AWS Bedrock Adds Claude Sonnet 4.5 With Hybrid Search

Amazon Web Services has officially added Claude Sonnet 4.5 from Anthropic to its AWS Bedrock platform, pairing the latest foundation model with new hybrid search capabilities designed for enterprise retrieval-augmented generation (RAG) workloads. The integration marks a significant upgrade for organizations building AI-powered applications on AWS, combining Anthropic's most capable mid-tier model with enhanced knowledge retrieval that blends semantic and keyword-based search.

The announcement positions AWS Bedrock as the most comprehensive managed AI platform for enterprises seeking to deploy production-grade AI systems without managing complex infrastructure.

Key Takeaways at a Glance

• Claude Sonnet 4.5 is now generally available on AWS Bedrock, offering improved reasoning and extended thinking capabilities
• Hybrid search combines vector-based semantic search with traditional keyword matching in Bedrock Knowledge Bases
• Enterprise customers gain access to improved RAG pipelines with up to 35% better retrieval accuracy, according to AWS benchmarks
• The integration supports context windows of up to 200,000 tokens for complex document analysis
• Pricing follows Bedrock's on-demand model at approximately $3 per million input tokens and $15 per million output tokens
• AWS Bedrock now supports over 40 foundation models from leading AI providers

Claude Sonnet 4.5 Brings Enhanced Reasoning to Bedrock

Claude Sonnet 4.5 represents a meaningful step forward from its predecessor, Claude Sonnet 3.5, which became one of the most popular models on the Bedrock platform since its launch. The new model delivers stronger performance on coding, mathematical reasoning, and instruction-following benchmarks while maintaining the speed and cost efficiency that made the Sonnet tier attractive to enterprise developers.

Unlike Claude's larger Opus models, Sonnet 4.5 strikes a balance between capability and latency. Early benchmarks suggest it achieves performance comparable to many flagship models while running at roughly 2x the speed and half the cost of premium-tier alternatives.

The model introduces extended thinking support on Bedrock, allowing developers to enable a 'chain-of-thought' mode where the model reasons through complex problems step by step before delivering a final answer. This feature proves particularly valuable for financial analysis, legal document review, and scientific research applications where accuracy outweighs response speed.

Hybrid Search Transforms Enterprise RAG Pipelines

The most architecturally significant addition is the new hybrid search capability within Bedrock Knowledge Bases. Previously, enterprises using Bedrock's RAG features relied primarily on vector-based semantic search to retrieve relevant documents before passing them to a foundation model for generation.

Semantic search excels at understanding intent and meaning but can struggle with exact matches — product SKUs, legal citation numbers, or specific technical terms. Traditional keyword search handles these precisely but misses conceptual relationships. Hybrid search merges both approaches.

Here is what the hybrid search integration delivers:

• Semantic vector search powered by Amazon Titan Embeddings or third-party embedding models for intent-based retrieval
• BM25 keyword matching for precise term-level lookups across enterprise document stores
• Reciprocal rank fusion that intelligently combines results from both search methods into a unified ranking
• Configurable weighting allowing developers to adjust the balance between semantic and keyword search based on use case
• Metadata filtering to scope searches by document type, date range, department, or custom attributes

AWS reports that hybrid search improves retrieval accuracy by up to 35% compared to pure vector search in benchmarks using enterprise document corpora. For regulated industries like healthcare and finance, this accuracy improvement can mean the difference between a compliant AI system and one that generates unreliable outputs.

How This Compares to Competing Platforms

Microsoft Azure and Google Cloud have both expanded their managed AI offerings in recent months, but the Bedrock hybrid search integration gives AWS a distinct advantage in the RAG pipeline space. Azure AI Search offers similar hybrid capabilities but requires more manual configuration and separate service orchestration. Google's Vertex AI provides grounding features but has been slower to integrate third-party models at the same breadth as Bedrock.

The competitive landscape for managed AI platforms is intensifying rapidly. AWS Bedrock now hosts models from Anthropic, Meta, Mistral, Cohere, Stability AI, Amazon itself, and several other providers. This model diversity, combined with native search infrastructure, creates a compelling lock-in effect for enterprises already invested in the AWS ecosystem.

Notably, OpenAI's models remain exclusive to Azure, meaning enterprises that want access to both GPT-4o and Claude Sonnet 4.5 must work across multiple cloud providers or choose sides. AWS is betting that Anthropic's safety-focused approach and competitive model performance will win over enterprise buyers who prioritize reliability and responsible AI deployment.

What This Means for Developers and Enterprises

For development teams building AI applications, the combined release simplifies what has traditionally been a complex multi-service architecture. Instead of stitching together separate vector databases, search engines, embedding pipelines, and language models, Bedrock now offers an integrated stack.

Practical implications include:

• Faster prototyping: Developers can build RAG applications with hybrid search in hours rather than weeks
• Lower operational overhead: No need to manage separate Elasticsearch or OpenSearch clusters for keyword matching
• Improved accuracy: Combined search methods reduce hallucination rates in generated responses
• Cost predictability: On-demand pricing eliminates the need to provision dedicated search infrastructure
• Compliance readiness: Built-in guardrails and logging support audit requirements for regulated industries

Enterprise architects should pay particular attention to the guardrails integration, which allows organizations to apply content filters, PII detection, and topic restrictions directly within the Bedrock pipeline. When paired with Claude Sonnet 4.5's improved instruction following, these guardrails create a more controllable AI deployment environment.

Industry Context: The RAG Infrastructure Race Heats Up

The addition of hybrid search to Bedrock reflects a broader industry trend: the shift from 'model-centric' AI strategies to 'system-centric' approaches. As foundation models commoditize — with performance gaps between top models narrowing — the competitive differentiation increasingly comes from the surrounding infrastructure.

Retrieval-augmented generation has emerged as the dominant architecture for enterprise AI because it solves the fundamental knowledge problem. Foundation models have training data cutoffs and lack access to proprietary business data. RAG bridges this gap by retrieving relevant information at query time and injecting it into the model's context.

Market research from Gartner suggests that by 2026, more than 60% of enterprise AI deployments will use some form of RAG architecture. The firms that control the RAG infrastructure stack — search, embeddings, orchestration, and generation — stand to capture significant recurring revenue.

AWS's strategy with Bedrock is clearly aimed at owning this entire stack within its cloud ecosystem. By making hybrid search a native feature rather than a bolt-on service, Amazon reduces friction for adoption and deepens customer commitment to the platform.

Looking Ahead: What Comes Next for Bedrock

AWS has signaled that additional capabilities are on the roadmap for Bedrock throughout the remainder of 2025. Expected enhancements include agentic workflows with multi-step reasoning, improved multi-modal RAG supporting image and video retrieval alongside text, and deeper integration with Amazon Q for business intelligence use cases.

The pace of iteration on managed AI platforms suggests that by the end of the year, the gap between custom-built AI systems and managed platform deployments will narrow substantially. For most enterprises, the 'build versus buy' calculus is shifting decisively toward managed services like Bedrock, Azure AI, and Vertex AI.

Developers interested in testing the new capabilities can access Claude Sonnet 4.5 and hybrid search through the Bedrock console or API immediately. AWS offers a free tier for initial experimentation, though production workloads will incur standard on-demand charges. Organizations already using Bedrock Knowledge Bases can enable hybrid search with a configuration update — no data migration required.

The message from AWS is clear: enterprise AI is no longer just about choosing the right model. It is about building the right system around it — and Bedrock wants to be that system.