Milvus on GCP - Developer Guide

Welcome to the Meetrix Milvus developer guide for Google Cloud Platform! Milvus is the open-source vector database behind many production RAG pipelines, semantic search engines, and recommendation systems. With the Meetrix pre-configured GCP image, you can deploy a production-ready, self-hosted Milvus instance in minutes, without managing the underlying installation and dependencies yourself.

This guide walks you through finding the product on GCP Marketplace, configuring the deployment, verifying it is running, and connecting to it with the pymilvus SDK.

Related Article

Milvus - Developer Guide (AWS)

Step-by-step guide for deploying and configuring the Milvus vector database on AWS using the Meetrix pre-configured AMI.

Meetrix.IO • Binuka Ranatunga

Launch the Product

Step 1: Find the Product

1. Log in to your Google account.
2. Go directly to the product page: Milvus Vector Database for AI, RAG & Semantic Search on GCP Marketplace
3. You can also browse all Meetrix products at the Meetrix Solutions Page.

Step 2: Launch the Product

1. Select your GCP project from the project selector at the top.
2. Click the Launch button.
3. Review the terms and agreements, tick the acknowledgement checkbox, and click AGREE.

Free Trial

This product includes a 5-day free trial with up to USD 50.00 in licence fee credits. To activate it, tick I accept the solution trial Terms and Conditions before proceeding.

Step 3: Configure the Deployment

You will see the deployment configuration form. Fill in the fields across the following sections:

General

• Deployment name - A unique name for this deployment (a default is pre-filled).
• Deployment Service Account - Select an existing service account that has the roles/config.agent, roles/compute.admin, and roles/iam.serviceAccountUser roles, or let GCP create a new one for you.
• Zone - Select the GCP zone closest to your users (for example us-central1-a).

Machine Type

• Series - Default E2 under the General purpose tab.
• Machine type - Default e2-standard-2 (2 vCPUs, 8 GB memory). Choose a larger type for heavier workloads.

Boot Disk

• Boot disk type - Default Balanced Persistent Disk is recommended.
• Boot disk size - Default 20 GB is sufficient for most setups.

GCP shows an estimated monthly cost, made up of the Meetrix licence fee and the underlying infrastructure fee, based on your selected machine type and disk size before you deploy.

Step 4: Configure Networking

• Network and Subnetwork - Leave as default unless you have a custom VPC.
• External IP - Leave as Ephemeral. Select None only if you do not need public internet access.
• Allow SSH (TCP port 22) from the Internet - Enabled by default. Restrict the source IP range if you want to limit SSH access to specific IPs.

Click DEPLOY and wait a few minutes for the deployment to complete.

Access the Deployment

Step 1: Get the External IP

1. Once deployment is complete, open the VM instance from the deployment details.
2. Copy the External IP from the Network interfaces section.

Step 2: Verify Milvus Is Running

Visit http://<External IP> in your browser. You should see a confirmation page.

Connect to Milvus

Milvus listens on port 19530 and ships with a default admin user, so you can connect immediately using the pymilvus SDK. No web UI is bundled with this deployment - if you want one, run Attu yourself in a separate container pointed at this host's port 19530.

pip install pymilvus

from pymilvus import connections, utility

connections.connect(
    host="<External IP address>",
    port="19530",
    user="root",
    password="Milvus"
)

print("Connected!")
print(utility.list_collections())

Running the script confirms the connection and lists any existing collections (empty on a fresh instance):

Manage the Deployment

Stop the VM

To stop the VM without deleting it, go to Compute Engine → VM Instances in the GCP console, select your instance, and click Stop. You can restart it later with your data intact.

Delete the Deployment

To fully remove the deployment and stop all billing:

1. Go to Solution deployments in the GCP console.
2. Find your Milvus deployment.
3. Click Delete to remove all associated resources.

Back up any collections or data you need before deleting the deployment.

Upgrades

When a new image version is available in the GCP Marketplace, back up your Milvus data, delete the previous deployment, and relaunch with the new version.

Troubleshoot

Check Server Logs

Open an SSH session to the instance from the GCP console, then check the running containers and their logs:

sudo docker ps

sudo docker logs <container-id-prefix>

Quota or Capacity Errors

GCP enforces regional CPU quotas. If you hit a quota error when deploying, request a Compute Engine API CPU quota increase for that region, or choose a different region or zone with available capacity.

Connection Issues

If pymilvus cannot connect, confirm a firewall rule in VPC Network → Firewall allows inbound TCP traffic on port 19530 from your IP address.

Disk Space

If Milvus becomes unresponsive, check whether the boot disk is full:

df -h

If the root volume is between 90-100% full, resize the persistent disk in the GCP console, then reboot the instance and restart the service.

Conclusion

The Meetrix Milvus Deployment Guide helps you get a self-hosted vector database running on your own GCP project in minutes. Whether you're building a RAG pipeline, a semantic search feature, or a recommendation engine, this guide gives you a secure, scalable starting point.

Technical Support

If you run into any issues, our support team is here to help. Reach out to us at support@meetrix.io and we will respond within 12 hours.