Observability Stacks Comparison

This document compares different observability stack options to help you choose the right one for your needs.

Stack Comparison Matrix

Stack	Metrics	Traces	Logs	Best For	Complexity
Grafana LGTM Stack	✅ Excellent	✅ Excellent	✅ Excellent	Comprehensive observability, cost-effective	Medium
Prometheus + Grafana	✅ Excellent	⚠️ Limited	⚠️ Limited	Metrics-focused	Medium
Jaeger	❌ No	✅ Excellent	❌ No	Distributed tracing	Low
ELK Stack	⚠️ Limited	⚠️ Limited	✅ Excellent	Log aggregation	High
Seq	❌ No	❌ No	✅ Excellent	.NET structured logs	Low
Azure Monitor	✅ Good	✅ Good	✅ Good	Azure-hosted apps	Medium
Application Insights	✅ Good	✅ Good	✅ Good	Azure .NET apps	Low

Detailed Comparison

Prometheus + Grafana

Strengths:

• Excellent metrics collection and visualization
• Powerful query language (PromQL)
• Large ecosystem of exporters
• Time-series database optimized for metrics

Weaknesses:

• Limited trace support (requires additional tools)
• Log aggregation not primary focus
• Requires separate solutions for traces/logs

Use Cases:

• Metrics-heavy applications
• SRE teams focused on SLIs/SLOs
• Kubernetes-native environments

Resource Requirements:

• Prometheus: 2-4GB RAM, 50-100GB storage
• Grafana: 512MB-1GB RAM

Jaeger

Strengths:

• Excellent distributed tracing
• Simple deployment
• Good UI for trace visualization
• Supports multiple storage backends

Weaknesses:

• No metrics or logs
• Memory storage not suitable for production
• Requires separate solutions for metrics/logs

Use Cases:

• Understanding request flows
• Debugging distributed systems
• Performance analysis

Resource Requirements:

• 256MB-512MB RAM (all-in-one)
• Minimal storage (memory mode)

ELK Stack (Elasticsearch + Logstash + Kibana)

Strengths:

• Excellent log aggregation and search
• Powerful query capabilities
• Scalable architecture
• Good visualization with Kibana

Weaknesses:

• High resource requirements
• Complex setup and maintenance
• Limited metrics support
• Requires additional tools for traces

Use Cases:

• Centralized logging
• Log analysis and search
• Security event monitoring

Resource Requirements:

• Elasticsearch: 4-8GB RAM, 100GB+ storage
• Kibana: 1-2GB RAM
• Logstash: 1-2GB RAM

Seq

Strengths:

• Excellent for .NET structured logging
• Simple setup
• Good query interface
• Built-in alerting

Weaknesses:

• .NET-focused (less suitable for polyglot)
• No metrics or traces
• Commercial license for production

Use Cases:

• .NET applications
• Structured logging
• Development environments

Resource Requirements:

• 512MB-1GB RAM
• 10-50GB storage

Azure Monitor / Application Insights

Strengths:

• Integrated with Azure ecosystem
• Good for .NET applications
• Managed service (no infrastructure)
• Unified view of metrics, traces, logs

Weaknesses:

• Azure-specific
• Cost can be high at scale
• Less flexible than self-hosted

Use Cases:

• Azure-hosted applications
• .NET applications
• Teams wanting managed solutions

Resource Requirements:

• Managed service (no infrastructure)

Grafana LGTM Stack (Loki, Grafana, Tempo, Mimir)

Strengths:

• Complete observability solution (metrics, traces, logs)
• Cost-effective open-source stack
• Excellent integration between components
• Correlated observability (trace-to-logs, trace-to-metrics)
• Scalable architecture
• OpenTelemetry-native
• Efficient storage and compression

Weaknesses:

• Requires more setup than managed services
• Resource-intensive (especially for large volumes)
• Requires operational expertise

Use Cases:

• Comprehensive observability needs
• Cost-effective self-hosted solution
• High-volume telemetry
• Multi-tenant environments
• Teams wanting unified observability platform

Resource Requirements:

• Loki: 2-4GB RAM, 50-100GB storage
• Tempo: 2-4GB RAM, 50-100GB storage
• Mimir: 4-8GB RAM, 100GB+ storage
• Grafana: 1-2GB RAM

Components:

• Loki: Log aggregation (Prometheus-inspired, label-based indexing)
• Tempo: Distributed tracing backend
• Mimir: Long-term metrics storage (Prometheus-compatible)
• Grafana: Unified visualization and dashboards

See Grafana LGTM Stack Example for detailed setup instructions.

Recommended Combinations

Development

• Simple: Prometheus + Grafana + Jaeger
• Full Stack: All backends enabled

Production - Small Scale

• Metrics + Traces: Prometheus + Grafana + Jaeger
• Logs: Seq (for .NET) or ELK (for polyglot)

Production - Medium Scale

• Metrics: Prometheus + Grafana
• Traces: Jaeger with persistent storage
• Logs: ELK Stack

Production - Large Scale

• All-in-One: Grafana LGTM Stack (Loki, Grafana, Tempo, Mimir)
• Alternative: Prometheus + Grafana (clustered) + Jaeger with Elasticsearch backend + ELK Stack (clustered)

Cloud-Native (Azure)

• All: Azure Monitor / Application Insights

Decision Matrix

Choose Prometheus + Grafana if:

• ✅ You need excellent metrics visualization
• ✅ You're using Kubernetes
• ✅ You have SRE practices in place
• ❌ You don't need comprehensive logging

Choose Jaeger if:

• ✅ You need distributed tracing
• ✅ You want simple deployment
• ✅ You're debugging request flows
• ❌ You need metrics or logs

Choose ELK Stack if:

• ✅ You need centralized logging
• ✅ You have polyglot services
• ✅ You need powerful search capabilities
• ❌ You have limited resources

Choose Seq if:

• ✅ You're using .NET
• ✅ You want simple setup
• ✅ You're in development
• ❌ You need metrics or traces

Choose Azure Monitor if:

• ✅ You're on Azure
• ✅ You want managed service
• ✅ You're using .NET
• ❌ You need maximum flexibility

Choose Grafana LGTM Stack if:

• ✅ You need comprehensive observability (metrics, traces, logs)
• ✅ You want cost-effective self-hosted solution
• ✅ You need correlated observability
• ✅ You're using OpenTelemetry
• ❌ You want fully managed service

Hybrid Approaches

Recommended: Collector + Multiple Backends

Use OpenTelemetry Collector to send data to multiple backends:

Application → Collector → Prometheus (metrics)
                      → Jaeger (traces)
                      → ELK (logs)

Benefits:

• Best of all worlds
• Flexible backend selection
• Easy to change backends

Recommended: Grafana LGTM Stack

Use OpenTelemetry Collector with Grafana LGTM Stack for unified observability:

Application → Collector → Mimir (metrics)
                      → Tempo (traces)
                      → Loki (logs)
                      → Grafana (visualization)

Benefits: - Single unified platform - Correlated observability - Cost-effective - OpenTelemetry-native - Efficient storage

Cost Considerations

Self-Hosted

• Infrastructure: Compute, storage, networking
• Maintenance: Time and expertise
• Scaling: Additional resources as needed

Managed Services

• Azure Monitor: Pay per GB ingested
• CloudWatch: Pay per metric/log/trace
• Seq Cloud: Subscription-based

Migration Paths

From Direct Export to Collector

1. Deploy collector
2. Update application to point to collector
3. Configure collector to export to existing backends
4. Verify data flow
5. Remove direct export code

Between Backends

1. Configure collector to export to new backend
2. Run both backends in parallel
3. Verify new backend receives data
4. Remove old backend configuration

Examples

• Grafana LGTM Stack Example: Complete setup guide for Loki, Grafana, Tempo, and Mimir
• Prometheus + Grafana Example: Prometheus and Grafana setup
• ELK Stack Example: ELK Stack setup and configuration

Further Reading

• Observability Production Patterns
• OpenTelemetry Collector