Speaker
Abstract
You can't really "opt out" of multicloud anymore. Between cloud concentration risk, SaaS sprawl, and increasing regulatory expectations, most enterprises end up operating across multiple clouds whether they planned to or not. The hard part isn't having two or three providers. It's the hidden operational complexity that follows: demand management with each provider, consistent enforcement of controls, and environments that keep getting harder to run.
That complexity multiplies fast. Multicloud becomes an "N problem" because it is not just clouds — it is clouds times lines of business, times product teams, times data and tooling. Every combination creates more decisions to make and more things to keep consistent: identity, networking, policy, observability, cost, delivery. Traditional engineering execution alone can't scale to that.
The only sustainable approach is to treat multicloud strategy as a product: define customers and outcomes, set boundaries, build a capability map, and run a roadmap with measurable results.
Then AI lands on top of all of this. Used well, AI can reduce the burden of N-scale complexity across architecture, organisational change, and engineering workflows. Used without a solid foundation, it accelerates chaos: faster changes, more drift, more inconsistency, and more risk.
In this talk, I'll share the pre-work that makes the difference — the technical foundations and product management discipline that allow AI to help rather than hurt. No silver bullets, just hard-won patterns that scale.
You'll learn:
- How to frame multicloud as a product with clear users, boundaries, and success metrics
- How to reduce N-scale complexity using capability-based planning and "golden path" thinking
- Where AI helps (and where it amplifies disorder) in platform and architecture work
- Practical patterns for demand management, guardrails, and measurable adoption
Interview
Most companies ended up multicloud by accident — shadow IT, M&A, best-of-breed SaaS. The problem is that nobody treated it as a product. This session is about fixing that. We'll show how to apply product management discipline to cloud strategy: define your customers, set boundaries, build a capability map, run a roadmap with measurable outcomes. For senior developers and architects, this matters because the complexity you're fighting every day is often a strategy problem, not a technology one.
AI is the forcing function. Every AI workload decision — where it runs, how data moves, what controls apply — lands on top of your multicloud foundation. If that foundation is unmanaged chaos, AI makes it worse, faster. Leaders who don't get ahead of this will spend the next two years firefighting instead of building.
Three things we see constantly: no single source of truth (every team has their own cloud view, nobody has the full picture); demand management at N-scale (it's not just clouds — it's clouds x business lines x product teams x data, and every combination creates new decisions); and treating multicloud as an engineering execution problem when it's fundamentally a governance and prioritisation problem.
Filter the noise and sit with the problem before jumping to solutions. We tend to skip prioritisation entirely — or do it once and never revisit it. We focus on features instead of outcomes, on how instead of why and what. Multicloud strategy is no different: most teams are already three solutions deep into a problem they haven't properly defined yet. Slow down. Ask why this matters, what success looks like, and who it serves. The how gets a lot easier after that.
The signal-to-noise ratio. Most conferences are either too vendor-driven or too academic. QCon consistently brings practitioners who are doing the work at scale and are willing to share what actually happened — including what didn't work. That's rare and valuable.
Topics
QCon London 2026 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
From the same track
Monday 16 March
10:35 Mountbatten (6th Fl.) Session architecture How To Run on Three Clouds at Once, and When Not To Ross McFarlane, Kevin Holditch If you've made a bank transfer recently, there's a good chance that Form3 handled it. When we have a wobble, people notice. 11:45 Windsor (5th Fl.) Session AI/ML Your Multicloud Strategy Is a Product Problem - Treat It Like One Luis Henrique Albinati Junior, Surabhi Mahajan You can't really "opt out" of multicloud anymore. Between cloud concentration risk, SaaS sprawl, and increasing regulatory expectations, most enterprises end up operating across multiple clouds whether they planned to or not. The hard part isn't having two or three providers. 13:35 Rutherford (4th Fl.) Unconference Unconference: Tech of Finance Industry 14:45 Mountbatten (6th Fl.) Session Alpha Copilot The Realities of Building an AI Native Fintech Startup David Lin Founder and CEO @Linvest21, Previously CTO @JPMorgan For fifty years, $400 trillion in professionally managed assets sat untouched by external technology. The most analytically demanding investment workflows required a combination of PhD-level quant finance and advanced engineering that no outside vendor could sustain commercially. 15:55 Mountbatten (6th Fl.) Session Platform Engineering Move Fast, Don’t Break Trust: Shipping Constantly with Humans and Beyond Suhail Patel Senior Staff Engineer @Monzo Leading the Platform and Data Functions, Previously @Citymapper Moving fast only works if your systems are engineered with trust and verification in mind. In this talk, we showcase how teams can scale to the point where shipping hundreds of changes a day becomes boring, routine, and safe.