The OpenLIT Operator supports multiple instrumentation providers, each offering different capabilities and approaches to AI observability. This overview helps you choose the right provider for your use case.

What is Instrumentation?

Instrumentation is the process of adding observability code to your applications to collect telemetry data such as:
  • Traces: Detailed execution flows showing request paths
  • Metrics: Quantitative measurements of performance and usage
  • Logs: Structured event records for debugging and monitoring
  • Costs: Token usage and estimated API costs
The OpenLIT Operator automatically injects instrumentation code into your applications without requiring code changes.

Supported Providers

The operator supports four instrumentation providers:

How Instrumentation Works

1

Webhook Interception

The operator intercepts pod creation requests via an admission webhook
2

Label Matching

Checks if the pod matches any AutoInstrumentation selector labels
3

Init Container Injection

Injects an init container with the selected instrumentation provider
4

Environment Setup

The init container installs instrumentation packages and sets up environment variables
5

Application Start

Your application starts with instrumentation automatically enabled
6

Telemetry Collection

Traces, metrics, and costs are automatically collected and sent to your OTLP endpoint

Provider Comparison

FeatureOpenLITOpenInferenceOpenLLMetryCustom
AI Instrumentations50+30+30+Depends
Cost Tracking✅ Advanced✅ Basic✅ BasicDepends
Performance Metrics✅ Comprehensive✅ Standard✅ StandardDepends
Vendor Lock-inNoneNoneNoneNone

Troubleshooting

Common Issues