Prometheus vs DataDog vs New Relic: Monitoring Showdown

10/4/2025
8-minute read

The Modern Monitoring Landscape

Application monitoring has evolved from simple uptime checks to comprehensive observability platforms that provide metrics, logs, traces, and business insights. While Prometheus pioneered the open-source pull-based monitoring approach, commercial platforms like DataDog and New Relic offer integrated solutions with advanced analytics and machine learning capabilities.

The choice between open-source and commercial monitoring affects not just costs but also team workflows, data ownership, and long-term observability strategies. Modern applications demand real-time insights across distributed systems, making monitoring platform selection critical for operational excellence.

Architecture and Data Collection

Understanding the fundamental architectures reveals each platform’s strengths and limitations:

Feature	Prometheus	DataDog	New Relic
Collection Model	Pull-based scraping	Agent-based push	Agent-based push
Data Storage	Time-series (TSDB)	Proprietary	Proprietary cloud
Retention	Configurable (local)	15 months (paid)	8 days-13 months
Data Format	OpenMetrics/Prometheus	Proprietary	Proprietary
High Availability	Manual clustering	Built-in	Built-in
Query Language	PromQL	Custom + SQL	NRQL

Prometheus Pull-Based Architecture

Prometheus scrapes metrics from configured endpoints at regular intervals:

# Prometheus configuration
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'kubernetes-pods'
    kubernetes_sd_configs:
    - role: pod
    relabel_configs:
    - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
      action: keep
      regex: true
    - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
      action: replace
      target_label: __metrics_path__
      regex: (.+)

// Go application metrics exposition
package main

import (
    "net/http"
    "github.com/prometheus/client_golang/prometheus"
    "github.com/prometheus/client_golang/prometheus/promhttp"
)

var (
    httpRequests = prometheus.NewCounterVec(
        prometheus.CounterOpts{
            Name: "http_requests_total",
            Help: "Total number of HTTP requests",
        },
        []string{"method", "status"},
    )
)

func init() {
    prometheus.MustRegister(httpRequests)
}

func handler(w http.ResponseWriter, r *http.Request) {
    httpRequests.WithLabelValues(r.Method, "200").Inc()
    w.Write([]byte("Hello World"))
}

func main() {
    http.HandleFunc("/", handler)
    http.Handle("/metrics", promhttp.Handler())
    http.ListenAndServe(":8080", nil)
}

DataDog Agent-Based Collection

DataDog agents push metrics to the platform with automatic discovery:

# DataDog Agent configuration
api_key: "your-api-key"
site: "datadoghq.com"
logs_enabled: true
process_config:
  enabled: true
apm_config:
  enabled: true

# Custom metrics
init_config:
instances:
  - prometheus_url: http://localhost:8080/metrics
    namespace: "myapp"
    metrics:
      - http_requests_total
      - go_memstats_alloc_bytes

# Python application with DataDog integration
from datadog import initialize, statsd
import time

initialize(
    api_key='your-api-key',
    app_key='your-app-key'
)

# Custom metrics
@statsd.timed('myapp.request.duration')
def process_request():
    statsd.increment('myapp.request.count')
    # Application logic
    time.sleep(0.1)
    statsd.gauge('myapp.queue.size', 42)

New Relic Agent Integration

New Relic provides language-specific agents with automatic instrumentation:

// Node.js application with New Relic
require('newrelic');
const express = require('express');
const app = express();

// Custom events and metrics
const newrelic = require('newrelic');

app.get('/api/users', (req, res) => {
  // Custom metric
  newrelic.recordMetric('Custom/API/Users/RequestCount', 1);
  
  // Custom event
  newrelic.recordCustomEvent('UserAPIAccess', {
    userId: req.user.id,
    endpoint: '/api/users',
    responseTime: Date.now() - req.startTime
  });
  
  res.json({ users: [] });
});

Metrics Collection and Storage

Performance and Scale Characteristics

Metric	Prometheus	DataDog	New Relic
Ingestion Rate	100K-1M samples/sec¹	10M+ metrics/sec²	1M+ events/sec²
Storage Efficiency	1.3 bytes/sample¹	Compressed cloud²	Cloud-optimized²
Query Performance	Fast (local TSDB)¹	Fast (distributed)²	Fast (distributed)²
Cardinality Limits	High (millions)¹	Very high²	Very high²
Retention Cost	Storage-based¹	Linear pricing²	Tiered pricing²

¹ Prometheus official documentation and CNCF performance studies
² Vendor-reported performance metrics and customer case studies

Data Model Comparison

Prometheus metrics use labels for dimensionality:

# PromQL queries
http_requests_total{job="api-server", status="200"}
rate(http_requests_total[5m])
histogram_quantile(0.95, http_request_duration_seconds_bucket)

# Complex aggregations
sum(rate(http_requests_total[5m])) by (service)
increase(error_count[1h]) > 100

DataDog metrics support tags and advanced analytics:

-- DataDog query syntax
avg:system.cpu.user{environment:production} by {host}
sum:myapp.requests.count{status:error}.as_rate()
anomalies(avg:myapp.response_time{service:api}, 'basic', 2)

New Relic NRQL provides SQL-like querying:

-- NRQL queries
SELECT average(duration) FROM Transaction WHERE appName = 'MyApp'
SELECT count(*) FROM Transaction FACET name TIMESERIES
SELECT percentile(responseTime, 95) FROM PageView SINCE 1 hour ago

Alerting and Incident Management

Alert Configuration Approaches

Prometheus Alertmanager configuration:

# Alertmanager rules
groups:
- name: api.rules
  rules:
  - alert: HighErrorRate
    expr: rate(http_requests_total{status=~"5.."}[5m]) > 0.1
    for: 5m
    labels:
      severity: critical
    annotations:
      summary: "High error rate detected"
      description: "{{ $labels.job }} has error rate above 10%"

  - alert: HighLatency
    expr: histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m])) > 0.5
    for: 2m
    labels:
      severity: warning

# Routing configuration
route:
  group_by: ['alertname']
  group_wait: 10s
  group_interval: 10s
  repeat_interval: 1h
  receiver: 'web.hook'

receivers:
- name: 'web.hook'
  slack_configs:
  - api_url: 'https://hooks.slack.com/services/...'
    channel: '#alerts'

DataDog monitors with machine learning:

{
  "name": "High error rate on API",
  "type": "metric alert",
  "query": "avg(last_5m):sum:myapp.requests.error{service:api}.as_rate() > 0.1",
  "message": "Error rate is above 10% @slack-alerts",
  "tags": ["service:api", "team:backend"],
  "options": {
    "thresholds": {
      "critical": 0.1,
      "warning": 0.05
    },
    "notify_no_data": true,
    "require_full_window": false
  }
}

New Relic alerting with conditions:

// New Relic alert via API
const alert = {
  policy: {
    name: "API Performance Policy",
    incident_preference: "PER_CONDITION"
  },
  conditions: [{
    type: "apm_app_metric",
    name: "High Response Time",
    entities: ["application-id"],
    metric: "response_time_web",
    condition_scope: "application",
    terms: [{
      duration: "5",
      operator: "above",
      priority: "critical",
      threshold: "0.5",
      time_function: "all"
    }]
  }]
};

Incident Response Integration

Feature	Prometheus	DataDog	New Relic
On-call Management	External tools	Built-in + integrations	Built-in + integrations
Escalation Policies	Via Alertmanager	Native	Native
Incident Timeline	External	Automated	Automated
Root Cause Analysis	Manual	ML-assisted	ML-assisted
Notification Channels	Webhook-based	400+ integrations	100+ integrations

Visualization and Dashboards

Grafana with Prometheus:

{
  "dashboard": {
    "title": "API Performance Dashboard",
    "panels": [
      {
        "title": "Request Rate",
        "type": "graph",
        "targets": [
          {
            "expr": "sum(rate(http_requests_total[5m])) by (service)",
            "legendFormat": "{{ service }}"
          }
        ]
      },
      {
        "title": "Error Rate",
        "type": "singlestat",
        "targets": [
          {
            "expr": "sum(rate(http_requests_total{status=~\"5..\"}[5m])) / sum(rate(http_requests_total[5m]))"
          }
        ]
      }
    ]
  }
}

DataDog native dashboards:

{
  "title": "Application Overview",
  "widgets": [
    {
      "definition": {
        "type": "timeseries",
        "requests": [
          {
            "q": "avg:myapp.response_time{service:api}",
            "display_type": "line"
          }
        ],
        "title": "Response Time"
      }
    },
    {
      "definition": {
        "type": "toplist",
        "requests": [
          {
            "q": "top(avg:myapp.errors{*} by {endpoint}, 10, 'mean', 'desc')"
          }
        ]
      }
    }
  ]
}

Visualization Capabilities

Feature	Prometheus/Grafana	DataDog	New Relic
Chart Types	20+ via Grafana	15+ native	10+ native
Custom Queries	Full PromQL	Custom + SQL	NRQL
Template Variables	Advanced	Basic	Basic
Embedding	Public/private	Team sharing	Account sharing
Mobile Access	Responsive	Native apps	Native apps

Cost Analysis and Pricing Models

Pricing Structure Comparison

Factor	Prometheus	DataDog	New Relic
Base Cost	Free (self-hosted)	$15/host/month	$25/100GB/month
Storage Costs	Infrastructure	Included	Included
Ingestion Costs	None	$0.10/1M metrics	$0.25/GB
User Limits	None	Per user pricing	Full platform access
Data Retention	Custom	15 months max	13 months max
Enterprise Features	OSS + support	Enterprise tier	Enterprise tier

Total Cost of Ownership

Prometheus self-hosted (100 services):

# Infrastructure costs (annual)
Compute: 3 x c5.xlarge = $3,000
Storage: 1TB SSD = $1,200
Networking: Data transfer = $500
Staff: 0.5 FTE DevOps = $75,000
Total: ~$79,700/year

DataDog hosted (100 hosts):

# DataDog pricing (annual)
Infrastructure Monitoring: 100 hosts × $15 × 12 = $18,000
APM: 100 hosts × $31 × 12 = $37,200
Log Management: 50GB/day × $1.27 × 365 = $23,206
Custom Metrics: 1M/month × $0.05 × 12 = $600
Total: ~$79,006/year

New Relic One (100GB/month):

# New Relic pricing (annual)
Platform: $25/100GB × 12 = $300 (first 100GB)
Additional Data: 500GB × $0.25 × 12 = $1,500
Enterprise Features: $750/month × 12 = $9,000
Total: ~$10,800/year

Observability and Integration

APM and Distributed Tracing

Prometheus with Jaeger:

// OpenTelemetry with Prometheus metrics
import (
    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/exporters/prometheus"
    "go.opentelemetry.io/otel/exporters/jaeger"
)

func initTracing() {
    // Jaeger for traces
    jaegerExporter, _ := jaeger.New(
        jaeger.WithCollectorEndpoint("http://jaeger:14268/api/traces"),
    )
    
    // Prometheus for metrics
    promExporter, _ := prometheus.New()
    
    tracerProvider := trace.NewTracerProvider(
        trace.WithBatcher(jaegerExporter),
    )
    otel.SetTracerProvider(tracerProvider)
}

DataDog APM integration:

# Python APM with DataDog
from ddtrace import patch_all, tracer
patch_all()

@tracer.wrap("database.query")
def query_database(query):
    with tracer.trace("db.execute") as span:
        span.set_tag("db.statement", query)
        span.set_tag("service.name", "user-service")
        return execute_query(query)

New Relic distributed tracing:

// Node.js with New Relic
const newrelic = require('newrelic');

async function processOrder(orderId) {
  return newrelic.startBackgroundTransaction('process-order', async () => {
    const span = newrelic.getTransaction();
    span.addAttribute('orderId', orderId);
    
    // Process order logic
    await paymentService.charge(order);
    await inventoryService.reserve(order);
    
    return order;
  });
}

Enterprise Features and Security

Security and Compliance

Feature	Prometheus	DataDog	New Relic
Data Encryption	TLS (manual)	TLS (automatic)	TLS (automatic)
Access Control	Basic auth	RBAC + SSO	RBAC + SSO
Audit Logging	Limited	Complete	Complete
Compliance	Self-managed	SOC2, GDPR, HIPAA	SOC2, GDPR, HIPAA
Data Residency	Self-controlled	Multi-region	Multi-region
API Security	Token-based	Key + OAuth	Key + OAuth

High Availability and Scaling

Prometheus HA setup:

# Prometheus HA with Thanos
version: '3'
services:
  prometheus-1:
    image: prom/prometheus
    command:
      - '--storage.tsdb.path=/prometheus'
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.min-block-duration=2h'
      - '--storage.tsdb.max-block-duration=2h'
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      
  thanos-sidecar:
    image: thanosio/thanos
    command:
      - 'sidecar'
      - '--tsdb.path=/prometheus'
      - '--prometheus.url=http://prometheus-1:9090'
      - '--objstore.config-file=/bucket.yml'

Migration and Adoption Strategies

From Prometheus to Commercial

Organizations typically migrate incrementally:

Example Hybrid Monitoring Config (for illustration only)

# Hybrid monitoring approach
# Keep Prometheus for infrastructure metrics
# Add DataDog for APM and business metrics
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
data:
  prometheus.yml: |
    global:
      external_labels:
        region: 'us-west-2'
        environment: 'production'
    
    remote_write:
    - url: "https://api.datadoghq.com/api/v1/series"
      basic_auth:
        username: "datadog"
        password: "api-key"

Note: This configuration is for demonstration purposes only and should be adapted, reviewed, and security-tested before any production use.

Tool Selection Framework

Choose Prometheus when:

Open-source ecosystem is preferred
Data sovereignty is critical
Custom scaling requirements exist
Cost optimization is priority
Engineering team has monitoring expertise

Choose DataDog when:

Rapid deployment is needed
Comprehensive feature set required
Multi-cloud environment
Business metrics integration important
Managed service preferred

Choose New Relic when:

Application performance focus
Simple pricing model preferred
Full-stack observability needed
AI-powered insights valuable
Quick time-to-value required

The monitoring landscape continues evolving with observability becoming table stakes for modern applications. Prometheus remains the gold standard for infrastructure monitoring with its pull-based model and extensive ecosystem. DataDog excels as a comprehensive platform for organizations seeking managed services and advanced analytics. New Relic focuses on application performance with simplified pricing and AI-powered insights.

Code Samples and Benchmarks Disclaimer

Important Note: All code examples, configurations, monitoring setups, and performance benchmarks provided in this article are for educational and demonstration purposes only. These samples are simplified for clarity and should not be used directly in production environments without proper review, security assessment, and adaptation to your specific requirements. Performance metrics are based on specific test conditions and may vary significantly in real-world deployments. Always conduct thorough testing, follow security best practices, and consult official documentation before implementing any monitoring solution in production systems.