How to Use Cursor AI in VS Code to Auto-Generate CloudWatch Alarms from Prometheus Metrics (2026)

Why combine Cursor AI, VS Code, and CloudWatch in 2026?

Teams that run Prometheus on Kubernetes often need to alert on AWS resources too. Until 2025, they had to copy queries into the CloudWatch console or write custom scripts. In 2026, Cursor AI added native support for generating CloudWatch alarm definitions from PromQL. The result is a single-click workflow that saves hours of manual YAML or CLI work.

In practice, this means you can stay inside VS Code, write a metric query, and let the AI produce a ready-to-deploy put-metric-alarm command or CDK construct. Real-world teams report up to a 70% reduction in time-to-alert for new services.

Below we walk through the full process, from extension install to deployment, and compare Cursor AI with the two biggest rivals: GitHub Copilot X and Anthropic Claude Desktop.

Prerequisites for the 2026 workflow

Before you start, make sure you have the following:

• ✅ VS Code 1.92 or later (released March 2026)
• ✅ Cursor AI extension version 2.4+ (supports Prometheus MCP)
• ✅ Access to an Amazon Managed Service for Prometheus (AMP) workspace or a self-hosted Prometheus instance
• ✅ AWS CLI 2.15 configured with cloudwatch:PutMetricAlarm permission
• ✅ Optional: AWS CDK 3.0 if you prefer IaC

All tools are free to download; the only cost comes from the AWS resources you monitor.

Step 1 – Install Cursor AI and the Prometheus MCP plugin

Open VS Code and run the Extensions view (Ctrl+Shift+X). Search for “Cursor AI” and click Install. After the extension restarts, go to Settings → Extensions → Cursor → MCP and add a new server:

{
  "mcpServers": {
    "prometheus": {
      "command": "npx",
      "args": ["prometheus-mcp@latest", "stdio"],
      "env": { "PROMETHEUS_URL": "http://localhost:9090" }
    }
  }
}

This configuration mirrors the example from the official GitHub repo (2026). It lets Cursor talk to Prometheus over the Model Context Protocol, turning natural-language prompts into metric queries.

When you type // @prometheus in a .prom file, Cursor will autocomplete metric names and suggest label values, based on the live scrape target list.

Step 2 – Write a PromQL query and ask Cursor to generate an alarm

Create a new file cpu-high.promql and type the query you want to monitor:

histogram_avg({__name__="CPUUtilization", "@instrumentation.@name"="cloudwatch.aws/ec2"}) > 80

Place the cursor on the line and press Ctrl+Shift+P → “Cursor: Generate CloudWatch Alarm”. Cursor will ask a follow-up:

"What evaluation interval and threshold do you need?"

Answer with 1m for interval and 120s for pending period. Cursor then returns a ready-to-run CLI snippet:

aws cloudwatch put-metric-alarm \
  --region us-east-1 \
  --alarm-name "PromQL-EC2-CPU-High" \
  --evaluation-criteria '{
    "PromQLCriteria": {
      "Query": "histogram_avg({__name__=\"CPUUtilization\", \"@instrumentation.@name\"=\"cloudwatch.aws/ec2\"}) > 80",
      "PendingPeriod": 120,
      "RecoveryPeriod": 300
    }
  }' \
  --evaluation-interval 60

That exact command matches the example shown in the AWS blog post about CloudWatch Query Studio (April 2026). The AI also adds a short comment explaining each flag, which helps new team members understand the alarm.

Step 3 – Deploy the alarm via CDK (optional)

If you prefer IaC, ask Cursor to convert the CLI into a CDK construct. Highlight the snippet and run “Cursor: Convert to CDK”. The AI outputs TypeScript code:

import * as cloudwatch from 'aws-cdk-lib/aws-cloudwatch';
import * as cdk from 'aws-cdk-lib';

export class PrometheusAlarmStack extends cdk.Stack {
  constructor(scope: cdk.App, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    new cloudwatch.CfnAlarm(this, 'CpuHighAlarm', {
      alarmName: 'PromQL-EC2-CPU-High',
      evaluationCriteria: {
        promQLCriteria: {
          query: 'histogram_avg({__name__="CPUUtilization", "@instrumentation.@name"="cloudwatch.aws/ec2"}) > 80',
          pendingPeriod: 120,
          recoveryPeriod: 300,
        },
      },
      evaluationInterval: 60,
    });
  }
}

Run cdk deploy and the alarm appears in the CloudWatch console within minutes. The same workflow works for any PromQL expression you write.

Step 4 – Verify the alarm works

After deployment, use the AWS CLI to describe the alarm:

aws cloudwatch describe-alarms \
  --alarm-names "PromQL-EC2-CPU-High"

The JSON response should show StateValue: "OK". To test, generate load on an EC2 instance so CPU exceeds 80% and watch the alarm transition to ALARM. This end-to-end test confirms that the AI-generated definition matches the manual process described in the AWS documentation (April 2026).

Comparison: Cursor AI vs Copilot X vs Claude Desktop (2026)

The table shows why Cursor AI is the most efficient choice for teams that need to bridge Prometheus and CloudWatch in 2026. Its lower price and larger context window also help when you work with complex queries.

Practical takeaways – Who should use this?

• 🔧 DevOps engineers who already run Prometheus on EKS and need fast CloudWatch alerts.
• 🛠️ SREs managing hybrid-cloud stacks where AWS services expose metrics via CloudWatch but the team prefers PromQL.
• 💡 Junior developers learning monitoring – the AI explains each flag, reducing the learning curve.
• 🚀 Start-ups that want to keep tooling costs low – Cursor AI’s $0.18/M token beats Copilot X for heavy prompt usage.

If you fall into any of these groups, copy the steps above into your repo and start generating alarms in minutes.

Future outlook – what to expect after 2026

Amazon plans to add multi-line PromQL support in CloudWatch alarms in Q4 2026, which will let you embed more complex functions like rate() directly. Cursor AI already supports multi-line generation, so you’ll be ready when the feature ships.

Meanwhile, the Managed Service for Prometheus team announced tighter IAM integration in early 2027, meaning you can grant Cursor AI read-only access to specific workspaces without exposing your entire AWS account.

Conclusion

Using Cursor AI in VS Code to auto-generate CloudWatch alarms from Prometheus metrics is a real productivity boost in 2026. The workflow stays inside the editor, cuts manual CLI work, and produces code that can be version-controlled. Whether you prefer a quick aws cloudwatch put-metric-alarm command or a full CDK stack, Cursor AI handles the translation for you. Try it today and see how much faster you can alert on critical metrics.