex. Other expected features like data visualizations and alerts are limited in CloudWatch. And, it is missing integrations with commonly used tools like Slack and PagerDuty and doesn't support Webhooks, making it difficult for engineers to receive notifications of issues when they arise.

Table of Contents

    Share Article

    RSS Feed

    Next blog post
    You're viewing our latest blog post.
    Previous blog post
    You're viewing our oldest blog post.
    Mezmo + Catchpoint deliver observability SREs can rely on
    Mezmo’s AI-powered Site Reliability Engineering (SRE) agent for Root Cause Analysis (RCA)
    What is Active Telemetry
    Launching an agentic SRE for root cause analysis
    Paving the way for a new era: Mezmo's Active Telemetry
    The Answer to SRE Agent Failures: Context Engineering
    Empowering an MCP server with a telemetry pipeline
    The Debugging Bottleneck: A Manual Log-Sifting Expedition
    The Smartest Member of Your Developer Ecosystem: Introducing the Mezmo MCP Server
    Your New AI Assistant for a Smarter Workflow
    The Observability Problem Isn't Data Volume Anymore—It's Context
    Beyond the Pipeline: Data Isn't Oil, It's Power.
    The Platform Engineer's Playbook: Mastering OpenTelemetry & Compliance with Mezmo and Dynatrace
    From Alert to Answer in Seconds: Accelerating Incident Response in Dynatrace
    Taming Your Dynatrace Bill: How to Cut Observability Costs, Not Visibility
    Architecting for Value: A Playbook for Sustainable Observability
    How to Cut Observability Costs with Synthetic Monitoring and Responsive Pipelines
    Unlock Deeper Insights: Introducing GitLab Event Integration with Mezmo
    Introducing the New Mezmo Product Homepage
    The Inconvenient Truth About AI Ethics in Observability
    Observability's Moneyball Moment: How AI Is Changing the Game (Not Ending It)
    Do you Grok It?
    Top Five Reasons Telemetry Pipelines Should Be on Every Engineer’s Radar
    Is It a Cup or a Pot? Helping You Pinpoint the Problem—and Sleep Through the Night
    Smarter Telemetry Pipelines: The Key to Cutting Datadog Costs and Observability Chaos
    Why Datadog Falls Short for Log Management and What to Do Instead
    Telemetry for Modern Apps: Reducing MTTR with Smarter Signals
    Transforming Observability: Simpler, Smarter, and More Affordable Data Control
    Datadog: The Good, The Bad, The Costly
    Mezmo Recognized with 25 G2 Awards for Spring 2025
    Reducing Telemetry Toil with Rapid Pipelining
    Cut Costs, Not Insights:   A Practical Guide to Telemetry Data Optimization
    Webinar Recap: Telemetry Pipeline 101
    Petabyte Scale, Gigabyte Costs: Mezmo’s Evolution from ElasticSearch to Quickwit
    2024 Recap - Highlights of Mezmo’s product enhancements
    My Favorite Observability and DevOps Articles of 2024
    AWS re:Invent ‘24: Generative AI Observability, Platform Engineering, and 99.9995% Availability
    From Gartner IOCS 2024 Conference: AI, Observability Data, and Telemetry Pipelines
    Our team’s learnings from Kubecon: Use Exemplars, Configuring OTel, and OTTL cookbook
    How Mezmo Uses a Telemetry Pipeline to Handle Metrics, Part II
    Webinar Recap: 2024 DORA Report: Accelerate State of DevOps
    Kubecon ‘24 recap: Patent Trolls, OTel Lessons at Scale, and Principle Platform Abstractions
    Announcing Mezmo Flow: Build a Telemetry Pipeline in 15 minutes
    Key Takeaways from the 2024 DORA Report
    Webinar Recap | Telemetry Data Management: Tales from the Trenches
    What are SLOs/SLIs/SLAs?
    Webinar Recap | Next Gen Log Management: Maximize Log Value with Telemetry Pipelines
    Creating In-Stream Alerts for Telemetry Data
    Creating Re-Usable Components for Telemetry Pipelines
    Optimizing Data for Service Management Objective Monitoring
    More Value From Your Logs: Next Generation Log Management from Mezmo
    A Day in the Life of a Mezmo SRE
    Webinar Recap: Applying a Data Engineering Approach to Telemetry Data
    Dogfooding at Mezmo: How we used telemetry pipeline to reduce data volume
    Unlocking Business Insights with Telemetry Pipelines
    Why Your Telemetry (Observability) Pipelines Need to be Responsive
    How Data Profiling Can Reduce Burnout
    Data Optimization Technique: Route Data to Specialized Processing Chains
    Data Privacy Takeaways from Gartner Security & Risk Summit
    Mastering Telemetry Pipelines: Driving Compliance and Data Optimization
    A Recap of Gartner Security and Risk Summit: GenAI, Augmented Cybersecurity, Burnout
    Why Telemetry Pipelines Should Be A Part Of Your Compliance Strategy
    Pipeline Module: Event to Metric
    Telemetry Data Compliance Module
    OpenTelemetry: The Key To Unified Telemetry Data
    Data optimization technique: convert events to metrics
    What’s New With Mezmo: In-stream Alerting
    How Mezmo Used Telemetry Pipeline to Handle Metrics
    Webinar Recap: Mastering Telemetry Pipelines - A DevOps Lifecycle Approach to Data Management
    Open-source Telemetry Pipelines: An Overview
    SRECon Recap: Product Reliability, Burn Out, and more
    Webinar Recap: How to Manage Telemetry Data with Confidence
    Webinar Recap: Myths and Realities in Telemetry Data Handling
    Using Vector to Build a Telemetry Pipeline Solution
    Managing Telemetry Data Overflow in Kubernetes with Resource Quotas and Limits
    How To Optimize Telemetry Pipelines For Better Observability and Security
    Gartner IOCS Conference Recap: Monitoring and Observing Environments with Telemetry Pipelines
    AWS re:Invent 2023 highlights: Observability at Stripe, Capital One, and McDonald’s
    Webinar Recap: Best Practices for Observability Pipelines
    Introducing Responsive Pipelines from Mezmo
    My First KubeCon - Tales of the K8’s community, DE&I, sustainability, and OTel
    Modernize Telemetry Pipeline Management with Mezmo Pipeline as Code
    How To Profile and Optimize Telemetry Data: A Deep Dive
    Kubernetes Telemetry Data Optimization in Five Steps with Mezmo
    Introducing Mezmo Edge: A Secure Approach To Telemetry Data
    Understand Kubernetes Telemetry Data Immediately With Mezmo’s Welcome Pipeline
    Unearthing Gold: Deriving Metrics from Logs with Mezmo Telemetry Pipeline
    Webinar Recap: The Single Pane of Glass Myth
    Empower Observability Engineers: Enhance Engineering With Mezmo
    Webinar Recap: How to Get More Out of Your Log Data
    Unraveling the Log Data Explosion: New Market Research Shows Trends and Challenges
    Webinar Recap: Unlocking the Full Value of Telemetry Data
    Data-Driven Decision Making: Leveraging Metrics and Logs-to-Metrics Processors
    How To Configure The Mezmo Telemetry Pipeline
    Supercharge Elasticsearch Observability With Telemetry Pipelines
    Enhancing Grafana Observability With Telemetry Pipelines
    Optimizing Your Splunk Experience with Telemetry Pipelines
    Webinar Recap: Unlocking Business Performance with Telemetry Data
    Enhancing Datadog Observability with Telemetry Pipelines
    Transforming Your Data With Telemetry Pipelines