> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlit.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Get started with VectorDB Observability
> Quickly start monitoring your Vector Database Applications in just a single line of code
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.
```mermaid theme={null}
flowchart TB;
subgraph " "
direction LR;
subgraph " "
direction LR;
OpenLIT_SDK[OpenLIT SDK] -->|Sends Traces & Metrics| OTC[OpenTelemetry Collector];
OTC -->|Stores Data| ClickHouseDB[ClickHouse];
end
subgraph " "
direction RL;
OpenLIT_UI[OpenLIT] -->|Pulls Data| ClickHouseDB;
end
end
```
```shell theme={null}
git clone git@github.com:openlit/openlit.git
```
From the root directory of the [OpenLIT Repo](https://github.com/openlit/openlit), Run the below command:
```shell theme={null}
docker compose up -d
```
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.
```shell theme={null}
pip install openlit
```
```shell theme={null}
npm install openlit
```
Not sure which method to choose? Check out [Instrumentation Methods](/latest/sdk/instrumentation-methods) to understand the differences.
```bash theme={null}
# 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
```
```bash theme={null}
# 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
```
Add the following two lines to your VectorDB application code:
```python theme={null}
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:
```shell theme={null}
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:
```python theme={null}
import openlit
openlit.init()
```
Add the following two lines to your VectorDB application code:
```javascript theme={null}
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:
```shell theme={null}
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:
```javascript theme={null}
import { OpenLIT } from 'openlit';
await OpenLIT.init();
```
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 backends**
```mermaid theme={null}
flowchart TB;
subgraph " "
direction LR;
ApplicationCode[Application Code] -->|Instrumented with| OpenLIT_SDK[OpenLIT SDK];
OpenLIT_SDK -->|Sends Traces & Metrics| OT_Backend[OpenTelemetry Backend];
end
```
If 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](/latest/sdk/destinations/overview) guide.
```sh theme={null}
docker compose down
```
If you have any questions or need support, reach out to our [community](https://join.slack.com/t/openlit/shared_invite/zt-2etnfttwg-TjP_7BZXfYg84oAukY8QRQ).
***
60+ AI integrations with automatic instrumentation and performance tracking
Create custom visualizations with flexible widgets, queries, and real-time AI monitoring
Version, deploy, and collaborate on prompts with centralized management and tracking
Discover and instrument LLM traffic across Kubernetes, Docker, and Linux using eBPF — no code changes required.