CloudWatch vs Prometheus: In-Depth Comparison 2026

AWS CloudWatch and Prometheus solve the same problem, infrastructure and application monitoring but through fundamentally different architectures. CloudWatch is a managed AWS service that auto-collects metrics from AWS resources. Prometheus is an open source pull-based system built for dynamic environments like Kubernetes. According to the 2025 CNCF Survey, 83% of organizations using Kubernetes also use Prometheus for monitoring, making it the de facto standard in cloud native stacks.

This guide compares CloudWatch and Prometheus across data model, deployment, cost, alerting, and Kubernetes monitoring to help you choose the right tool or understand when to use both together.

Quick Comparison: CloudWatch vs Prometheus

	CloudWatch	Prometheus
Data model	Pre-aggregated metrics at 1 or 5 min intervals	Raw time series data scraped at configurable intervals (default 15s)
Collection method	Push: AWS services auto-publish metrics	Pull: Prometheus scrapes /metrics endpoints
Deployment	Fully managed SaaS by AWS	Self-hosted or managed via third-party services
Query language	CloudWatch Insights (SQL-like for logs), GetMetricStatistics API	PromQL (purpose-built for time series)
Alerting	CloudWatch Alarms trigger SNS, Lambda, Auto Scaling	Alertmanager with flexible routing and silencing
Kubernetes support	Requires CloudWatch agent + Container Insights ($0.50 per million metrics beyond 10 million/mo)	Native service discovery, pod/node/container metrics out of the box
Retention	15 months default (1 min resolution), free	Configurable, typically 15–30 days local storage (cost = your infra)
Cost model	Per metric, per alarm, per dashboard API call, per GB logs	Free software, you pay for compute + storage
Best for	AWS-native workloads, managed services like RDS/Lambda, teams wanting zero ops	Kubernetes, multi-cloud, high-resolution metrics, teams comfortable with self-hosting

AWS CloudWatch Overview

CloudWatch is AWS’s native monitoring service. It auto-collects metrics from AWS resources like EC2 instances, RDS databases, Lambda functions, API Gateway endpoints and aggregates them into 1-minute or 5-minute intervals. You create alarms on these metrics to trigger Auto Scaling, SNS notifications, or Lambda functions.

CloudWatch supports custom metrics published via the PutMetricData API, allowing applications to push their own data. For logs, CloudWatch Logs collects and indexes log streams from services like Lambda, ECS, and EC2 (via the CloudWatch agent). CloudWatch Insights provides a query language for log analysis.

Deployment: Fully managed SaaS. No infrastructure to run, no agents to install for most AWS services (except EC2, ECS).

Strengths: Zero setup for AWS services, deep integration with AWS ecosystem (Auto Scaling, SNS, Lambda), managed retention and availability.

Limitations: Pre-aggregated data means you cannot retroactively query at higher resolution than what was stored. PromQL’s flexibility and raw metric access are not available. Costs scale with metric volume, alarms, and dashboard API calls.

Pricing: CloudWatch charges per metric ($0.30 per custom metric per month beyond the free tier), per alarm ($0.10 per alarm per month), per GB of logs ingested ($0.50/GB), and per million log events scanned in Insights ($0.005 per GB scanned). Container Insights for EKS/ECS costs $0.50 per million metrics beyond 10 million per month.

Prometheus Overview

Prometheus is an open source monitoring system built by SoundCloud in 2012, now a CNCF graduated project. It uses a pull model: Prometheus scrapes HTTP endpoints exposing metrics in a text-based format at configurable intervals (default 15 seconds). This architecture fits dynamic environments where services scale up and down frequently.

Prometheus natively discovers Kubernetes pods, services, and nodes through service discovery integrations. It stores raw time series data locally in a custom time series database (TSDB) optimized for high cardinality and fast range queries.

Deployment: Self-hosted on VMs, Kubernetes, or Docker. Managed Prometheus offerings include AWS Managed Service for Prometheus (AMP), Google Cloud Managed Service for Prometheus, Grafana Cloud, and platforms like CubeAPM that embed Prometheus-compatible storage with unified observability.

Strengths: Native Kubernetes integration, PromQL for flexible time series queries, active exporter ecosystem (500+ exporters for third-party systems), local control over retention and resolution.

Limitations: Requires operational overhead (storage management, backups, HA setup). No built-in long-term storage—requires integration with Thanos, Cortex, or remote write to managed backends. Alertmanager is powerful but requires configuration and maintenance.

Pricing: Prometheus software is free. You pay for the infrastructure: compute, storage, and optionally managed services. AWS Managed Prometheus charges $0.30 per million samples ingested and $0.03 per million samples queried. Self-hosting on EC2 or Kubernetes means you own the full cost stack.

Feature-by-Feature Comparison

Data Collection and Architecture

CloudWatch pushes metrics from AWS services to a centralized data store. For custom application metrics, you call PutMetricData to publish data points. EC2 detailed monitoring publishes metrics every 1 minute; basic monitoring publishes every 5 minutes. This push model works well for AWS-native workloads but requires API calls and IAM permissions to publish custom metrics.

Prometheus pulls metrics by scraping HTTP endpoints. Applications expose a /metrics endpoint in Prometheus format. Prometheus discovers these endpoints through static configs, file-based service discovery, or native integrations with Kubernetes, Consul, EC2 SD. The pull model simplifies client logic—applications just expose metrics, Prometheus handles collection and retry logic.

Why this matters: In Kubernetes, services scale dynamically. Prometheus’s pull model with service discovery means new pods are automatically scraped without manual configuration. CloudWatch requires the CloudWatch agent plus Container Insights to achieve similar coverage, adding setup complexity and cost.

Query Language and Flexibility

CloudWatch uses GetMetricStatistics API and CloudWatch Insights (for logs). Metric queries support basic aggregations (avg, sum, min, max) over time windows. You cannot query raw data points retroactively at higher resolution than stored. For example, if EC2 basic monitoring stores 5-minute intervals, you cannot later drill into 1-minute granularity for that period.

Prometheus uses PromQL, a functional query language purpose-built for time series. PromQL supports instant vectors, range vectors, aggregations, rate calculations, and complex math across metrics. Example:

rate(http_requests_total[5m])

This calculates the per-second rate of HTTP requests over the last 5 minutes. PromQL’s flexibility allows percentile calculations, alerting on derived metrics, and correlation across multiple time series in a single query.

Real-world implication: During an incident, PromQL lets you interactively explore metrics, calculate latency percentiles, and correlate errors with CPU usage in real time. CloudWatch requires pre-configured dashboards and alarms—post-hoc exploration is limited to the aggregations already stored.

Alerting

CloudWatch Alarms trigger when a metric crosses a threshold for a specified number of evaluation periods. Alarms can invoke SNS topics, Lambda functions, Auto Scaling policies, or EC2 actions. Composite alarms combine multiple alarms using AND/OR logic. CloudWatch Alarms are tightly integrated with AWS services but lack advanced grouping, silencing, or routing logic.

Prometheus Alertmanager receives alerts from Prometheus based on PromQL expressions. Alertmanager groups, deduplicates, silences, and routes alerts to receivers (Slack, PagerDuty, email, webhooks). Example alert rule:

- alert: HighErrorRate
  expr: rate(http_requests_total{status=~"5.."}[5m]) > 0.05
  for: 10m
  annotations:
    summary: "High 5xx error rate on {{ $labels.instance }}"

Alertmanager routes this to different channels based on severity, team, or environment. Silencing prevents alert fatigue during known maintenance windows.

Verdict: CloudWatch alerting is simpler for basic AWS resource alarms. Prometheus Alertmanager is more powerful for complex distributed systems with multi-team routing and dynamic labeling.

Kubernetes Monitoring

CloudWatch requires CloudWatch Container Insights to monitor EKS clusters. Container Insights deploys the CloudWatch agent as a DaemonSet, which scrapes kubelet metrics and publishes them to CloudWatch. You get node CPU/memory, pod restarts, and namespace-level aggregations. Cost: $0.50 per million metrics beyond 10 million per month, plus $0.50/GB for log ingestion from FluentBit.

Prometheus natively scrapes Kubernetes metrics from kubelet, kube-state-metrics, and application pods. Service discovery automatically detects pods with Prometheus annotations. Metric granularity is higher—15-second scrape intervals vs. CloudWatch’s 1-minute aggregations. Prometheus collects raw metrics like container_cpu_usage_seconds_total and container_memory_working_set_bytes, which you query with PromQL for instant or range insights.

Example: To see CPU usage by pod:

sum(rate(container_cpu_usage_seconds_total[5m])) by (pod)

CloudWatch Insights provides similar data but at lower resolution and with query latency measured in seconds, not milliseconds.

Verdict: Prometheus is the standard for Kubernetes monitoring. CloudWatch works but adds cost and operational overhead. For hybrid or multi-cloud Kubernetes, Prometheus is the only practical choice.

Retention and Long-Term Storage

CloudWatch retains metrics for 15 months by default. 1-minute data points are stored for 15 days, then aggregated to 5-minute intervals for 63 days, then 1-hour intervals for 455 days. You cannot increase resolution retroactively. Logs in CloudWatch Logs are retained indefinitely by default (you pay for storage at $0.03/GB per month).

Prometheus local storage is configurable, typically 15–30 days. Retention depends on disk size and scrape frequency. For long-term storage, Prometheus remote writes to backends like Thanos, Cortex, AWS Managed Prometheus, Grafana Mimir, or CubeAPM. Remote write decouples long-term retention from local Prometheus instances, enabling multi-year retention with efficient compression.

Cost implication: CloudWatch’s managed retention simplifies ops but limits query flexibility. Prometheus requires you to manage retention and storage backends, but gives you full control over resolution and query access.

Integration Ecosystem

CloudWatch integrates natively with all AWS services. For non-AWS systems, you publish custom metrics via PutMetricData or use the CloudWatch agent. Third-party integrations exist but are limited compared to Prometheus’s exporter ecosystem.

Prometheus has 500+ exporters for databases (MySQL, PostgreSQL, MongoDB, Redis), message queues (Kafka, RabbitMQ), proxies (NGINX, HAProxy), and infrastructure (node_exporter for Linux, windows_exporter). If a system exposes metrics, a Prometheus exporter likely exists. OpenTelemetry Collector also bridges metrics to Prometheus via remote write.

Verdict: CloudWatch wins for AWS-only stacks. Prometheus wins for multi-cloud, hybrid, or heterogeneous infrastructure.

Pricing Comparison

This section models costs for a mid-sized Kubernetes cluster on AWS: 50 EC2 nodes, 200 pods, 1 million metric samples per minute, 30-day retention, basic alerting. Understand how Cloudwatch pricing grows with scale using this cloudwatch pricing calculator.

CloudWatch Cost Breakdown

Item	Calculation	Monthly Cost
Custom metrics (pods, nodes, services)	500 metrics × $0.30	$150
Container Insights metrics (beyond free tier)	10M samples/month free, 40M billable × $0.50 per million	$20
Alarms (20 alarms for critical pods/nodes)	20 × $0.10	$2
CloudWatch Logs ingestion (10 GB/day avg)	300 GB × $0.50/GB	$150
Log storage (300 GB/month at 30 days)	300 GB × $0.03/GB	$9
Dashboard API calls (10k requests/month)	~$0 (first 1M free)	$0
Total		$331/month

This assumes you use Container Insights. If you publish custom metrics via PutMetricData, metric costs increase significantly. For 100k custom metrics, add $30,000/month just for metric publishing.

Prometheus Cost Breakdown (Self-Hosted on EC2)

Item	Calculation	Monthly Cost
EC2 instance for Prometheus (m5.2xlarge, 8 vCPU, 32 GB RAM)	$0.384/hour × 730 hours	$280
EBS storage for TSDB (500 GB GP3)	500 GB × $0.08/GB	$40
Alertmanager instance (t3.medium)	$0.0416/hour × 730 hours	$30
Grafana instance (t3.medium)	$0.0416/hour × 730 hours	$30
Data transfer within AWS VPC	~0 (same region)	$0
Total		$380/month

For long-term storage with Thanos on S3: add ~$20/month for 1 TB of compressed historical data stored in S3 ($0.023/GB).

AWS Managed Prometheus Cost Breakdown

Item	Calculation	Monthly Cost
Metric samples ingested (1M samples/min × 60 × 24 × 30 = 43.2B samples)	43.2B samples × $0.30 per million	$12,960
Query samples (estimate 5% of ingested)	2.16B samples × $0.03 per million	$65
Total		$13,025/month

AWS Managed Prometheus pricing makes it prohibitively expensive for high-volume workloads. Most teams using AMP limit ingestion by reducing scrape frequency or filtering metrics at the source.

Verdict: For a 50-node Kubernetes cluster, self-hosted Prometheus costs ~$380/month vs. CloudWatch at ~$331/month (using Container Insights). However, CloudWatch’s cost scales linearly with custom metrics—if you publish 10k custom metrics, CloudWatch jumps to $3,000+/month. Prometheus cost remains fixed at infrastructure cost regardless of metric cardinality.

Disclaimer: This estimate models a specific infrastructure profile. Your actual costs will vary based on metric cardinality, scrape frequency, log volume, and retention period. Verify current pricing on AWS CloudWatch pricing and AWS Managed Prometheus pricing pages.

When to Use CloudWatch

Use CloudWatch when:

• Your stack is 100% AWS-native (EC2, RDS, Lambda, ECS, API Gateway)
• You want zero operational overhead—no servers to manage, no software to patch
• You need tight integration with AWS Auto Scaling, SNS, Lambda triggers
• Your team has limited monitoring expertise and prefers managed services
• Metric volume is low (under 1,000 custom metrics)

Example use case: A SaaS company running a monolithic Rails app on EC2 with RDS PostgreSQL. CloudWatch auto-collects EC2 CPU/disk, RDS query latency, and ALB request counts. Alarms trigger Auto Scaling and PagerDuty via SNS. Total cost: ~$200/month for basic monitoring without custom metrics.

When to Use Prometheus

Use Prometheus when:

• You run Kubernetes, ECS, or other containerized workloads at scale
• You need high-resolution metrics (sub-minute intervals) for real-time debugging
• You want PromQL’s flexibility for complex queries and alerting
• You operate multi-cloud or hybrid infrastructure (AWS + GCP + on-prem)
• You need high-cardinality metrics (thousands of unique label combinations)
• Your team has the skills to run and maintain monitoring infrastructure

Example use case: A fintech platform running 200 microservices on EKS with 80 nodes. Prometheus scrapes pod metrics every 15 seconds, detects latency spikes in sub-minute windows, and alerts based on error rate thresholds. Engineers query PromQL interactively during incidents to correlate CPU usage, request rates, and database latency. Cost: ~$400/month for Prometheus + Thanos on EC2.

Using CloudWatch and Prometheus Together

Many teams run both. CloudWatch monitors AWS-managed services (RDS, Lambda, ALB, S3). Prometheus monitors Kubernetes workloads, application metrics, and custom infrastructure. The CloudWatch exporter for Prometheus bridges the two: it scrapes CloudWatch metrics via the AWS API and exposes them in Prometheus format, allowing unified querying in Grafana.

Common pattern:

1. CloudWatch collects RDS metrics, Lambda invocations, ALB request counts
2. Prometheus scrapes Kubernetes pods, nodes, and application /metrics endpoints
3. CloudWatch exporter makes CloudWatch data available in Prometheus
4. Grafana dashboards query both Prometheus and CloudWatch data sources side by side
5. Alertmanager handles all alerts with unified routing

This hybrid approach avoids CloudWatch’s high cost for custom metrics while keeping managed AWS service monitoring in place.

CubeAPM: Unified Observability with Prometheus Compatibility

CubeAPM offers an alternative to both CloudWatch and self-hosted Prometheus by combining full-stack observability—APM, logs, infrastructure, Kubernetes in a single platform that runs inside your cloud or on-prem. It ingests OpenTelemetry and Prometheus metrics natively, eliminating the need to choose between CloudWatch’s managed simplicity and Prometheus’s operational complexity.

Key features:

• Deployment: Self-hosted in your VPC or data center, managed by CubeAPM. No data leaves your infrastructure.
• Data model: Ingests Prometheus metrics, CloudWatch metrics (via exporters), and OpenTelemetry signals. Unified storage with unlimited retention.
• Query language: PromQL support for Prometheus queries, plus SQL-based log and trace querying.
• Kubernetes monitoring: Native Kubernetes service discovery, pod/node/container metrics, cluster health dashboards.
• Cost: $0.15/GB ingested, all signal types (metrics, logs, traces). No per-metric, per-alarm, or per-dashboard charges.

When to choose CubeAPM:

• You want Prometheus-level flexibility without self-hosting Prometheus infrastructure
• You need unified APM, logs, and infrastructure in one tool
• Data residency or compliance requires on-prem deployment
• You want predictable pricing that does not scale with metric cardinality

Cost comparison for 50-node Kubernetes cluster:

Assuming 10 TB/month total telemetry (metrics + logs + traces):

• CubeAPM: 10 TB × $0.15/GB = $1,500/month
• CloudWatch + Container Insights: ~$3,000/month (high metric volume)
• Self-hosted Prometheus + Thanos + Grafana: ~$600/month (infra only, ops burden not included)

CubeAPM sits between managed SaaS and DIY self-hosting: you get managed operations with full data control and Prometheus compatibility.

Who Should Choose Each Tool

Tool	Best for
CloudWatch	AWS-only infrastructure, teams with limited monitoring expertise, low custom metric volume, fully managed preference
Prometheus	Kubernetes, multi-cloud, high-cardinality metrics, teams comfortable with self-hosting, PromQL power users
CubeAPM	Teams wanting Prometheus flexibility + managed operations, data sovereignty requirements, unified observability across APM/logs/infra

Verdict

For AWS-native workloads with low customization: CloudWatch is the easiest path. It auto-collects AWS service metrics, integrates with Auto Scaling and Lambda, and requires zero operational overhead. Cost is predictable for basic monitoring but scales sharply with custom metrics.

For Kubernetes and cloud native stacks: Prometheus is the industry standard. Its pull model, native service discovery, and PromQL make it the best fit for dynamic containerized environments. Operational overhead is real—you manage storage, retention, and high availability—but the flexibility and community support justify the investment.

For teams needing both power and managed operations: CubeAPM combines Prometheus compatibility, unified observability, and self-hosted deployment without the DIY burden. It is the best middle ground for platform teams that want control over data and cost without building and maintaining a full Prometheus stack.

Disclaimer: The information in this article reflects the latest details available at the time of publication and may change as technologies and products evolve. Features, pricing, and plan limits can change over time. Always verify the latest information directly with the vendor before making purchasing or deployment decisions.

Frequently Asked Questions