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This product has charges associated with it for seller support. Weaviate, the open source vector database for AI semantic, keyword and hybrid search, preinstalled behind an nginx reverse proxy on port 80 and gated by a unique API key generated on first boot. Backed by 24/7 cloudimg support.
Overview
This is a repackaged open source software product wherein additional charges apply for cloudimg support services.
Overview Weaviate is the popular open source vector database built for AI applications. It stores data objects together with their vector embeddings and runs fast semantic, keyword and hybrid search over them through GraphQL and REST APIs, making it a strong backend for retrieval augmented generation, recommendation and similarity search. This image delivers Weaviate fully installed and configured as a system service, so a production ready vector database is running within minutes of launch. The current release available is Weaviate 1.38.
Application Stack Weaviate is installed under /opt/weaviate from the official release binary and run by an unprivileged service account. It listens on the loopback address and an nginx reverse proxy fronts it on port 80, exposing the unauthenticated readiness probe and the data APIs. A systemd service starts the database on boot and restarts it on failure.
Secure By Default Anonymous access is disabled and API-key authentication is enabled. This image generates a fresh API key, unique to your instance, on its first boot and writes it to a root only file. No shared or default key ships in the image. Callers authenticate with the standard Authorization Bearer header.
Ready To Use Connect with the Weaviate client, create a collection, and add objects with your own vectors or via an external vectorizer you configure. Your data lives on a dedicated, independently resizable storage volume kept separate from the operating system disk. The image ships no embedding model and is CPU only.
cloudimg Support 24/7 technical support by email and chat. Help with Weaviate deployment, schema and collection design, vectorizer and module configuration, backups, TLS termination and scaling.
Use Cases Vector search backend for retrieval augmented generation. Semantic, keyword and hybrid search over your own data. Recommendation and similarity search. A self hosted, in your own VPC vector database for teams with data residency or compliance requirements.
All product and company names are trademarks or registered trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.
Highlights
- Weaviate, the open source vector database for AI semantic, keyword and hybrid search, preinstalled as a systemd service behind an nginx reverse proxy on port 80, ready to use with no manual setup
- Secure by default: anonymous access is disabled and API-key authentication is enabled, with a unique key generated for every instance on first boot and stored in a root only file
- 24/7 technical support from cloudimg, with expert help for schema and collection design, vectorizer and module configuration, backups, TLS and scaling
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
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Pricing
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Try this product free for 7 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
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If you are an AWS Free Tier customer with a free plan, you are eligible to subscribe to this offer. You can use free credits to cover the cost of eligible AWS infrastructure. See AWS Free Tier for more details. If you created an AWS account before July 15th, 2025, and qualify for the Legacy AWS Free Tier, Amazon EC2 charges for Micro instances are free for up to 750 hours per month. See Legacy AWS Free Tier for more details.
Dimension | Description | Cost/hour |
|---|---|---|
m5.large Recommended | m5.large | $0.08 |
t3.micro | t3.micro instance type | $0.04 |
t2.micro | t2.micro instance type | $0.04 |
m8i.8xlarge | m8i.8xlarge instance type | $0.24 |
m5dn.metal | m5dn.metal instance type | $0.24 |
i4i.large | i4i.large instance type | $0.08 |
i4i.8xlarge | i4i.8xlarge instance type | $0.24 |
r5ad.xlarge | r5ad.xlarge instance type | $0.12 |
m5ad.16xlarge | m5ad.16xlarge instance type | $0.24 |
r5n.16xlarge | r5n.16xlarge instance type | $0.24 |
Vendor refund policy
Refunds available on request.
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Initial release of the Weaviate 1.38 open source vector database.
Additional details
Usage instructions
Connect via SSH on port 22 as the default login user for your operating system variant (the user guide lists it per variant; on Ubuntu it is 'ubuntu'). Weaviate is served by nginx on port 80. Retrieve the generated API key with: sudo cat /root/weaviate-credentials.txt. The readiness probe is open at http://<instance-public-ip>/v1/.well-known/ready; the data APIs require the API key as 'Authorization: Bearer <api-key>'. Point the Weaviate client at http://<instance-public-ip>/ with the API key. The server runs on loopback port 8080 (REST + GraphQL) with gRPC on 50051, and is managed with systemctl (weaviate.service, nginx.service). Data is stored under /var/lib/weaviate. The user guide covers creating collections, adding objects and vectors, configuring a vectorizer, backups and enabling HTTPS.
Resources
Vendor resources
Support
Vendor support
cloudimg provides 24/7 technical support for this product by email and live chat. Our engineers help with deployment, configuration, updates, performance tuning and troubleshooting; critical issues receive a one hour average response. Contact support@cloudimg.co.uk .
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
Enterprise Vector Database Platform and Assurance Support Services sold as a Weaviate UNITS SKU that customer can flexibly use
The Weaviate SaaS Platform offers hassle-free deployment, hosting the vector database cluster within your AWS tenant and VPC. This end-to-end deployment includes the Weaviate Enterprise Terms (support) and Enterprise Service License Agreement, ensuring a comprehensive and supported SaaS experience for your organization.
Code Creator Weaviate Vector AI Server deploys Weaviate Community Edition on Ubuntu as a ready-to-launch self-hosted vector database for semantic search, RAG, embeddings storage, and AI application backends. It exposes REST on port 8080 and gRPC on port 50051, and supports secure API based access through official client libraries and standard Weaviate interfaces. Note: this AMI provides the server backend only, so customers should plan to connect using their own API calls, client libraries, or external applications. This product has charges associated with it for the provision and deployment of the application and AMI support.
This product has charges associated with it for seller support. Weaviate is an open-source vector database that enables developers to store, search, and manage data using AI powered vector embeddings for semantic search, recommendation, and generative AI applications.
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