Two Free Paths to a Cloud Vector Database

	Managed Free Tier	Self-Hosted on a Cloud VM
Setup time	~5 minutes	15–30 minutes
Infrastructure to manage	None	VM, OS, Docker, networking
Cost	Free within plan limits	Cost of the VM (free tier VMs available on all major clouds)
Vector/storage limits	Plan-capped	Limited only by VM disk size
Data residency	Provider-controlled region	Your chosen region and account
Best for	Fast start, shared team access, no DevOps	Full control, compliance, larger datasets

Option 1 — Managed Free Tier

Several open-source vector databases offer a free managed cloud tier alongside their self-hosted option. These sandboxes are fully managed — no server to configure — and accessible from any client via a URL and API key.

The example below connects to a free managed sandbox from Python:

import weaviate
from weaviate.auth import AuthApiKey

client = weaviate.connect_to_weaviate_cloud(
    cluster_url="https://your-sandbox.weaviate.network",
    auth_credentials=AuthApiKey("your-api-key"),
)
print(client.is_ready())

Replace the URL and key with those from your free sandbox. No infrastructure setup required — the database is ready to query immediately after signing up.

Option 2 — Self-Host on a Free Cloud VM

All major cloud providers offer free-tier virtual machines. The self-hosted approach removes plan-based vector limits and keeps data in your own account and region.

Free VM Options by Provider

• AWS — EC2 t2.micro (1 vCPU, 1 GB RAM), free for 12 months
• Google Cloud — e2-micro (1 vCPU, 1 GB RAM), always-free in eligible regions
• Azure — B1s (1 vCPU, 1 GB RAM), free for 12 months
• Oracle Cloud — Ampere A1 (up to 4 OCPUs, 24 GB RAM), always-free; the most capable free VM available and the practical choice for a real vector database workload

For a usable vector database deployment, aim for at least 2 GB RAM. Oracle Cloud’s always-free Ampere A1 is the standout option — 24 GB RAM is enough to run a meaningful workload indefinitely at no cost.

Deploy a Free Vector Database on a Cloud VM

SSH into your VM, then install Docker and run the database:

# Install Docker
curl -fsSL https://get.docker.com | sh
sudo usermod -aG docker $USER && newgrp docker

# Create docker-compose.yml
cat > docker-compose.yml << 'EOF'
---
services:
  vectordb:
    image: cr.weaviate.io/semitechnologies/weaviate:1.38.0
    ports:
    - 8080:8080
    - 50051:50051
    volumes:
    - vectordb_data:/var/lib/weaviate
    restart: always
    environment:
      QUERY_DEFAULTS_LIMIT: 25
      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
      PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
      CLUSTER_HOSTNAME: 'node1'
volumes:
  vectordb_data:
EOF

# Start the database
docker compose up -d

# Confirm it is ready
curl http://localhost:8080/v1/meta

Open the Required Firewall Ports

In your cloud provider's security group or firewall rules, open the ports the database listens on:

• Port 8080 — HTTP REST and GraphQL API
• Port 50051 — gRPC (required by modern client libraries)

Restrict access to known IP ranges for any deployment handling real data.

Add HTTPS with a Reverse Proxy

For HTTPS access over a domain, Caddy is the simplest option — it provisions a Let's Encrypt certificate automatically:

vectordb.yourdomain.com {
    reverse_proxy localhost:8080
}

grpc-vectordb.yourdomain.com {
    reverse_proxy h2c://localhost:50051
}

Which Path Should You Choose?

Start with a managed free tier to skip infrastructure entirely and validate your use case first. Move to a self-hosted VM when you need more storage, full data control, or want to avoid hitting plan caps. Because both use the same API and client library, the switch is a one-line URL change — no application code changes required.

Frequently Asked Questions

Which cloud VM is best for a free vector database?

Oracle Cloud's always-free Ampere A1 instance is the most capable option — up to 4 OCPUs and 24 GB RAM, permanently free. It is sufficient for a real development or small production workload and has no 12-month expiry like AWS and Azure free tiers.

Is a managed free sandbox suitable for production?

It depends on the provider and the workload. Free sandboxes are generally well-suited for development, staging, and small-scale production. For workloads that need SLA guarantees, backups, or dedicated compute, a paid plan or self-managed cluster is more appropriate.

Can I migrate from a managed sandbox to self-hosted later?

Yes. Export your vectors and metadata from the sandbox, deploy a self-hosted instance on a cloud VM, and re-import. Application code does not need to change — only the cluster URL and credentials.