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This post is co-written with Vianney Bruned, Filippo Giruzzi, Belkiss Saidi, and Carlos Ramirez from Decathlon. Decathlon is one of the world’s largest sporting goods retailers, with more than 100,000 teammates and 400 million users worldwide. The company relies on accurate demand forecasting at scale to support the availability of the appropriate products in each […]

When Salesforce set out to make Agentforce (Salesforce’s AI foundation for agents) highly available (HA) across multiple Availability Zones (AZs), the team faced a gap. Amazon SageMaker AI Inference Components (ICs) could cut GPU costs, but their default placement didn’t guarantee the Multi-AZ resilience Salesforce’s compliance bar required. For Salesforce, the ICs delivered an 8x

Creative teams face growing demand for more assets, formats, and revisions, while their scripts, references, models, and outputs often remain fragmented across tools. Creators must repeatedly transfer context and assemble results manually. With 78% of creative leaders saying demand exceeds their teams’ capacity, faster generation alone does not solve the underlying workflow problem. To address

Amazon Bedrock now supports the OpenAI GPT-5.6 models, Terra and Luna, in India, with India geographic cross-Region inference. If you have local data processing requirements in India, including in financial services, healthcare, and the public sector, you can now use these OpenAI models at scale. Amazon Bedrock processes inference requests and data within India. Both

Self-hosted speech AI has historically carried an observability trade-off. The service can tell you an endpoint is up and how many requests it served. The questions that actually drive capacity planning and cost management stay locked inside the vendor’s container: what you are billed for, which features your traffic uses, and what the inference engine

This post is a collaboration between AWS, NVIDIA and Heidi. Reducing automatic speech recognition (ASR) inference costs on Amazon Elastic Compute Cloud (Amazon EC2) becomes critical when GPU utilization per request is low but latency requirements are strict. A single ASR inference request typically uses only 15–20 percent of a GPU’s compute capacity, yet the default

Book hotels and track airfares, plus view miles and rewards with AI Mode in Google Search. ​ 

AI teams building production agents face a frustrating asymmetry: the diversity of agent frameworks keeps growing, but evaluation tooling has not kept pace. Most evaluation systems assume you built your agent in a specific way: a specific SDK, a specific large language model (LLM) client, a specific tracing pattern. The moment you step outside that

GoDaddy is one of the world’s largest domain registrar and web hosting companies, serving more than 20 million customers and managing approximately 82 million domain names. At that scale, access to timely business data directly affects how quickly the company can act. When GoDaddy’s analytics infrastructure faced challenges under the weight of thousands of dashboards,

Booking a phlebotomy appointment shouldn’t be a hassle for oncology patients already managing treatment. Natera’s service, powered by Amazon Bedrock AgentCore, allows a phlebotomist to come to the patient, helping Natera deliver a more convenient experience. Natera, a global diagnostics company specializing in cell-free DNA testing, wanted to transform their patient experience by replacing manual