Why Your Monitoring Bill Tripled (And How I Fixed Mine)

I'm looking at our monitoring bill and it's something like 18k, maybe more. I stopped checking after it hit five figures because my eye started twitching. Someone left debug logging on after a weekend deploy and Datadog just kept charging us for every log line. Classic Friday deploy mistake that nobody talks about until it costs you actual money.

Single-vendor platforms remind me of those infomercials that promise everything for three easy payments. Looks great on paper, then you get the bill. Datadog's infrastructure monitoring is solid until you realize you're paying more for visibility than for the actual servers. New Relic's "AI insights" sound impressive in demos but mostly tell you things you already knew. Splunk costs more than most people's AWS bills and performs about as well as my laptop from 2015.

The Real Cost of Vendor Lock-in (From Someone Who's Been There)

I've managed monitoring budgets at three companies. Here's what actually happens:

Datadog pricing is unpredictable: Started at around 800 bucks a month for 20 hosts. Seemed fair. Ten months later the bill hit something like 6,500 for the same 20 hosts. Turns out they charge extra for logs (200 monthly for basic ingestion), custom metrics (5 bucks each after you hit 100), APM traces (another 300 monthly), and real-time alerting. Their sales rep called it "growth pricing." I wasn't feeling the growth.

New Relic's data units make no sense: Their pricing calculator might as well be a random number generator. Think you're paying 1,200 monthly then get a 3,200 bill. Our Rails app was apparently above baseline consumption because we logged more than their threshold. Nobody could explain what baseline means or where the threshold comes from.

The fees that just appear:

• Log ingestion that's free until it's 2k monthly
• Extra charges when your app has errors (like that's optional)
• Custom dashboards cost 50 monthly per user for pro features
• Learning four different query languages because SQL is apparently too mainstream

Why Four Tools Actually Work Better (And Cost Less)

Here's what I learned after getting burned by vendor lock-in: specialized tools that do one thing really well cost less and work better than "unified" platforms that do everything poorly.

Sentry for errors (around 150-175 monthly for our volume): Catches JavaScript exceptions with stack traces that actually help. Source maps work most of the time, which is better than I expected. When they break it's usually after deploys when webpack decides to get creative.

Datadog for infrastructure (started at 1,200, now closer to 2,800): Their agent mostly works and the error messages make sense. Use it for infrastructure monitoring. Skip their APM since New Relic does that better. Their machine learning alerts aren't very smart but the basic ones work fine.

New Relic for application performance (we pay maybe 800 or 900, their billing confuses me): When distributed tracing works it's helpful. Last month it caught a Postgres query eating 4 seconds per request. Sometimes the agent just stops sending traces and I spend an hour figuring out why.

Prometheus for custom metrics (free until you need storage): Free like Linux is free - costs nothing until you need help at 3am. Still better than paying Datadog 5 bucks per metric to count logout button clicks. PromQL is weird but you get used to it.

How This Actually Works

Forget the fancy architecture diagrams. Here's how I actually make four monitoring tools work together:

Sentry catches errors - Every 500 error and JavaScript exception gets logged with context. When something breaks I know what broke and why. Source maps usually work after deploys.

Datadog watches infrastructure - CPU spikes, memory usage, disk space. The agent takes some setup but once it's running it keeps working. I use their infrastructure stuff and ignore the rest.

New Relic traces slow requests - When users say the app is slow, New Relic shows me which database query is the problem. Distributed tracing works well when I need it.

Prometheus handles custom metrics - Business metrics, counters, anything the other tools don't cover. It's free and I control the data. PromQL takes getting used to but it's powerful.

The Part That Actually Matters: Making Them Work Together

Here's the thing nobody tells you: getting four monitoring tools to correlate data is like teaching cats to perform synchronized swimming. Possible, but painful.

Correlation IDs are a pipe dream - You add a unique ID to every request thinking you're hot shit and suddenly you're the monitoring genius. Reality check: half your correlation IDs vanish into the Bermuda Triangle of distributed systems, 30% show up in two tools max, and the rest get truncated because someone decided 64 characters was "too long." You'll spend more time debugging why correlation isn't working than debugging actual problems.

// What actually works (after 3 months of pain and 2 mental breakdowns)
// Spoiler: it's hacky as hell but works 80% of the time
const correlationId = `${Date.now()}-${Math.random().toString(36).substr(2, 9)}`;
// ^ This breaks with clock drift but whatever, at least it's unique-ish

// Shotgun approach: spray trace IDs everywhere and pray
try {
  Sentry.setTag('trace_id', correlationId);
  Sentry.setTag('request_id', req.id); // backup plan
} catch (e) {
  // Sentry SDK randomly throws exceptions for no reason
  console.warn('Sentry decided to have a moment:', e.message);
}

// New Relic is picky about attribute names, go figure
try {
  newrelic.addCustomAttribute('trace_id', correlationId);
  newrelic.addCustomAttribute('req_id', req.id);
} catch (e) {
  // Agent probably isn't loaded yet or decided to take a nap
}

logger.info('Request started', {
  trace_id: correlationId,
  method: req.method,
  url: req.url,
  user_agent: req.get('User-Agent') || 'unknown'
});
// ^ At least the logs are reliable when ELK isn't shitting itself

The shit that will actually break (trust me on this):

1. Webhooks just stop working and you won't know for weeks - They return 200 OK like everything's fine, but nothing's actually happening. Last month our Slack alerts stopped and we only found out during an outage when everyone was like "why didn't I get notified?"

2. Clock drift is the devil - Your servers are off by like 30 seconds and suddenly events are happening "in the wrong order." Sentry says the error happened before Datadog saw the CPU spike. Good luck explaining that timeline to your manager.

3. Rate limits kick in exactly when you need monitoring most - During an incident when everything's on fire, you hit API limits and correlation just... stops. Because apparently 1000 requests per minute isn't enough when your app is melting down.

Problems That Will Actually Bite You

Your monitoring will cost more than expected - Start with a $2,000/month budget, end up at $8,000/month because:

• Datadog's custom metrics are $5 each after the first 100
• New Relic charges per "data unit" and their calculator is deliberately confusing
• Prometheus storage grows like cancer if you don't tune retention
• Alert fatigue leads to ignored alerts, which leads to outages, which leads to panic purchases of premium features

Context switching will slow down incident response - Instead of one dashboard, you now have four. During a 3am outage, you'll waste 10 minutes just figuring out which tool has the information you need. We solved this with:

• One primary tool per incident type (Sentry for errors, Datadog for infrastructure)
• Slack integrations that put key metrics in one place
• Pre-built Grafana dashboards that pull from all four tools (when they work)

Tool expertise becomes a bottleneck - Your team needs to know PromQL, Datadog's query language, New Relic's NRQL, and Sentry's search syntax. Reality: one person becomes the "monitoring expert" and becomes a bottleneck for every incident.

The bottom line: this approach works but it's messier than vendor marketing suggests. Budget 40% more time and money than you think you'll need.

Essential Reading:

• Sentry Getting Started Guide - Actually clear setup instructions
• Datadog Agent Installation - The agent works once configured right
• New Relic APM Guide - PDF guide that covers the basics
• Prometheus Configuration - Dense but complete
• Observability Architecture Guide - Good overview of patterns
• Datadog Pricing Reality Check - Multiply by 3x for actual costs
• New Relic Cost Calculator - Their "data units" calculator is confusing
• Sentry Error Tracking Best Practices - Most predictable pricing of the bunch
• Prometheus vs Paid Solutions - Why free can work better
• Multi-Vendor Monitoring Strategy - Real examples from companies doing this

Enough theory. Here's how to implement this without losing your mind. I've set this up a few times now and each time something different breaks in ways I didn't expect.

Step 1: Get Networking Right First

Before installing agents everywhere, sort out your networking. This saves hours of debugging when agents stop working and you think it's the application but it's actually firewall rules.

Ports you need open:

## Basic firewall rules that work
firewall_rules:
  sentry:
    outbound:
      - sentry.io:443
      - cdn.sentry.io:443  # Source maps break without this

  datadog:
    inbound:
      - 8125  # StatsD
      - 8126  # APM traces
    outbound:
      - api.datadoghq.com:443
      - logs.datadoghq.com:443

  newrelic:
    outbound:
      - collector.newrelic.com:443
      - rpm.newrelic.com:443

  prometheus:
    inbound:
      - 9090  # Prometheus
      - 9093  # AlertManager
      - 3000  # Grafana

Things that will break:

• Datadog agent fails if it can't resolve hostnames. DNS issues make metrics disappear.
• New Relic sometimes changes collector URLs and firewalls block the new ones.
• Prometheus defaults to localhost which breaks in Docker. Use --web.listen-address=0.0.0.0:9090.
• Time sync matters. When servers disagree about time by more than 30 seconds, correlation breaks. Install chrony.

Step 2: Instrument Your App

Here's what works for adding monitoring without breaking everything. Keep it simple.

## What I run in production (Flask app)
import sentry_sdk
import newrelic.agent
from datadog import DogStatsd
import time
import os
import logging

## Datadog setup
try:
    statsd = DogStatsd(
        host=os.getenv('DATADOG_AGENT_HOST', 'localhost'),
        port=int(os.getenv('DATADOG_AGENT_PORT', '8125')),
        max_buffer_size=50
    )
    statsd.increment('app.startup.test')
except Exception as e:
    logging.warning(f"Datadog agent connection failed: {e}")
    statsd = None

## Sentry setup
if os.getenv('SENTRY_DSN'):
    sentry_sdk.init(
        dsn=os.getenv('SENTRY_DSN'),
        traces_sample_rate=0.1,  # Don't use 1.0, kills performance
        environment=os.getenv('ENVIRONMENT', 'development'),
        debug=False,
        before_send=lambda event, hint: None if 'healthcheck' in event.get('request', {}).get('url', '') else event
    )
else:
    logging.warning("SENTRY_DSN not set, error tracking disabled")

## New Relic setup
if os.path.exists('/app/newrelic.ini'):
    try:
        newrelic.agent.initialize('/app/newrelic.ini')
    except Exception as e:
        print(f"New Relic init failed: {e}")
else:
    print("New Relic config not found, APM disabled")

def track_request(method, endpoint, status_code, duration):
    try:
        if statsd:
            statsd.increment('http.requests', tags=[
                f'method:{method}',
                f'endpoint:{endpoint}',
                f'status:{status_code}'
            ])
            statsd.histogram('http.duration', duration)

        try:
            newrelic.agent.record_custom_metric('Custom/HTTP/Requests', 1)
        except AttributeError:
            pass  # Agent not initialized

    except Exception as e:
        logging.error(f"Monitoring failed: {e}")

@app.after_request
def after_request(response):
    duration = time.time() - g.get('start_time', time.time())
    track_request(
        method=request.method,
        endpoint=request.endpoint or 'unknown',
        status_code=response.status_code,
        duration=duration
    )
    return response

What breaks and why you'll want to throw your laptop out the window (during the April incident):

1. Sentry source maps randomly stop working - Usually happens right after a deploy when you need them most. Upload source maps as part of your deploy script, not CI. And for the love of god, make sure the timing is right or you'll get beautiful stack traces pointing to minified gibberish.

2. Datadog agent just... dies sometimes - No warning, no error, it just stops sending metrics. Happened to us on 3 servers last Tuesday. The agent process was running but not actually doing anything. Restart fixed it. Still don't know why.

3. New Relic config files are written by sadists - One misplaced character and the agent fails silently. No error message, no warning, just... nothing works. YAML is apparently too mainstream for them.

4. Prometheus metrics endpoint randomly times out - Usually when you need it most during an incident. Don't put the metrics endpoint on your main app server unless you enjoy debugging why metrics collection is killing your API performance.

Step 3: Making Tools Talk to Each Other (The Fun Part)

This is where things get interesting. You want all your tools to share information so when Sentry sees an error, Datadog can tell you if the server was melting down at the same time.

Cross-tool integration that actually works:

// What actually runs in production (emphasis on "actually")
app.post('/webhooks/sentry', async (req, res) => {
  try {
    const error = req.body;

    // Send to Datadog when errors spike (when it feels like working)
    if (error.level === 'error') {
      try {
        const response = await fetch('https://api.datadoghq.com/api/v1/events', {
          method: 'POST',
          headers: {
            'DD-API-KEY': process.env.DATADOG_API_KEY,
            'Content-Type': 'application/json'
          },
          body: JSON.stringify({
            title: `Sentry Error: ${error.message || 'Unknown error'} `,
            tags: [`environment:${error.environment || 'unknown'}`],
            alert_type: 'error'
          }),
          timeout: 5000  // Don't hang forever
        });

        if (!response.ok) {
          console.error(`Datadog webhook failed: ${response.status}`);
          // But don't crash because of it
        }
      } catch (webhookError) {
        console.error(`Webhook to Datadog failed: ${webhookError.message}`);
        // Webhooks fail all the time, don't worry about it
      }
    }

    res.status(200).send('OK');
  } catch (e) {
    console.error(`Sentry webhook handler crashed: ${e.message}`);
    res.status(500).send('Webhook handler failed');
  }
});

Phase 3: Advanced Correlation and Analytics

Here's the Prometheus config I use for pulling data from the other tools

## prometheus.yml configuration for unified metrics
global:
  scrape_interval: 15s
  external_labels:
    cluster: 'production'
    integration: 'unified-observability'

## Scrape Datadog metrics via OpenMetrics endpoint
scrape_configs:
  - job_name: 'datadog-openmetrics'
    static_configs:
      - targets: ['datadog-agent:8080']
    metrics_path: '/openmetrics'
    scrape_interval: 30s

  - job_name: 'newrelic-prometheus-exporter'
    static_configs:
      - targets: ['newrelic-exporter:9090']
    scrape_interval: 60s

  - job_name: 'sentry-exporter'
    static_configs:
      - targets: ['sentry-prometheus-exporter:9091']
    scrape_interval: 30s

## Remote write to New Relic for long-term storage
remote_write:
  - url: "https://metric-api.newrelic.com/prometheus/v1/write?prometheus_server=YOUR_SERVER_NAME"
    headers:
      Authorization: "Bearer ${NEW_RELIC_LICENSE_KEY}"  # Replace with your actual license key
    write_relabel_configs:
      - source_labels: [__name__]
        regex: 'sentry_.*|datadog_.*|custom_.*'
        action: keep

## Alerting rules with cross-tool correlation
rule_files:
  - "alert_rules/*.yml"

## AlertManager configuration for intelligent routing
alerting:
  alertmanagers:
    - static_configs:
        - targets:
          - alertmanager:9093

This alert saved our ass last month - here's how I set up intelligent correlation

## alert_rules/unified_observability.yml
groups:
  - name: unified_infrastructure_health
    rules:
    - alert: HighErrorRateWithInfrastructureIssue
      expr: |
        (
          rate(sentry_errors_total[5m]) > 0.1
          and
          avg(datadog_system_cpu_user) > 80
        )
        or
        (
          newrelic_apm_error_rate > 5
          and
          avg(datadog_system_memory_used_percent) > 90
        )
      for: 2m
      labels:
        severity: critical
        team: platform
        correlation: infrastructure_application
      annotations:
        summary: "High error rate detected with infrastructure stress"
        description: |
          Multiple signals indicate a correlated infrastructure and application issue:
          - Sentry error rate: {{ $value }}%
          - Infrastructure CPU: {{ with query "avg(datadog_system_cpu_user)" }}{{ . | first | value | printf "%.1f" }}{{ end }}%
          - Infrastructure Memory: {{ with query "avg(datadog_system_memory_used_percent)" }}{{ . | first | value | printf "%.1f" }}{{ end }}%

    - alert: ApplicationPerformanceDegradation
      expr: |
        (
          newrelic_apm_response_time_p95 > 2000
          and
          increase(sentry_performance_issues_total[10m]) > 5
        )
      for: 3m
      labels:
        severity: warning
        team: application
        correlation: performance_errors
      annotations:
        summary: "Application performance degradation detected"
        description: |
          Performance issues detected across multiple observability tools:
          - New Relic P95 response time: {{ $value }}ms
          - Sentry performance issues (10m): {{ with query "increase(sentry_performance_issues_total[10m])" }}{{ . | first | value | printf "%.0f" }}{{ end }}

    - alert: ReleaseImpactDetection
      expr: |
        (
          increase(sentry_errors_total{release=~"v.*"}[15m]) > 10
          and
          increase(datadog_deployment_events_total[15m]) > 0
        )
      for: 1m
      labels:
        severity: critical
        team: deployment
        correlation: release_errors
      annotations:
        summary: "Release impact detected - error spike after deployment"
        description: |
          Potential release issue detected:
          - New Sentry errors since deployment: {{ $value }}
          - Recent deployment detected in Datadog
          - Immediate investigation recommended

Phase 4: What I Learned About Running This in Production

Here's how I actually manage costs without losing visibility

## Cost optimization service
import asyncio
from datetime import datetime, timedelta
import aiohttp

class ObservabilityCostOptimizer:
    def __init__(self):
        self.cost_thresholds = {
            'sentry': {'monthly_budget': 500, 'events_per_month': 1000000},
            'datadog': {'monthly_budget': 3000, 'hosts': 50},
            'newrelic': {'monthly_budget': 2000, 'data_gb_per_month': 100},
            'prometheus': {'storage_gb': 500, 'retention_days': 30}
        }

    async def optimize_sentry_sampling(self):
        """Dynamically adjust Sentry sampling based on budget consumption"""
        current_usage = await self.get_sentry_usage()
        budget_usage_percent = current_usage['events'] / self.cost_thresholds['sentry']['events_per_month']

        if budget_usage_percent > 0.8:  # 80% of budget used
            new_sample_rate = max(0.01, 0.1 * (1 - budget_usage_percent))
            await self.update_sentry_sample_rate(new_sample_rate)

    async def optimize_datadog_metrics(self):
        """Reduce Datadog metric resolution for non-critical services"""
        high_cost_metrics = await self.identify_high_cost_datadog_metrics()

        for metric in high_cost_metrics:
            if metric['cost_impact'] > 100:  # $100+ monthly impact
                await self.reduce_metric_resolution(metric['name'], '5m')

    async def optimize_prometheus_storage(self):
        """Implement tiered storage for Prometheus data"""
        # Move data older than 7 days to compressed storage
        await self.compress_old_prometheus_data(days=7)

        # Move data older than 30 days to cold storage
        await self.archive_prometheus_data(days=30)

    async def generate_cost_report(self):
        """Generate monthly cost optimization report"""
        return {
            'total_monthly_cost': await self.calculate_total_cost(),
            'cost_by_tool': await self.get_cost_breakdown(),
            'optimization_opportunities': await self.identify_savings(),
            'recommended_actions': await self.get_cost_recommendations()
        }

Making sure your monitoring doesn't go down when everything else does

## High availability configuration
observability_ha:
  sentry:
    deployment: multi-region
    backup_strategy:
      - on_premise_relay: true
      - local_buffering: 24_hours
      - failover_dsn: backup_project

  datadog:
    agents:
      - primary_datacenter: us-east-1
      - secondary_datacenter: us-west-2
      - local_buffering: 48_hours

  newrelic:
    apm_agents:
      - circuit_breaker: enabled
      - local_buffering: 12_hours
      - failover_collector: eu_collector

  prometheus:
    federation:
      - primary: prometheus-primary:9090
      - secondary: prometheus-secondary:9090
    remote_storage:
      - thanos: enabled
      - retention: 2_years
      - backup_frequency: daily

monitoring_the_monitors:
  healthchecks:
    - endpoint: /health/sentry-relay
      interval: 30s
      timeout: 5s
    - endpoint: /health/datadog-agent
      interval: 30s
      timeout: 5s
    - endpoint: /health/newrelic-agent
      interval: 30s
      timeout: 5s
    - endpoint: /health/prometheus
      interval: 30s
      timeout: 5s

  synthetic_monitoring:
    - test_sentry_ingestion: every_5_minutes
    - test_datadog_metrics: every_5_minutes
    - test_newrelic_traces: every_5_minutes
    - test_prometheus_scraping: every_minute

This implementation guide provides the technical foundation for deploying a unified observability stack. The next section covers cost comparison and architectural trade-offs to help teams make informed decisions about tool selection and configuration.

Implementation Resources:

• Datadog Agent Setup Guide - Complete installation documentation
• Sentry SDK Integration - Step-by-step integration tutorial
• New Relic APM Installation - APM feature overview
• Prometheus Getting Started - Hello World tutorial
• Grafana Prometheus Integration - Visualization setup
• Docker Agent Configuration - Container deployment guide
• Kubernetes Monitoring Setup - K8s integration patterns
• Webhook Integration Examples - Cross-tool communication
• Datadog Cost Management - Managing monitoring expenses
• Production Deployment Checklist - Pre-production validation
• Alerting Best Practices - Alert management configuration