OpenLIT: AI Observability Platform - Technical Reference

Core Functionality

Primary Purpose: One-command AI application observability for LLM monitoring and GPU tracking without traditional setup complexity.

Key Value Proposition: Auto-instruments applications without code changes using openlit-instrument python app.py instead of multi-step installation processes.

Critical Configuration Requirements

Memory Requirements

• Production Minimum: 32GB RAM for ClickHouse (non-negotiable)
• Failure Mode: OOM crashes during trace aggregations if under-provisioned
• Storage Growth: 10GB per million traces
• Container Memory: +50-100MB per instrumented process

Port Configuration

• Default OTLP Port: 4318
• Critical Issue: Conflicts with Jaeger and other collectors
• Solution: Use alternative port (e.g., 4320) and configure explicitly

Setup Methods and Reliability

Zero-Code Instrumentation

# Standard approach
openlit-instrument python app.py

• Success Rate: 90% of implementations
• Failure Scenarios: Port conflicts, OTLP collector connectivity issues
• Auto-Detection: 50+ integrations (OpenAI, Anthropic, LangChain, ChromaDB)

Docker Deployment

git clone https://github.com/openlit/openlit.git
cd openlit
docker compose up -d

• Setup Time: 2 minutes on M1 Mac, 20 minutes on older Intel hardware
• Default Credentials: user@openlit.io / openlituser (CHANGE IMMEDIATELY)
• Common Failure: ClickHouse container startup delays (wait 10 minutes before troubleshooting)

Kubernetes Deployment

helm repo add openlit https://artifacthub.io/packages/helm/openlit
helm install openlit openlit/openlit

• Memory Requirements: 8GB minimum for ClickHouse
• RBAC Issues: Operator auto-injection breaks without proper permissions
• Resource Planning: Budget for high memory consumption

Performance Impact Analysis

Latency Overhead

• Local LLM calls: 2-5ms additional latency
• Remote API calls: Negligible (network dominates)
• High-throughput scenarios: 10-20ms during trace ingestion spikes
• Marketing vs Reality: "Less than 5ms" is misleading

Cost Tracking Accuracy

• Method: Pulls actual token counts from API responses
• Accuracy: High for supported models
• Lag Time: 5-10 seconds on large datasets
• Real-World Save: $5k OpenAI bill prevention (400x retry loop detection)

GPU Monitoring Limitations

Driver Compatibility

• NVIDIA Requirements: Driver 470.x+ (older versions fail randomly)
• AMD Support: ROCm implementation less stable
• Failure Pattern: Monitoring stops after driver updates
• Recovery: Container restart required

Monitoring Capabilities

• Metrics: Utilization, memory, temperature, power draw
• Detection Examples: Thermal throttling at 83°C
• Performance Impact: Training jobs 3x slower without monitoring

Production Failure Modes

Critical Breakage Scenarios

1. ClickHouse OOM: Most common production killer
2. Storage Exhaustion: 10GB per million traces accumulates rapidly
3. Network Latency: Cross-region OTLP endpoints destroy performance
4. GPU Monitoring Drops: Post-driver update failures

3AM Debugging Checklist

# Port availability
netstat -tulpn | grep 4318

# OTLP connectivity
curl http://localhost:4318/v1/traces

# Container status
docker logs clickhouse-container

# GPU monitoring status
nvidia-smi  # or restart containers

Time Investment: 15 minutes if experienced, 2+ hours if not

Competitive Analysis

Feature	OpenLIT	Langfuse	Phoenix	Traceloop	Helicone
Zero-Code Setup	✅ Works 90%	❌ Manual SDK	✅ Auto-instrument	✅ Auto-instrument	❌ Manual SDK
GPU Monitoring	✅ NVIDIA/AMD	❌ None	❌ None	❌ None	❌ None
Self-Hosted	✅ Full control	✅ Available	✅ Available	✅ Available	❌ Cloud-only
OpenTelemetry	✅ Native support	⚠️ Limited	✅ Full support	✅ Full support	❌ Proprietary

Resource Requirements

Time Investment

• Initial Setup: 5 minutes (Docker), 30+ minutes (Kubernetes)
• Configuration Debugging: 15 minutes to 2 hours depending on expertise
• Production Readiness: Plan for memory sizing and storage management

Expertise Requirements

• Basic Setup: Minimal (follows standard Docker patterns)
• Production Deployment: Requires ClickHouse and OpenTelemetry knowledge
• Troubleshooting: OpenTelemetry debugging skills essential for 10% failure cases

Critical Warnings

Security Considerations

• Default Credentials: Change immediately from defaults
• Data Exposure: Self-hosted but prompts logged by default
• Air-Gapped Deployment: Possible but requires manual configuration

Scalability Limitations

• Dashboard Performance: Degrades significantly above 1M traces
• Time Filtering: Required for large datasets
• Storage Planning: Exponential growth requires proactive management

Integration Reality

• 50+ Integrations Claimed: Auto-detection works for popular frameworks
• Custom Clients: Manual instrumentation required for non-standard implementations
• Breaking Changes: Updates can affect instrumentation reliability

Decision Criteria

Choose OpenLIT When:

• GPU monitoring required (unique differentiator)
• Self-hosted deployment mandatory
• Zero-code instrumentation critical
• Cost tracking accuracy essential

Avoid OpenLIT When:

• Memory constraints prevent 32GB+ allocation
• Cross-region latency unacceptable
• Enterprise prompt management required (Langfuse superior)
• Managed service preferred over self-hosting

Migration Considerations

• OpenTelemetry Native: Easier migration to/from other OTLP-compatible tools
• Data Export: Standard OTLP format enables portability
• Vendor Lock-in: Minimal due to open-source nature

Implementation Success Factors

1. Memory Allocation: Plan for 32GB+ ClickHouse requirements upfront
2. Port Management: Resolve 4318 conflicts during initial setup
3. Geographic Deployment: Co-locate collectors with applications
4. Storage Strategy: Implement log rotation before production deployment
5. Driver Maintenance: Plan for GPU monitoring restarts post-updates

Maintenance Requirements

Regular Tasks

• Memory Monitoring: Track ClickHouse resource usage
• Storage Cleanup: Implement trace retention policies
• Driver Updates: Plan for GPU monitoring interruptions
• Container Restarts: Required for various failure modes

Emergency Procedures

• OOM Recovery: Increase memory allocation, restart ClickHouse
• Port Conflicts: Reconfigure OTLP endpoints
• Storage Full: Implement immediate log rotation
• Network Issues: Verify collector connectivity and geographic placement