Get started with VectorDB Observability

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.

Deploy OpenLIT

Git clone OpenLIT repository

git clone git@github.com:openlit/openlit.git

Start Docker Compose

From the root directory of the OpenLIT Repo, Run the below command:

docker compose up -d

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

Instrument your application

Python
Typescript

Not sure which method to choose? Check out Instrumentation Methods to understand the differences.

Zero-Code instrumentation
Manual instrumentation

Via CLI arguments
Via environment variables

# Install OpenLIT
pip install openlit

# Start VectorDB monitoring instantly
openlit-instrument --service-name my-vectordb-app python your_vectordb_app.py

# With custom settings for VectorDB applications
openlit-instrument \
  --otlp-endpoint http://127.0.0.1:4318 \
  --service-name my-vectordb-app \
  --environment production \
  python your_vectordb_app.py

# Install OpenLIT
pip install openlit

# Set environment variables
export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"
export OTEL_SERVICE_NAME=my-vectordb-app
export OTEL_DEPLOYMENT_ENVIRONMENT=production

# Run your VectorDB application
openlit-instrument python your_vectordb_app.py

Via function parameters
Via environment variables

Add the following two lines to your VectorDB application code:

import openlit

openlit.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:

export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"
export OTEL_SERVICE_NAME=my-vectordb-app
export OTEL_DEPLOYMENT_ENVIRONMENT=production

Add the following two lines to your VectorDB application code:

import openlit

openlit.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:

export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"
export OTEL_SERVICE_NAME=my-vectordb-app
export OTEL_DEPLOYMENT_ENVIRONMENT=production

Add the following two lines to your VectorDB application code:

import { OpenLIT } from 'openlit';

await OpenLIT.init();

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.

Integrations

60+ AI integrations with automatic instrumentation and performance tracking

Create a dashboard

Create custom visualizations with flexible widgets, queries, and real-time AI monitoring

Manage prompts

Version, deploy, and collaborate on prompts with centralized management and tracking

Zero-code observability with the OpenLIT Controller

Discover and instrument LLM traffic across Kubernetes, Docker, and Linux using eBPF — no code changes required.