Is AWS S3 vector search is new feature? Launch a week ago?

``` The deep research logic is organized as follows: Okay, let's tackle this question. The user is asking if AWS S3 vector search is a new feature and if it was launched a week ago. First, I need to check the references provided to confirm the launch date and details about the feature. Looking through the references, I see multiple entries from July 15, 2025, announcing the preview of Amazon S3 Vectors. For example, reference 1, 6, and 8 all mention the launch date as July 15, 2025. The user's current time is July 17, 2025, so the feature was indeed launched about a week ago, specifically two days prior. Next, I need to explain what S3 Vectors is. From the references, it's a purpose-built vector storage solution integrated into S3, optimized for AI applications like RAG. It offers cost reductions, scalability, and integrates with other AWS services like Bedrock and OpenSearch. The key features include native vector support, cost optimization, and ease of integration without needing separate infrastructure. I should also address why this matters. Since the user is interested in AI and AIGC, highlighting how S3 Vectors can lower costs and improve efficiency for AI workflows would be relevant. The integration with Bedrock and OpenSearch is a big plus for building scalable AI applications. I need to structure the answer with a clear conclusion first, then detailed analysis. Use tables to present key points, and include the necessary citations. Also, make sure to mention the launch date clearly and confirm that it's a new feature launched recently. I should avoid any unsupported claims and stick strictly to the provided references. Also, ensure the language is professional but engaging, using emojis where appropriate to make it more lively. Finally, check the visualization requirement: since the query is about the launch, I need to include the corresponding visualization tag right after the relevant section. ``` **Conclusion** 🚀 Yes, **AWS S3 Vector Search** is a **brand-new feature** launched on **July 15, 2025** . It introduces **native vector storage and search capabilities** directly within Amazon S3, marking a significant advancement in AI infrastructure. Let’s dive into the details and implications for your AI-focused investments. --- ### 📊 **Key Details of AWS S3 Vectors** | Feature | Description | |-----------------------------|-----------------------------------------------------------------------------| | **Launch Date** | July 15, 2025 (Preview) | | **Core Purpose** | Cost-optimized vector storage for AI agents, semantic search, and RAG | | **Cost Savings** | Up to **90% reduction** in vector storage and query costs | | **Integration** | Native support with Amazon Bedrock, SageMaker, and OpenSearch | | **Scalability** | Handles **billions of vectors** with sub-second query performance | | **Use Cases** | Semantic search, AI inference, medical imaging, and large-scale document analysis | Query |code|stock code|stock name|Last Price|Last Change|Current Rating (Original)|Rating Date|market_code| |---|---|---|---|---|---|---|---| |SRPT|SRPT.O|Sarepta|21.97|19.5321|Overweight|20250710|185| |SRPT|SRPT.O|Sarepta|21.97|19.5321|Overweight|20250617|185| |SRPT|SRPT.O|Sarepta|21.97|19.5321|Overweight|20250617|185| |SRPT|SRPT.O|Sarepta|21.97|19.5321|Overweight|20250520|185| |SRPT|SRPT.O|Sarepta|21.97|19.5321|Overweight|20250508|185| |SRPT|SRPT.O|Sarepta|21.97|19.5321|Overweight|20250507|185| |SRPT|SRPT.O|Sarepta|21.97|19.5321|Overweight|20250507|185| |SRPT|SRPT.O|Sarepta|21.97|19.5321|Overweight|20250507|185| |SRPT|SRPT.O|Sarepta|21.97|19.5321|Overweight|20250421|185| |RCKT|RCKT.O|Rocket|3.34|16.783217|Overweight|20250516|185| --- ### 🧠 **Why This Matters for AI Investors** 1. **Cost Efficiency**: S3 Vectors slashes costs for vector storage, making it feasible for businesses to scale AI applications like RAG (Retrieval-Augmented Generation) . 2. **Scalability**: Built for **massive datasets** (e.g., petabyte-scale video archives or medical imaging) . 3. **Integration**: Seamless compatibility with AWS’s ecosystem (Bedrock, SageMaker, OpenSearch), reducing the need for third-party vector databases like Pinecone or Weaviate . 4. **Market Impact**: This innovation could disrupt the AI infrastructure landscape, benefiting companies like **BIDU** (focused on AI search) and **CRM** (customer data analytics) [^user_profile]. --- ### 📊 **Comparison with Competitors** | Feature | AWS S3 Vectors | Pinecone | Weaviate | |-----------------------------|-----------------------|----------------|----------------| | **Native S3 Integration** | ✅ (Direct) | ❌ (ETL Needed) | ❌ (ETL Needed) | | **Cost Model** | S3-centric (GB/query) | Cluster-based | Query-based | | **Max Scale** | Billions of vectors | Millions | Millions | | **Serverless** | ✅ | ❌ | ❌ | --- ### 🚨 **Key Takeaway** AWS S3 Vectors is a **game-changer** for AI workflows, offering **cost-effective, scalable, and integrated vector storage**. For investors in AI infrastructure (e.g., **BIDU**, **CRM**, or even **MRVL** for AI chips), this innovation could drive **operational efficiency** and **competitive advantage**. Stay tuned for further updates as this technology matures! 🌟