Use this file to discover all available pages before exploring further.
OpenLIT automatically instruments VectorDBs alongside LLMs, MCP, and frameworks by default.
This guide demonstrates production-ready VectorDB observability setup with OpenTelemetry-native auto-instrumentations. Get enterprise-grade AI monitoring with zero code changes using our CLI or minimal SDK integration for complete vector database performance tracking.Learn how to implement real-time vector operations monitoring, embedding performance tracking, similarity search optimization, and cost analysis for your VectorDB applications with OpenTelemetry traces and metrics.
1
Deploy OpenLIT
1
Git clone OpenLIT repository
git clone git@github.com:openlit/openlit.git
2
Start Docker Compose
From the root directory of the OpenLIT Repo, Run the below command:
docker compose up -d
2
Install OpenLIT SDK
Want zero-code observability? Try the OpenLIT Controller
The Controller uses eBPF to automatically discover and instrument LLM traffic across Kubernetes, Docker, and Linux — no SDK required. Use SDKs for deeper application-level tracing.
Python
Typescript
pip install openlit
npm install openlit
3
Instrument your application
Python
Typescript
Not sure which method to choose? Check out Instrumentation Methods to understand the differences.
# Install OpenLITpip install openlit# Set environment variablesexport OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"export OTEL_SERVICE_NAME=my-vectordb-appexport OTEL_DEPLOYMENT_ENVIRONMENT=production# Run your VectorDB applicationopenlit-instrument python your_vectordb_app.py
Via function parameters
Via environment variables
Add the following two lines to your VectorDB application code:
import openlitopenlit.init( otlp_endpoint="http://127.0.0.1:4318", service_name="my-vectordb-app", environment="production")# Your existing VectorDB code works the same# Vector operations are traced automatically
Configure your OTLP endpoint using environment variables:
Add the following two lines to your VectorDB application code:
import openlitopenlit.init()
Manual instrumentation
Parameters
Environment Variables
Add the following two lines to your VectorDB application code:
import { OpenLIT } from 'openlit';await OpenLIT.init({ otlpEndpoint: 'http://127.0.0.1:4318', serviceName: 'my-vectordb-app', environment: 'production'});// Your existing VectorDB code works the same// Vector operations are traced automatically
Configure your OTLP endpoint using environment variables:
Add the following two lines to your VectorDB application code:
import { OpenLIT } from 'openlit';await OpenLIT.init();
4
Monitor, debug and test the quality of your vector database
Navigate to OpenLIT at 127.0.0.1:3000 to start monitoring your VectorDB applications.
You should see VectorDB-specific traces and metrics including:
Vector Operations: Track insert, update, delete, and query operations performance
Similarity Search Metrics: Monitor search latency, relevance scores, and result quality
Embedding Performance: Analyze embedding generation and storage efficiency
Index Operations: Monitor index building, updates, and optimization processes
Resource Utilization: Track memory usage, disk I/O, and computational costs
Database Performance: Monitor connection pooling, query optimization, and throughput
Send Observability telemetry to other OpenTelemetry backendsIf you wish to send telemetry directly from the SDK to another backend, you can stop the current Docker services by using the command below. For more details on sending the data to your existing OpenTelemetry backends, checkout our Supported Destinations guide.
docker compose down
If you have any questions or need support, reach out to our community.
Quickstart: LLM Evaluations
Get started with evaluating your LLM responses in 2 simple steps
Integrations
60+ AI integrations with automatic instrumentation and performance tracking
Destinations
Send telemetry to Datadog, Grafana, New Relic, and other observability stacks
Zero-code observability with the OpenLIT Controller
Discover and instrument LLM traffic across Kubernetes, Docker, and Linux using eBPF — no code changes required.
You should see VectorDB-specific traces and metrics including:If you have any questions or need support, reach out to our community.