When Amazon Web Services Inc. launched Lambda 10 years ago, it was a bold experiment that would go on to reshape the cloud computing landscape.

Serverless computing, a term that scarcely existed before Lambda’s debut, has since grown into a multibillion-dollar market, enabling enterprises and startups to build applications at unprecedented speed and scale. As the 10-year anniversary approaches, Amazon.com Chief Technology Officer Werner Vogels sat down with me for an exclusive interview to reflect on Lambda’s legacy and how its principles may guide the future of cloud technology and artificial intelligence.

Lambda’s origin story is one of curiosity, ambition and, per Amazon founder Jeff Bezos’ mantra, a relentless focus on customers. “When we started Lambda,” Vogels recalls, “there was no blueprint. We were listening to customers like WeTransfer, who were struggling with the inefficiencies of provisioning and managing infrastructure for tasks that could be triggered by events, such as virus scans on uploaded files.”

A ‘working backwards’ approach with a feedback loop that defined serverless

AWS’ famous customer-centric “working backwards” methodology was foundational to Lambda’s development, starting with defining the problem and vision from the customer’s perspective. As Vogels explains, AWS’ internal six-page “PRFAQ” for Lambda became a roadmap that evolved directly from customer input. “Everyone was thrilled,” he says. “The document was so clear that it was almost like we could see the product fully formed, ready to go to market.”

The feedback loop was essential from day one. As customers started using Lambda, AWS gathered insights to shape its roadmap, guiding Lambda’s evolution and revealing new possibilities and customer needs. “The big question wasn’t ‘Will customers use this?’ We knew they would. It was ‘Can we afford to build it?’” Vogels reflects. With this loop, AWS quickly iterated, improving Lambda with features such as faster start times, memory adjustments and new billing models, all informed by real-world feedback.

Firecracker: a foundational innovation in efficiency

To make Lambda viable, AWS developed foundational technologies, including Firecracker — a lightweight micro virtual machine designed for fast, isolated serverless functions. Firecracker not only underpins Lambda today but also powers AWS Fargate, extending serverless capabilities into container-based environments. This innovation epitomized AWS’ dedication to customer feedback and efficiency.

Firecracker embodies AWS’s commitment to delivering cost-effective, efficient solutions that align with customer needs. “We knew what customers wanted — high security, fine-tuned economics and the flexibility to only pay for the resources they used,” Vogels explains. Firecracker enabled AWS to make Lambda cost-effective while maintaining the scalability and performance customers demanded, reinforcing AWS’ role as a leader in efficient cloud infrastructure.

Serverless computing: From novelty to necessity

Lambda’s impact on the cloud ecosystem has been transformational. Before Lambda, developers and information technology teams had to allocate server resources for even fleeting, lightweight tasks, leading to wasted compute power, higher costs and maintenance burdens. Lambda’s serverless model shifted the paradigm. AWS offered the world a glimpse of a new cloud model, freeing developers to focus solely on building applications.

“One of Lambda’s greatest contributions was making distributed applications accessible,” Vogels says. “It wasn’t just about computing in the cloud. It was about fundamentally rethinking what computing could look like when freed from servers.” Lambda’s success sparked a wave of serverless services within AWS, from S3 triggers to DynamoDB integrations, providing developers with an expansive toolkit for event-driven, scalable applications.

As Vogels sees it, Lambda’s impact reaches far beyond AWS. “We’ve seen an explosion of serverless architecture in the industry,” he says. “Companies that wouldn’t have considered themselves tech-forward are now able to build and scale apps quickly. It’s been a game changer.”

Enter generative AI: Building the next chapter of serverless with Bedrock and beyond

Fast forward to today, and AWS finds itself at another frontier: generative artificial intelligence. Just as Lambda introduced serverless computing, AWS is now setting the stage for a future where AI is deeply integrated into business applications. Vogels likens this moment to the early days of Lambda, with gen AI tools such as Bedrock offering powerful capabilities and relying on a robust, cloud-native foundation.

“We’re seeing similar excitement and curiosity around gen AI as we did with Lambda,” says Vogels. “Customers are no longer just talking about AI as a cool technology. They want to build meaningful, scalable applications that are deeply integrated with their data and business logic.” Bedrock, AWS’s gen AI platform, builds on Lambda’s lessons, offering flexibility for users to select and fine-tune a range of large language models based on specific needs.

The parallel between Lambda and Bedrock lies in AWS’ approach to simplifying complex systems and empowering developers. Vogels believes this is just the beginning. “In the same way that Lambda democratized distributed applications, Bedrock and gen AI will democratize intelligent applications. But the infrastructure needs to support it, just as we built Firecracker for Lambda.”

The future: Cloud Generation 2 and the data-driven AI layer

The cloud has come a long way since AWS pioneered infrastructure-as-a-service. However, the next decade will require even more: scalability, security, cultural adaptability and sustainability. Vogels envisions a “Cloud Generation 2,” where compute and AI tools are optimized for localized, data-rich and real-time applications, whether in remote locations or edge environments with sovereign data requirements.

“Compute without data is meaningless,” Vogels says. “In this new era, you’ll need a seamless connection between data systems, wherever they reside.” For AWS, this means further integration of data and AI across every aspect of their cloud offering, enabling applications that can function globally yet respond to local needs in real time.

Vogels also emphasizes the importance of transparency in resource usage, as customers increasingly focus on infrastructure costs and sustainability. “Customers now want to see the impact of their choices,” he says. “They’re asking for insights on costs, resource use, even carbon emissions. And we’re committed to giving them the tools to manage it all effectively.”

Democratizing the Future: AI for the masses

Reflecting on Lambda’s impact, Vogels notes that its greatest achievement may be the ripple effect it has had on the developer community. “We’ve empowered developers to experiment, to create applications that weren’t possible before,” he says. “And now, with AI, we’re about to see that empowerment multiplied.”

AWS’s vision is clear: Empower developers to build the future. From serverless computing to the AI-driven applications of tomorrow. “With Lambda, we saw developers find creative ways to solve problems,” Vogels reflects. “With gen AI, we’re giving them new tools to dream bigger, to build applications that not only perform but understand, respond and adapt.”

What’s next: A vision built on innovation

In celebrating Lambda’s anniversary, Vogels isn’t resting on past successes. Instead, he’s focused on the future, on what the next 10 years will bring. “If Lambda taught us anything, it’s that innovation doesn’t stop,” he says. “We’re just as curious now as we were 10 years ago about what we can build next.”

The success of Lambda in simplifying distributed applications for developers sets a model for gen AI, as AWS aims to democratize intelligent applications with similarly scalable, modular and accessible solutions.

In speaking with Vogels, it’s clear that AWS sees Lambda’s decade of evolution as the backbone for its ambitious gen AI roadmap. But as the industry prepares for AWS re:Invent early next month, the real question is whether AWS can deliver the same game-changing impact in the AI space as it did with serverless computing. Lambda set a high bar with its customer-driven, scalable, and simplified approach to cloud services — principles that are being fused into AWS’ gen AI offerings, from Bedrock to SageMaker.

Yet in a rapidly shifting AI landscape crowded with challengers, AWS faces a critical test: Can it replicate Lambda’s success and deliver AI solutions that resonate just as deeply with developers and enterprises alike? The builders will decide.

Photo: AWS