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Zhongwei Li
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references/tool_comparison.md
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references/tool_comparison.md
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# Monitoring Tools Comparison
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## Overview Matrix
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| Tool | Type | Best For | Complexity | Cost | Cloud/Self-Hosted |
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|------|------|----------|------------|------|-------------------|
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| **Prometheus** | Metrics | Kubernetes, time-series | Medium | Free | Self-hosted |
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| **Grafana** | Visualization | Dashboards, multi-source | Low-Medium | Free | Both |
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| **Datadog** | Full-stack | Ease of use, APM | Low | High | Cloud |
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| **New Relic** | Full-stack | APM, traces | Low | High | Cloud |
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| **Elasticsearch (ELK)** | Logs | Log search, analysis | High | Medium | Both |
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| **Grafana Loki** | Logs | Cost-effective logs | Medium | Free | Both |
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| **CloudWatch** | AWS-native | AWS infrastructure | Low | Medium | Cloud |
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| **Jaeger** | Tracing | Distributed tracing | Medium | Free | Self-hosted |
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| **Grafana Tempo** | Tracing | Cost-effective tracing | Medium | Free | Self-hosted |
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---
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## Metrics Platforms
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### Prometheus
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**Type**: Open-source time-series database
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**Strengths**:
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- ✅ Industry standard for Kubernetes
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- ✅ Powerful query language (PromQL)
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- ✅ Pull-based model (no agent config)
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- ✅ Service discovery
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- ✅ Free and open source
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- ✅ Huge ecosystem (exporters for everything)
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**Weaknesses**:
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- ❌ No built-in dashboards (need Grafana)
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- ❌ Single-node only (no HA without federation)
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- ❌ Limited long-term storage (need Thanos/Cortex)
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- ❌ Steep learning curve for PromQL
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**Best For**:
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- Kubernetes monitoring
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- Infrastructure metrics
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- Custom application metrics
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- Organizations that need control
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**Pricing**: Free (open source)
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**Setup Complexity**: Medium
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**Example**:
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```yaml
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# prometheus.yml
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scrape_configs:
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- job_name: 'app'
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static_configs:
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- targets: ['localhost:8080']
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```
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---
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### Datadog
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**Type**: SaaS monitoring platform
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**Strengths**:
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- ✅ Easy to set up (install agent, done)
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- ✅ Beautiful pre-built dashboards
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- ✅ APM, logs, metrics, traces in one platform
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- ✅ Great anomaly detection
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- ✅ Excellent integrations (500+)
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- ✅ Good mobile app
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**Weaknesses**:
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- ❌ Very expensive at scale
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- ❌ Vendor lock-in
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- ❌ Cost can be unpredictable (per-host pricing)
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- ❌ Limited PromQL support
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**Best For**:
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- Teams that want quick setup
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- Companies prioritizing ease of use over cost
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- Organizations needing full observability
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**Pricing**: $15-$31/host/month + custom metrics fees
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**Setup Complexity**: Low
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**Example**:
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```bash
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# Install agent
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DD_API_KEY=xxx bash -c "$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script.sh)"
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```
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---
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### New Relic
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**Type**: SaaS application performance monitoring
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**Strengths**:
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- ✅ Excellent APM capabilities
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- ✅ User-friendly interface
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- ✅ Good transaction tracing
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- ✅ Comprehensive alerting
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- ✅ Generous free tier
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**Weaknesses**:
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- ❌ Can get expensive at scale
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- ❌ Vendor lock-in
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- ❌ Query language less powerful than PromQL
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- ❌ Limited customization
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**Best For**:
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- Application performance monitoring
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- Teams focused on APM over infrastructure
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- Startups (free tier is generous)
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**Pricing**: Free up to 100GB/month, then $0.30/GB
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**Setup Complexity**: Low
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**Example**:
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```python
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import newrelic.agent
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newrelic.agent.initialize('newrelic.ini')
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```
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---
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### CloudWatch
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**Type**: AWS-native monitoring
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**Strengths**:
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- ✅ Zero setup for AWS services
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- ✅ Native integration with AWS
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- ✅ Automatic dashboards for AWS resources
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- ✅ Tightly integrated with other AWS services
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- ✅ Good for cost if already on AWS
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**Weaknesses**:
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- ❌ AWS-only (not multi-cloud)
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- ❌ Limited query capabilities
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- ❌ High costs for custom metrics
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- ❌ Basic visualization
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- ❌ 1-minute minimum resolution
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**Best For**:
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- AWS-centric infrastructure
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- Quick setup for AWS services
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- Organizations already invested in AWS
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**Pricing**:
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- First 10 custom metrics: Free
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- Additional: $0.30/metric/month
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- API calls: $0.01/1000 requests
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**Setup Complexity**: Low (for AWS), Medium (for custom metrics)
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**Example**:
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```python
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import boto3
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cloudwatch = boto3.client('cloudwatch')
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cloudwatch.put_metric_data(
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Namespace='MyApp',
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MetricData=[{'MetricName': 'RequestCount', 'Value': 1}]
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)
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```
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---
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### Grafana Cloud / Mimir
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**Type**: Managed Prometheus-compatible
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**Strengths**:
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- ✅ Prometheus-compatible (PromQL)
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- ✅ Managed service (no ops burden)
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- ✅ Good cost model (pay for what you use)
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- ✅ Grafana dashboards included
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- ✅ Long-term storage
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**Weaknesses**:
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- ❌ Relatively new (less mature)
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- ❌ Some Prometheus features missing
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- ❌ Requires Grafana for visualization
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**Best For**:
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- Teams wanting Prometheus without ops overhead
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- Multi-cloud environments
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- Organizations already using Grafana
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**Pricing**: $8/month + $0.29/1M samples
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**Setup Complexity**: Low-Medium
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---
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## Logging Platforms
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### Elasticsearch (ELK Stack)
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**Type**: Open-source log search and analytics
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**Full Stack**: Elasticsearch + Logstash + Kibana
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**Strengths**:
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- ✅ Powerful search capabilities
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- ✅ Rich query language
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- ✅ Great for log analysis
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- ✅ Mature ecosystem
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- ✅ Can handle large volumes
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- ✅ Flexible data model
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**Weaknesses**:
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- ❌ Complex to operate
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- ❌ Resource intensive (RAM hungry)
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- ❌ Expensive at scale
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- ❌ Requires dedicated ops team
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- ❌ Slow for high-cardinality queries
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**Best For**:
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- Large organizations with ops teams
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- Deep log analysis needs
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- Search-heavy use cases
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**Pricing**: Free (open source) + infrastructure costs
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**Infrastructure**: ~$500-2000/month for medium scale
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**Setup Complexity**: High
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**Example**:
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```json
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PUT /logs-2024.10/_doc/1
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{
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"timestamp": "2024-10-28T14:32:15Z",
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"level": "error",
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"message": "Payment failed"
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}
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```
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---
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### Grafana Loki
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**Type**: Log aggregation system
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**Strengths**:
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- ✅ Cost-effective (labels only, not full-text indexing)
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- ✅ Easy to operate
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- ✅ Prometheus-like label model
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- ✅ Great Grafana integration
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- ✅ Low resource usage
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- ✅ Fast time-range queries
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**Weaknesses**:
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- ❌ Limited full-text search
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- ❌ Requires careful label design
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- ❌ Younger ecosystem than ELK
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- ❌ Not ideal for complex queries
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**Best For**:
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- Cost-conscious organizations
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- Kubernetes environments
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- Teams already using Prometheus
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- Time-series log queries
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**Pricing**: Free (open source) + infrastructure costs
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**Infrastructure**: ~$100-500/month for medium scale
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**Setup Complexity**: Medium
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**Example**:
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```logql
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{job="api", environment="prod"} |= "error" | json | level="error"
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```
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---
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### Splunk
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**Type**: Enterprise log management
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**Strengths**:
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- ✅ Extremely powerful search
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- ✅ Great for security/compliance
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- ✅ Mature platform
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- ✅ Enterprise support
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- ✅ Machine learning features
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**Weaknesses**:
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- ❌ Very expensive
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- ❌ Complex pricing (per GB ingested)
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- ❌ Steep learning curve
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- ❌ Heavy resource usage
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**Best For**:
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- Large enterprises
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- Security operations centers (SOCs)
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- Compliance-heavy industries
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**Pricing**: $150-$1800/GB/month (depending on tier)
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**Setup Complexity**: Medium-High
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---
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### CloudWatch Logs
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**Type**: AWS-native log management
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**Strengths**:
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- ✅ Zero setup for AWS services
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- ✅ Integrated with AWS ecosystem
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- ✅ CloudWatch Insights for queries
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- ✅ Reasonable cost for low volume
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**Weaknesses**:
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- ❌ AWS-only
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- ❌ Limited query capabilities
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- ❌ Expensive at high volume
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- ❌ Basic visualization
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**Best For**:
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- AWS-centric applications
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- Low-volume logging
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- Simple log aggregation
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**Pricing**: Tiered (as of May 2025)
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- Vended Logs: $0.50/GB (first 10TB), $0.25/GB (next 20TB), then lower tiers
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- Standard logs: $0.50/GB flat
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- Storage: $0.03/GB
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**Setup Complexity**: Low (AWS), Medium (custom)
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---
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### Sumo Logic
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**Type**: SaaS log management
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**Strengths**:
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- ✅ Easy to use
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- ✅ Good for cloud-native apps
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- ✅ Real-time analytics
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- ✅ Good compliance features
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**Weaknesses**:
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- ❌ Expensive at scale
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- ❌ Vendor lock-in
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- ❌ Limited customization
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**Best For**:
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- Cloud-native applications
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- Teams wanting managed solution
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- Security and compliance use cases
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**Pricing**: $90-$180/GB/month
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**Setup Complexity**: Low
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---
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## Tracing Platforms
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### Jaeger
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**Type**: Open-source distributed tracing
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**Strengths**:
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- ✅ Industry standard
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- ✅ CNCF graduated project
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- ✅ Supports OpenTelemetry
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- ✅ Good UI
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- ✅ Free and open source
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**Weaknesses**:
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- ❌ Requires separate storage backend
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- ❌ Limited query capabilities
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- ❌ No built-in analytics
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**Best For**:
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- Microservices architectures
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- Kubernetes environments
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- OpenTelemetry users
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**Pricing**: Free (open source) + storage costs
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**Setup Complexity**: Medium
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---
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### Grafana Tempo
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**Type**: Open-source distributed tracing
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**Strengths**:
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- ✅ Cost-effective (object storage)
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- ✅ Easy to operate
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- ✅ Great Grafana integration
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- ✅ TraceQL query language
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- ✅ Supports OpenTelemetry
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**Weaknesses**:
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- ❌ Younger than Jaeger
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- ❌ Limited third-party integrations
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- ❌ Requires Grafana for UI
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**Best For**:
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- Cost-conscious organizations
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- Teams using Grafana stack
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- High trace volumes
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|
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**Pricing**: Free (open source) + storage costs
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**Setup Complexity**: Medium
|
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|
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---
|
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|
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### Datadog APM
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**Type**: SaaS application performance monitoring
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**Strengths**:
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- ✅ Easy to set up
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- ✅ Excellent trace visualization
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- ✅ Integrated with metrics/logs
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- ✅ Automatic service map
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- ✅ Good profiling features
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**Weaknesses**:
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- ❌ Expensive ($31/host/month)
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- ❌ Vendor lock-in
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- ❌ Limited sampling control
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**Best For**:
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- Teams wanting ease of use
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- Organizations already using Datadog
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- Complex microservices
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**Pricing**: $31/host/month + $1.70/million spans
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**Setup Complexity**: Low
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|
||||
---
|
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|
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### AWS X-Ray
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|
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**Type**: AWS-native distributed tracing
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**Strengths**:
|
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- ✅ Native AWS integration
|
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- ✅ Automatic instrumentation for AWS services
|
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- ✅ Low cost
|
||||
|
||||
**Weaknesses**:
|
||||
- ❌ AWS-only
|
||||
- ❌ Basic UI
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- ❌ Limited query capabilities
|
||||
|
||||
**Best For**:
|
||||
- AWS-centric applications
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- Serverless architectures (Lambda)
|
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- Cost-sensitive projects
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|
||||
**Pricing**: $5/million traces, first 100k free/month
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|
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**Setup Complexity**: Low (AWS), Medium (custom)
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|
||||
---
|
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|
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## Full-Stack Observability
|
||||
|
||||
### Datadog (Full Platform)
|
||||
|
||||
**Components**: Metrics, logs, traces, RUM, synthetics
|
||||
|
||||
**Strengths**:
|
||||
- ✅ Everything in one platform
|
||||
- ✅ Excellent user experience
|
||||
- ✅ Correlation across signals
|
||||
- ✅ Great for teams
|
||||
|
||||
**Weaknesses**:
|
||||
- ❌ Very expensive ($50-100+/host/month)
|
||||
- ❌ Vendor lock-in
|
||||
- ❌ Unpredictable costs
|
||||
|
||||
**Total Cost** (example 100 hosts):
|
||||
- Infrastructure: $3,100/month
|
||||
- APM: $3,100/month
|
||||
- Logs: ~$2,000/month
|
||||
- **Total: ~$8,000/month**
|
||||
|
||||
---
|
||||
|
||||
### Grafana Stack (LGTM)
|
||||
|
||||
**Components**: Loki (logs), Grafana (viz), Tempo (traces), Mimir/Prometheus (metrics)
|
||||
|
||||
**Strengths**:
|
||||
- ✅ Open source and cost-effective
|
||||
- ✅ Unified visualization
|
||||
- ✅ Prometheus-compatible
|
||||
- ✅ Great for cloud-native
|
||||
|
||||
**Weaknesses**:
|
||||
- ❌ Requires self-hosting or Grafana Cloud
|
||||
- ❌ More ops burden
|
||||
- ❌ Less polished than commercial tools
|
||||
|
||||
**Total Cost** (self-hosted, 100 hosts):
|
||||
- Infrastructure: ~$1,500/month
|
||||
- Ops time: Variable
|
||||
- **Total: ~$1,500-3,000/month**
|
||||
|
||||
---
|
||||
|
||||
### Elastic Observability
|
||||
|
||||
**Components**: Elasticsearch (logs), Kibana (viz), APM, metrics
|
||||
|
||||
**Strengths**:
|
||||
- ✅ Powerful search
|
||||
- ✅ Mature platform
|
||||
- ✅ Good for log-heavy use cases
|
||||
|
||||
**Weaknesses**:
|
||||
- ❌ Complex to operate
|
||||
- ❌ Expensive infrastructure
|
||||
- ❌ Resource intensive
|
||||
|
||||
**Total Cost** (self-hosted, 100 hosts):
|
||||
- Infrastructure: ~$3,000-5,000/month
|
||||
- Ops time: High
|
||||
- **Total: ~$4,000-7,000/month**
|
||||
|
||||
---
|
||||
|
||||
### New Relic One
|
||||
|
||||
**Components**: Metrics, logs, traces, synthetics
|
||||
|
||||
**Strengths**:
|
||||
- ✅ Generous free tier (100GB)
|
||||
- ✅ User-friendly
|
||||
- ✅ Good for startups
|
||||
|
||||
**Weaknesses**:
|
||||
- ❌ Costs increase quickly after free tier
|
||||
- ❌ Vendor lock-in
|
||||
|
||||
**Total Cost**:
|
||||
- Free: up to 100GB/month
|
||||
- Paid: $0.30/GB beyond 100GB
|
||||
|
||||
---
|
||||
|
||||
## Cloud Provider Native
|
||||
|
||||
### AWS (CloudWatch + X-Ray)
|
||||
|
||||
**Use When**:
|
||||
- Primarily on AWS
|
||||
- Simple monitoring needs
|
||||
- Want minimal setup
|
||||
|
||||
**Avoid When**:
|
||||
- Multi-cloud environment
|
||||
- Need advanced features
|
||||
- High log volume (expensive)
|
||||
|
||||
**Cost** (example):
|
||||
- 100 EC2 instances with basic metrics: ~$150/month
|
||||
- 1TB logs: ~$500/month ingestion + storage
|
||||
- X-Ray: ~$50/month
|
||||
|
||||
---
|
||||
|
||||
### GCP (Cloud Monitoring + Cloud Trace)
|
||||
|
||||
**Use When**:
|
||||
- Primarily on GCP
|
||||
- Using GKE
|
||||
- Want tight GCP integration
|
||||
|
||||
**Avoid When**:
|
||||
- Multi-cloud environment
|
||||
- Need advanced querying
|
||||
|
||||
**Cost** (example):
|
||||
- First 150MB/month per resource: Free
|
||||
- Additional: $0.2508/MB
|
||||
|
||||
---
|
||||
|
||||
### Azure (Azure Monitor)
|
||||
|
||||
**Use When**:
|
||||
- Primarily on Azure
|
||||
- Using AKS
|
||||
- Need Azure integration
|
||||
|
||||
**Avoid When**:
|
||||
- Multi-cloud
|
||||
- Need advanced features
|
||||
|
||||
**Cost** (example):
|
||||
- First 5GB: Free
|
||||
- Additional: $2.76/GB
|
||||
|
||||
---
|
||||
|
||||
## Decision Matrix
|
||||
|
||||
### Choose Prometheus + Grafana If:
|
||||
- ✅ Using Kubernetes
|
||||
- ✅ Want control and customization
|
||||
- ✅ Have ops capacity
|
||||
- ✅ Budget-conscious
|
||||
- ✅ Need Prometheus ecosystem
|
||||
|
||||
### Choose Datadog If:
|
||||
- ✅ Want ease of use
|
||||
- ✅ Need full observability now
|
||||
- ✅ Budget allows ($8k+/month for 100 hosts)
|
||||
- ✅ Limited ops team
|
||||
- ✅ Need excellent UX
|
||||
|
||||
### Choose ELK If:
|
||||
- ✅ Heavy log analysis needs
|
||||
- ✅ Need powerful search
|
||||
- ✅ Have dedicated ops team
|
||||
- ✅ Compliance requirements
|
||||
- ✅ Willing to invest in infrastructure
|
||||
|
||||
### Choose Grafana Stack (LGTM) If:
|
||||
- ✅ Want open source full stack
|
||||
- ✅ Cost-effective solution
|
||||
- ✅ Cloud-native architecture
|
||||
- ✅ Already using Prometheus
|
||||
- ✅ Have some ops capacity
|
||||
|
||||
### Choose New Relic If:
|
||||
- ✅ Startup with free tier
|
||||
- ✅ APM is priority
|
||||
- ✅ Want easy setup
|
||||
- ✅ Don't need heavy customization
|
||||
|
||||
### Choose Cloud Native (CloudWatch/etc) If:
|
||||
- ✅ Single cloud provider
|
||||
- ✅ Simple needs
|
||||
- ✅ Want minimal setup
|
||||
- ✅ Low to medium scale
|
||||
|
||||
---
|
||||
|
||||
## Cost Comparison
|
||||
|
||||
**Example: 100 hosts, 1TB logs/month, 1M spans/day**
|
||||
|
||||
| Solution | Monthly Cost | Setup | Ops Burden |
|
||||
|----------|-------------|--------|------------|
|
||||
| **Prometheus + Loki + Tempo** | $1,500 | Medium | Medium |
|
||||
| **Grafana Cloud** | $3,000 | Low | Low |
|
||||
| **Datadog** | $8,000 | Low | None |
|
||||
| **New Relic** | $3,500 | Low | None |
|
||||
| **ELK Stack** | $4,000 | High | High |
|
||||
| **CloudWatch** | $2,000 | Low | Low |
|
||||
|
||||
---
|
||||
|
||||
## Recommendations by Company Size
|
||||
|
||||
### Startup (< 10 engineers)
|
||||
**Recommendation**: New Relic or Grafana Cloud
|
||||
- Minimal ops burden
|
||||
- Good free tiers
|
||||
- Easy to get started
|
||||
|
||||
### Small Company (10-50 engineers)
|
||||
**Recommendation**: Prometheus + Grafana + Loki (self-hosted or cloud)
|
||||
- Cost-effective
|
||||
- Growing ops capacity
|
||||
- Flexibility
|
||||
|
||||
### Medium Company (50-200 engineers)
|
||||
**Recommendation**: Datadog or Grafana Stack
|
||||
- Datadog if budget allows
|
||||
- Grafana Stack if cost-conscious
|
||||
|
||||
### Large Enterprise (200+ engineers)
|
||||
**Recommendation**: Build observability platform
|
||||
- Mix of tools based on needs
|
||||
- Dedicated observability team
|
||||
- Custom integrations
|
||||
Reference in New Issue
Block a user