By Alexis Bertholf, Global Technical Evangelist
We look at the practical shifts unveiled at AWS re:Invent 2025 and how they’ll influence AI, networking, and cloud strategy heading into 2026.
Another AWS re:Invent is behind us, and this year felt different. While we spent much of last year’s event discussing how to turn tech hype into actionable strategies, this year the conversation was, “We’ve tested the ideas – now let’s actually build the systems.”
The conversations were grounded, the demos were practical, and across the keynotes and hallway chats, a few themes showed up again and again.
Here’s what stood out to me, and what I think is coming in 2026.
AWS announced the introduction of Trainium3 UltraServers, next-generation machine learning chips. The jump from the previous generation is hard to ignore: up to 144 chips per UltraServer, much higher memory bandwidth, and fourfold gains in compute and efficiency. These boxes are built for large-scale training, perfect for the kind of workloads that don’t fit neatly inside a single GPU cluster anymore.
In 2026, training timelines are going to shrink dramatically as a result of this upgrade, and the cost barrier for custom or fine-tuned models is going to drop too. Last year, we saw companies move from “testing” GenAI to “running it in production.” Now, this new hardware is going to help teams move faster and take on models they would’ve avoided before because of cost or time.
One of the biggest announcements this year: AWS and Google Cloud are launching a private, high-speed, native interconnect, built on an open standard they both support.
If you’ve been architecting in multicloud for a while, you know how much friction comes from stitching environments together yourself, and why platforms like Megaport are so critical to keeping your multicloud time- and cost-effective. Seeing AWS partner with another major cloud provider to mutually acknowledge this reality signals a pretty meaningful cultural shift.
I expect other providers to follow suit and embrace a more agnostic outlook next year, making multicloud architectures feel more “designed” instead of “assembled”. This will provide more options to anyone balancing cost, performance, and resilience across providers.
AWS previewed three long-running autonomous agents this year:
I’m not sure why only one of them was lucky enough to get a name – but these agents are built to run for hours or days, not seconds. They’re meant to troubleshoot, deploy, test, review, remediate, and report, all with clear guardrails. This is a noticeable step beyond last year’s “copilot” tools, which were mostly about speeding up human-driven workflows. Now we’re looking at systems that can actually own the workflow.
Our team has been thinking about this space a lot, especially after releasing our own Megaport Terraform Agent repo during the AWS hackathon this year. It’s not hard to imagine these autonomous agents plugging into networking workflows next — provisioning, testing, validating, and repairing infrastructure.
Expect 2026 to bring early production use cases where agents can handle repeatable operational tasks end-to-end.
AWS bumped Site-to-Site VPN throughput to 5 Gbps per tunnel, a massive fourfold increase from the previous limit. This change alone will make hybrid migrations and disaster recovery pathways a lot faster for teams relying on VPN.
AWS also introduced branch connectivity with eero, turning distributed sites into plug-and-play endpoints for AWS networks. It’s a small detail that solves a very real pain point: getting remote teams online quickly and securely without an involved deployment.
In 2025, we saw hybrid networking become more flexible. In 2026 it will become even faster and easier, and that shift is going to matter for organizations trying to modernize without touching every piece of hardware they own.
AWS has announced AI Factories, offering fully managed AWS AI infrastructure directly into your data center – same tooling, same operational model, just running on your own floor space.
For industries with strict sovereignty, compliance, or latency requirements, this answers the question of how to run AI workloads when the data can’t leave your building, allowing you to run sensitive workloads on-campus the same way you would in the cloud
We talked last year about hybrid AI patterns becoming more common. AI Factories take that a step further by making the physical boundary between cloud and on-premises a lot less binary. Thanks to this announcement, I think 2026 may be the year regulated sectors like government, finance, and healthcare move from AI pilots to real deployment.
AWS shared two notable updates for S3 that essentially turn it into an AI engine:
These changes turn S3 into a more capable backbone for retrieval-based AI systems. Larger objects simplify storing massive datasets, and high-scale vector indexing makes it easier to build semantic search, GenAI, and retrieval augmented generation (RAG) applications without bolting on an entirely separate storage system.
Now that storing and retrieving embeddings at scale is accessible, I expect to see a wave of domain-specific AI applications in 2026.
If I review all the announcements from re:Invent 2025, the thread connecting them is pretty clear: everything got more practical (just as we predicted last year). This is thanks to faster training, real multicloud support, agents that handle operational work, simpler hybrid networking, AI you can run inside your own data center, and storage built for AI workloads.
2026 is shaping up to be a year where teams move from “trying things out” to “running them for real.” And the network—the thing that ties all of this together—is going to matter more than ever.
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