Amazon Managed Service for Prometheus: What It Is and When to Use It

Definition

Amazon Managed Service for Prometheus is a serverless, Prometheus-compatible monitoring service that makes it easy to monitor containerized applications and infrastructure at scale. It solves the operational challenges of managing and scaling a highly available Prometheus environment by handling the ingestion, storage, alerting, and querying of metrics, allowing developers to focus on building applications rather than managing monitoring infrastructure.

How It Works

Amazon Managed Service for Prometheus (AMP) provides a fully managed environment that is compatible with the open-source Prometheus data model and the powerful Prometheus Query Language (PromQL).

The core component is the Workspace, which is a logical, isolated space where you ingest, store, and query your metrics. Each workspace is highly available, with data replicated across three Availability Zones (AZs) within an AWS Region.

Data gets into a workspace in one of two ways:

1. AWS Managed Collectors: For Amazon Elastic Kubernetes Service (EKS), AMP offers a fully managed, agentless collector that can automatically discover and scrape Prometheus metrics from your EKS applications and infrastructure. This collector runs within the AWS environment, not on your cluster, reducing operational overhead and cost. It securely sends metrics to your workspace endpoint via a Virtual Private Cloud (VPC) endpoint, ensuring data does not traverse the public internet.
2. Customer-Managed Collectors: You can configure your existing Prometheus servers (running on Amazon EKS, Amazon Elastic Container Service (ECS), Amazon EC2, or on-premises) to forward metrics to an AMP workspace. This is done by adding a remote_write configuration to your Prometheus setup, pointing to the unique endpoint of your AMP workspace. You can also use agents like the AWS Distro for OpenTelemetry (ADOT) to collect and send metrics.

Once metrics are in the workspace, you can:

• Query Data: Use PromQL to query metrics via the workspace's query endpoint. This endpoint can be integrated with visualization tools like Amazon Managed Grafana or self-hosted Grafana.
• Manage Rules and Alerts: Configure recording rules and alerting rules within the workspace. The service continuously evaluates these rules against your ingested metrics. Alerts can be routed to notification channels like Amazon Simple Notification Service (SNS) or other services like PagerDuty.

Authentication and authorization for all API operations, including metric ingestion and querying, are controlled through AWS Identity and Access Management (IAM).

Key Features and Limits

• Fully Managed & Serverless: No infrastructure to provision or manage. AWS handles scaling, patching, and availability.
• Prometheus-Compatible: Uses the standard Prometheus data model and PromQL, allowing you to use existing dashboards, alerts, and skills.
• High Availability: Workspaces are deployed across multiple Availability Zones, with data replicated across three AZs for durability.
• Scalability: Designed to elastically scale as your ingestion and query loads change, handling high-cardinality metrics common in container environments.
• Agentless Collection for EKS: A fully managed collector option simplifies metric collection from Amazon EKS clusters without needing to run agents in-cluster.
• Secure: Integrates with AWS IAM for authentication and authorization. API calls are logged to AWS CloudTrail, and you can use VPC endpoints for private connectivity.
• Integration with Amazon Managed Grafana: Provides a seamless, fully managed open-source observability stack for visualization and alerting.

Service Quotas (Limits) as of 2026:

• Active Series per Workspace: The default limit is 50 million active time series per workspace. This is an adjustable quota, and you can request increases.
• Ingestion Rate: There are adjustable quotas on the number of metric samples that can be ingested per second. If you exceed the limit, ingestion will be throttled.
• Data Retention: Metrics are stored for 150 days by default, but this is now a customizable setting per workspace.
• Label-based Active Series Limits: You can set specific active series limits for different metric producers within a single workspace, which helps manage costs and prevent noisy tenants from impacting critical metrics.

Common Use Cases

• Container Monitoring at Scale: The primary use case is monitoring the performance and health of applications and infrastructure running on container orchestrators like Amazon EKS and Amazon ECS, especially in large, dynamic environments.
• Modernizing Existing Prometheus Setups: Teams already using self-hosted Prometheus can offload the burden of managing scalable storage and high availability by using AMP as a long-term storage backend via remote_write.
• Unified Multi-Cluster/Multi-Account Monitoring: Centralize metrics from multiple Kubernetes clusters, AWS accounts, and even on-premises environments into a single AMP workspace for a global view of system health.
• High-Cardinality Metrics Analysis: Ideal for workloads that generate metrics with many labels (high cardinality), such as microservices, which can be challenging and expensive for traditional monitoring systems.

Pricing Model

Amazon Managed Service for Prometheus has a pay-as-you-go pricing model with no upfront fees or minimum commitments. The primary cost drivers are:

• Metric Ingestion: You are charged per million metric samples ingested into the service. The pricing is tiered, meaning the cost per million samples decreases as your ingestion volume increases.
• Metric Storage: You pay a per-GB fee for the volume of metrics stored per month.
• Query Processing: You are charged based on the number of metric samples processed by your PromQL queries.
• AWS Managed Collector (for EKS): If you use the agentless collector, there is an hourly charge per collector and a per-sample ingested fee.

For detailed and current pricing, always refer to the official AWS Pricing page and use the AWS Pricing Calculator to estimate costs for your specific workload.

Pros and Cons

Pros:

• Reduced Operational Overhead: Eliminates the need to manage, scale, and secure Prometheus servers and their long-term storage, freeing up engineering time.
• Massive Scalability & High Availability: Built to handle billions of time series and automatically scales, with multi-AZ replication for resilience.
• Open Standards-Based: Leverages the popular open-source Prometheus and PromQL, avoiding vendor lock-in and allowing reuse of existing skills and tools.
• Deep AWS Integration: Natively integrates with AWS security (IAM), container services (EKS, ECS), and other observability services like Amazon Managed Grafana.

Cons:

• Cost: For very large workloads, the pay-per-use model can become more expensive than a well-optimized self-hosted solution, although self-hosting has significant hidden labor and infrastructure costs.
• Metrics-Only Service: AMP is focused exclusively on Prometheus metrics and does not handle logs or traces. A complete observability solution requires integrating it with other services like Amazon CloudWatch Logs or AWS X-Ray.
• Less Control: As a managed service, you have less control over the underlying infrastructure and configuration compared to running Prometheus yourself.

Comparison with Alternatives

• Amazon Managed Service for Prometheus vs. Self-Hosted Prometheus on EC2/EKS:

  • Management: AMP is fully managed, while self-hosting requires significant effort for setup, scaling (e.g., with Thanos or Cortex), high availability, and maintenance.
  • Cost: AMP's pricing is consumption-based. Self-hosting involves infrastructure costs (EC2, EBS/S3) and significant operational labor costs, which can often exceed the cost of the managed service.
  • Scalability: AMP scales automatically. Scaling a self-hosted solution is complex and requires deep expertise in distributed systems.

• Amazon Managed Service for Prometheus vs. Amazon CloudWatch:

  • Focus: AMP is specifically designed for Prometheus-compatible, high-cardinality container monitoring using PromQL. CloudWatch is a broader observability service for logs, metrics, and traces across the entire AWS ecosystem.
  • Query Language: AMP uses the powerful and flexible PromQL. CloudWatch uses its own query mechanisms like CloudWatch Metrics Insights and CloudWatch Logs Insights.
  • Ecosystem: AMP is ideal for those invested in the open-source, Cloud Native Computing Foundation (CNCF) ecosystem (Prometheus, Grafana). CloudWatch is tightly integrated with all AWS services and provides a unified experience within the AWS console. CloudWatch can ingest Prometheus metrics, but AMP provides a native Prometheus API experience.

Exam Relevance

Amazon Managed Service for Prometheus is relevant for several AWS certifications, particularly those focused on DevOps, containers, and solutions architecture.

• AWS Certified DevOps Engineer - Professional (DOP-C02): This exam heavily tests monitoring, logging, and observability. Understanding how to implement a scalable monitoring solution for containerized applications using AMP is a key topic.
• AWS Certified Solutions Architect - Professional (SAP-C02): Questions may involve designing highly available and scalable architectures for microservices, where AMP would be a suitable component for the monitoring strategy.
• AWS Certified Security - Specialty (SCS-C02): Knowledge of how AMP integrates with IAM, VPC Endpoints, and other security controls for secure monitoring is relevant.

Examinees should know AMP's core purpose, its key components (workspace, collectors), its integration with EKS and Grafana, and when to choose it over CloudWatch or a self-managed solution.

Frequently Asked Questions

Q: Do I need to run my own Prometheus server to use Amazon Managed Service for Prometheus?

A: Not necessarily. For workloads on Amazon EKS, you can use the AWS managed, agentless collector to scrape and send metrics directly to your workspace without running a Prometheus server in your cluster. However, for other environments (ECS, EC2, on-premises) or for more complex scraping configurations, you would typically run a standard Prometheus server or an OpenTelemetry agent and configure it to remote_write metrics to the service.

Q: How does Amazon Managed Service for Prometheus relate to Amazon Managed Grafana?

A: They are complementary services that provide a fully managed, open-source-based observability stack. Amazon Managed Service for Prometheus acts as the scalable, durable backend for storing and querying metrics. Amazon Managed Grafana is the visualization layer, providing dashboards and alerting. You can easily add an AMP workspace as a data source in Amazon Managed Grafana to build dashboards and visualize your container metrics.

Q: Can I use Amazon Managed Service for Prometheus to monitor on-premises workloads?

A: Yes. As long as your on-premises Prometheus servers or agents have network connectivity to the AWS public endpoints for the service, you can configure them to remote_write metrics to an Amazon Managed Service for Prometheus workspace. This allows you to create a hybrid monitoring solution, centralizing metrics from both on-premises and AWS environments.

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This article reflects AWS features and pricing as of 2026. AWS services evolve rapidly — always verify against the official AWS documentation before making production decisions.