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AWS has reached its twentieth anniversary! With a gentle tempo of innovation, AWS has grown to supply over 240 complete cloud providers and continues to launch hundreds of recent options yearly for hundreds of thousands of shoppers. Throughout this time, over 4,700 posts have been printed on this weblog—greater than double the quantity since Jeff Barr wrote the tenth anniversary submit.
AWS modified my life
Reflecting on what I used to be doing 20 years in the past, I met Jeff in Seoul on March 13, 2006, when he got here because the keynote speaker for the Korea NGWeb convention. At the moment, Amazon was one of many first pioneers to provoke an API financial system, introducing ecommerce API providers. After the keynote speech, he returned house that night, and I imagine he wrote the Amazon S3 launch weblog submit on the flight again to the US.
That quick assembly with him introduced vital modifications to my life. He turned my position mannequin as a blogger, and I started constructing API-based providers in my firm and opening them to third-party builders. Once I was a PhD scholar whereas taking a break from work, I spotted that for particular person researchers like me, AWS Cloud providers are highly effective instruments for conducting large-scale analysis tasks. After returning to work, my firm turned one of many first AWS clients in Korea in 2014. Numerous builders—myself included—have embraced cloud computing and actively used its capabilities to perform what was beforehand unattainable.
Over the previous decade, the know-how panorama has remodeled dramatically. Deep studying emerged as a breakthrough in AI, evolving by way of generative AI primarily based on giant language fashions (LLMs) to right now’s agentic AI know-how. Jeff wrote, “When trying into the longer term, you want to have the ability to distinguish between flashy distractions and real tendencies, whereas remaining versatile sufficient to pivot if yesterday’s area of interest turns into right now’s mainstream know-how.” This precept guides how AWS approaches innovation—we begin by listening to what clients really want. The true pattern isn’t pursuing each rising know-how, however relatively reimagining options that handle clients’ most important challenges.
20 years of AWS
For the primary 10 years, Jeff chosen his favourite AWS launches and weblog posts. Amazon S3, Amazon EC2 (2006), Amazon Relational Database Service, Amazon Digital Non-public Cloud (2009), Amazon DynamoDB, Amazon Redshift (2012), Amazon WorkSpaces, Amazon Kinesis (2013), AWS Lambda (2014), and AWS IoT (2015).
Whereas I additionally hate to play favorites, I need to select a few of my favourite AWS weblog posts of the previous decade.
- Deploying containers simply (2014) – Amazon Elastic Container Service makes it easy so that you can run any variety of containers throughout a managed cluster of Amazon EC2 situations utilizing highly effective APIs and different instruments. In 2017, we launched Amazon Elastic Kubernetes Service as a totally managed Kubernetes service and AWS Fargate as a serverless deployment choice.
- Excessive availability database at international scale (2017) – Amazon Aurora is a contemporary relational database service providing efficiency and excessive availability at scale. In 2018, we launched Amazon Aurora Serverless v1, and this serverless database advanced to Amazon Aurora Serverless v2 to scale all the way down to zero. In 2025, we additionally launched Amazon Aurora DSQL is the quickest serverless distributed SQL database for at all times accessible purposes.
- Machine studying (ML) at your fingertips (2017) – Amazon SageMaker is a totally managed end-to-end ML service that knowledge scientists, builders, and ML specialists can use to shortly construct, practice, and host machine studying fashions at scale. In 2024, we launched the following era of Amazon SageMaker, a unified platform for knowledge, analytics, and AI and launched Amazon SageMaker AI to focus particularly on constructing, coaching, and deploying AI and ML fashions at scale.
- Finest value efficiency for cloud workloads (2018) – We launched Amazon EC2 A1 situations powered by the primary era of Arm-based AWS Graviton Processors designed to ship one of the best value efficiency in your cloud workloads. Final yr, we previewed EC2 M9g situations powered by AWS Graviton5 processors. Over 90,000 AWS clients have reaped the advantages of Graviton supporting widespread AWS providers similar to Amazon ECS and Amazon EKS, AWS Lambda, Amazon RDS, Amazon ElastiCache, Amazon EMR, and Amazon OpenSearch Service.
- Run AWS Cloud in your knowledge middle (2019) – AWS Outposts is a household of totally managed providers delivering AWS infrastructure and providers to nearly any on-premises or edge location for a really constant hybrid expertise. Now, AWS Outposts is accessible in a number of kind elements, from 1U and 2U Outposts servers to 42U Outposts racks, and a number of rack deployments. Prospects similar to DISH, Fanduel, Morningstar, Philips, and others use Outposts in workloads requiring low latency entry to on-premises techniques, native knowledge processing, knowledge residency, and utility migration with native system interdependencies.
- Finest value efficiency for ML workloads (2019) – We launched Amazon EC2 Inf1 situations powered by the primary era of AWS Inferentia chips designed to offer quick, low-latency inferencing. In 2022, we launched Amazon EC2 Trn1 situations powered by the primary era of AWS Trainium chips optimized for top efficiency AI coaching. Final yr, we launched Amazon EC2 Trn3 UltraServers powered by Trainium3 to ship one of the best token economics for next-generation generative AI purposes. Prospects similar to Anthropic, Decart, poolside, Databricks, Ricoh, Karakuri, SplashMusic, and others are realizing efficiency and price advantages of Trainium-based situations and UltraServers.
- Construct your generative AI apps on AWS (2023) – Amazon Bedrock is a totally managed service that gives a selection of business main AI fashions together with a broad set of capabilities that you could construct generative AI purposes, simplifying improvement with safety, privateness, and accountable AI. Final yr, we launched Amazon Bedrock AgentCore, an agentic platform for constructing, deploying, and working efficient brokers securely at scale. Now, greater than 100,000 clients worldwide select Amazon Bedrock to ship customized experiences, automate complicated workflows, and uncover actionable insights.
- Your AI coding companion (2023) – We launched Amazon CodeWhisperer because the business’s first cloud-based AI coding assistant service. The service delivered code era from feedback, open-source code reference monitoring, and vulnerability scanning capabilities. In 2024, we rebranded the service to Amazon Q Developer and expanded its options to incorporate a chat-based assistant within the console, project-based code era, and code transformation instruments. In 2025, this service advanced into Kiro, a brand new agentic AI improvement instrument that brings construction to AI coding by way of spec-driven improvement, taking tasks from prototype to manufacturing. Not too long ago, Kiro previewed an autonomous agent, a frontier agent that works independently on improvement duties, sustaining context and studying from each interplay.
- Broaden your AI mannequin selections (2024) – We launched Amazon Titan fashions additional growing cost-effective AI mannequin selection for textual content and multimodal wants in Amazon Bedrock. At AWS re:Invent 2024, we introduced Amazon Nova fashions that delivers frontier intelligence and business main value efficiency. Now Amazon Nova has a portfolio of AI choices—together with Amazon Nova fashions, Amazon Nova Forge, a brand new service to construct your individual frontier fashions; and Amazon Nova Act, a brand new service to construct brokers that automate browser-based UI workflows powered by a customized Amazon Nova 2 Lite mannequin.
Construct with AI: Your path ahead
A decade in the past, AWS responded to the emergence of deep studying by launching the broadest and deepest ML providers, similar to Amazon SageMaker, democratizing AI for a variety of shoppers—from particular person builders and startups to giant enterprises—no matter their technical experience.
AI know-how has superior considerably, however constructing and deploying AI fashions and purposes nonetheless stays complicated for a lot of builders and organizations. AWS gives the broadest collection of AI fashions by way of Amazon Bedrock, together with main suppliers similar to Anthropic and OpenAI. By utilizing our mannequin coaching and inference infrastructure and accountable AI each sensible and scalable, you may speed up trusted AI innovation whereas sustaining management of your knowledge and prices—all constructed on our international infrastructure’s operational excellence.
Reinvent your thought, carry on studying, construct confidently with AI you may belief, and share your successes with us! New AWS clients obtain as much as $200 in credit to attempt AWS AI without spending a dime. For those who’re a scholar, begin constructing with Kiro without spending a dime utilizing 1,000 credit per 30 days for one yr.
— Channy