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"We aren’t trading speed for scale; we are demanding both," says the military's program manager.
Amazon receives millions of visits every day, and earning each customer’s trust visit after visit is the foundation that the store is built on. A meaningful part of that trust comes down to whether the recommendations we surface feel relevant and whether they reflect what the customer actually cares about in the moment. In this post, we describe an architecture that makes it achievable. Amazon SageMaker hosts a sentence transformer model on a managed endpoint and turns customer query text into d
By implementing modern optimization techniques for Aurora, you can achieve additional cost reduction beyond traditional methods alone. This isn’t only about spending less—it’s about building a more efficient, scalable, and resilient database environment. In this post, we show you a structured approach to optimizing Amazon Aurora database costs. It outlines specific strategies, implementation steps, and best practices across different optimization areas.
As agent adoption scaled, we saw a common pattern emerge across enterprises, including our own sales organization: specialized agents deliver value, but without orchestration, users carry the cognitive load of choosing between them. At AWS Sales, this meant more than 20 domain-specific agents deployed across the global organization, with representatives context-switching between systems instead of […]
If you've deployed a retrieval-augmented generation (RAG) pipeline over enterprise data, you've probably ended up in the same place: Pinecone or Weaviate for embeddings, Delta Lake or Iceberg for structured data, and some custom middleware stitching them together that nobody fully owns. This split made sense historically. Vector databases existed before lakehouse formats efficiently supported high-dimensional arrays. Standing up a Pinecone index was faster than waiting for your data platform to
The Chinese company is adapting to the demise of Moore’s Law, which guides chip production. It could complicate US chip dominance.
Director describes how his views on existence of aliens have changed, interspersed with footage from film.
At Deploy 2026, we introduced the DigitalOcean AI-Native Cloud, built for the inference era. Batch Inference on the DigitalOcean Inference Engine enables high-volume asynchronous workloads. As developers move from AI prototypes to production-scale applications, the challenges of cost and rate limits often become a bottleneck. Batch Inference addresses these hurdles by allowing you to process high-volume workloads asynchronously at a fraction of the cost of synchronous requests. Whether you are p
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