Imagine a roundtable of technology leaders, each representing their organization’s cloud strategy discussing how to pick the right vector database as AI-powered apps and Retrieval Augmented Generation (RAG) become mission-critical. The transformation in handling high-dimensional, unstructured data is already underway.
But the real question for architects and developers is:
Which cloud platform’s vector store fits best for next-generation semantic search, chatbots or agent memory?
Let’s dissect to see the unique features of Amazon S3 Vectors, Amazon OpenSearch, Google Cloud Vertex AI Vector Search, and Azure AI Search and the scenario that best suits for each of these cloud providers.
Comparing the Core Feature Sets
| Feature | Amazon S3 Vectors | Amazon OpenSearch | Google Vertex AI Vector | Azure AI Search |
|---|---|---|---|---|
| Native Vector Search | Yes | Yes | Yes | Yes |
| Serverless | Yes | Yes (serverless mode) | Yes | Partial (scalable) |
| Scale | Billions of vectors | Billions of vectors | Billions of vectors | Hundreds of millions |
| Latency | Sub-second | Milliseconds | Single-digit milliseconds | Milliseconds |
| Multi-modal Search | Yes | Yes | Yes | Yes |
| Hybrid Search | No (object+vector only) | Yes (text+vector) | Partial | Yes (text+vector+filter) |
| Metadata Filtering | Yes | Yes | Yes | Yes |
| Max Dimension Supported | High (billions) | 16,000 | 30,000 (ScaNN) | ~15,000+ |
| Pricing Model | S3-based (usage) | Cluster/serverless | Usage-based | Usage-based |
| RAG / GenAI Integration | Extensive (Bedrock, SageMaker, OpenSearch) | Supported | Deep (Vertex AI / GenAI) | Deep (OpenAI SDK) |
| Object Storage Tie-in | Yes | No | No | No |
| Open Source | No | Yes | No | No |
Deep Dives: What Sets Each Provider Apart
Amazon S3 Vectors & OpenSearch
- S3 Vectors bring native vector buckets directly into your familiar S3 workflow — store, index, and query embeddings at cloud scale, joining files, metadata, and vectors seamlessly.
- OpenSearch extends this with unified search and analytics combining full-text, vector, and numerical data across serverless or cluster-based deployments. If your workloads span both classical document search and new retrieval workflows, OpenSearch offers mature hybrid capabilities and is open-source for flexibility.
Google Cloud Vertex AI Vector Search
- Vertex AI Vector Search specializes in embedding-aware storage and retrieval, with ScaNN delivering high-performance similarity search up to 30,000 dimensions. With deep integration to GenAI and Vertex AI platforms, it enables fast, scalable agent memory, semantic search, and RAG without stepping outside the Google Cloud ML toolkit.
Azure AI Search
- Azure AI Search (Vector Search/Vector Store) adds vector awareness and hybrid search to the Azure ecosystem, letting teams orchestrate search over text, metadata, and vectors in tandem. Managed scaling and OpenAI model support make it a solid choice for Microsoft-centric stacks.
Which Suits What Scenarios?
| Use Case | S3 Vectors / OpenSearch | Vertex AI Vector | Azure AI Search |
|---|---|---|---|
| Agent / LLM memory at scale | ✓ | ✓ | ✓ |
| Cost-optimized vector archive | ✓ (S3 cost) | ✓ | Partial |
| Real-time semantic search | ✓ | ✓ | ✓ |
| Hybrid text + vector search | ✓ (OpenSearch) | Partial | ✓ |
| Multi-modal / embedding search | ✓ | ✓ | ✓ |
| RAG / GenAI orchestration | ✓ | ✓ | ✓ |
| Open-source / portable setup | OpenSearch only | No | No |
Bottom Line for Architects & Developers
The best cloud-native vector solution is always shaped by your existing platform, data workflows, and long-term architecture vision.
AUTHOR
Ramesh Karthikeyan
Ramesh Karthikeyan is a results-driven Solution Architect skilled in designing and delivering enterprise applications using Microsoft and cloud technologies. He excels in translating business needs into scalable technical solutions with strong leadership and client collaboration.
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