Platform Engineering at QCon San Francisco 2025

Modern AI platforms don’t have to choose between deterministic precision and probabilistic exploration—they need both.

Platform teams frequently inherit systems that were never architected for their current scale, yet are so foundational that downtime can halt the business.

Maintaining up-to-date and secure software across a polyglot fleet is a challenge for any engineering organization. Manual migrations and urgent updates disrupt productivity and require coordination across many teams.

For decades, visualization of service metrics overwhelmingly converges to line charts. The time-centric nature of real-time telemetry further cemented this phenomenon via storage layouts and domain-specific query languages.

The Technical Leadership Challenge: Every successful engineering team faces the same inflection point: transforming beloved internal tools into a legitimate platform business.

When many teams work on a large, complex application, the microservice architecture potentially enables them to work independently and deliver a continuous stream of changes.

In this talk, Matthew will describe lessons learned from over 20+ years of building scalable, secure and stable infrastructure platforms for software in financial services (electronic trading, credit card processing etc.), the talk is relevant to anyone building platforms for mission-critic

At Uber’s scale, ensuring an always-green mainline while processing hundreds of changes per hour is a massive challenge— especially when those changes span multiple language monorepos supporting dozens of business-critical apps.

What does it take to safely delete data at Netflix scale? In large-scale systems, data deletion cuts across infrastructure, reliability, and performance complexities.

Once a stock market darling and a pioneering hyperscaler in the 1990s and early 2000s, eBay has been in steady decline since the 2010s. A household name with a flat business, eBay has been unable to make substantive strides in its market reach or its engineering outcomes in the last 15 years.

Details coming soon.

Expanding the market of your VR game to other platforms may seem like an easy way to increase your revenues but the journey is all but simple. When what makes your game unique is the presence and immersion it provides, reproducing the experience on a flat screen is a daunting task.

AI inference is expensive, but it doesn’t have to be. In this talk, we’ll break down how to systematically drive down the cost per token across different types of AI workloads.

Rust makes things faster but it slows down engineers, right? This was our team’s assumption when we decided to rewrite our code from Kotlin into Rust. But, we were wrong in completely unexpected ways.

In high-performance code, a single misplaced counter increment can cost more than the operation it’s measuring. That creates a paradox: instrument too much and you slow the system down; instrument too little and you miss the insights you need to continuously deliver.

Over the past year, Zoox has invested in integrating Large Language Models (LLMs) into internal developer workflows through a company-wide initiative called Zoox Intelligence (ZI).

Netflix runs on a complex multi-layer cloud architecture made up of thousands of services, caches, and databases. As hardware options, workload patterns, cost dynamics and the Netflix products evolve, the cost-optimal hardware and configuration for running our services is constantly changing.

Behind every productivity leap is a rock-solid platform. Go under the hood with Intuit’s GenOS team to see how vector stores, prompt management, RAG pipelines, and agent orchestration come together to serve ~100 million users.

SS&C Technologies runs 47 trillion dollars of assets on our global private cloud. We have the primitives for infrastructure as well as platforms as a service like Kubernetes, Kafka, NiFi, Databases, etc.

As AI applications demand faster and more intelligent data access, traditional caching strategies are hitting performance and reliability limits.

In January of 2023, the Microsoft Azure Wide Area Network experienced a global outage. If you were a Microsoft customer at the time, you were impacted by this outage.

As companies grow, so does the complexity of keeping distributed systems in sync. At DoorDash, we tackled this challenge while building a high-throughput, domain-oriented data platform for capturing changes across hundreds of services.