I recently joined the Blazity team on the Next Gen Web Podcast to talk about how enterprises approach Next.js and Vercel migrations. The conversation covered both the high-level business drivers and the technical details that make these projects successful.
Here’s the full recording:
What we covered:
Enterprise migrations to Vercel – how to make them smooth and effective
Performance, security, SEO & AI SEO – why these are key drivers for adoption
Rolling Releases, Microfrontends & Microservices – modern enterprise patterns
Professional Services at Vercel – how we help enterprise teams avoid mistakes and deliver faster
AI workflows – why Vercel is becoming the go-to platform for AI apps and AI-native projects
Timestamps
00:56 – My background at Vercel and role in Professional Services
04:52 – Indie hackers vs. enterprise workflows
10:45 – How Professional Services derisks migrations
13:35 – Why enterprises adopt Vercel (performance, security, SEO, AI SEO)
23:28 – Rolling Releases explained (with my Star Trek analogy)
28:00 – Microfrontends and scaling enterprise e-commerce
35:00 – AI agent + security workflows
42:47 – Vercel AI Cloud and why it matters for AI projects
A fun highlight
One of my favorite parts of the episode is when I explain Rolling Releases. Instead of describing deployment pipelines in abstract terms, I compared it to Star Trek: Captain Picard standing on the bridge, calmly shifting traffic 5%, 10%, 20% at a time, watching dashboards light up with metrics, and then finally calling out “Full throttle, 100%.”
It’s a fun analogy — but it also highlights how much release management has evolved. What used to be a stressful, all-or-nothing event is now a controlled, observable process that gives enterprises more confidence in shipping software.
If you’re in the middle of a Next.js migration, exploring headless e-commerce, or thinking about how to run AI workloads on Vercel, this episode has plenty of practical insights.
EDIT 31 August 2025
FAQ, resources and links to what was talked on the podcast
Q: How do enterprise customers migrate to Next.js and Vercel?
Most follow a staged approach: start with a POC, then an MVP with controlled traffic, then a production rollout. Vercel Professional Services helps validate architecture, run Code Review Audits, and guide release strategies.
Q: What role does Vercel Professional Services play in migrations?
They support architecture decisions, performance audits, code reviews, and training. The aim is to de-risk migrations and accelerate delivery without slowing down internal teams.
Q: What shipped in Vercel Ship for enterprise workflows?
AI Gateway – unified access to AI models with streaming-first infra and CPU-active pricing.
Fluid Compute with active CPU pricing – more cost-efficient workloads by charging only for active compute time, perfect for AI applications and streaming workloads.
Rolling Releases – gradual traffic shifting (5%, 10%, 20%) with real-time observability.
BotID – invisible replacement for CAPTCHAs, powered by request fingerprinting.
AI Agent – investigates anomalies in logs, traffic, and firewall events, then recommends mitigation steps.
Microfrontends – now a first-class citizen on the Vercel platform, supporting horizontal and vertical splits.
Q: What are Rolling Releases and why do they matter?
Instead of flipping traffic 100% at once, Rolling Releases shift users gradually while monitoring metrics. It reduces risk, improves confidence, and allows instant rollback if needed.
Q: How are microfrontends used in enterprise projects?
They split large surfaces like browse vs. checkout into independently deployed apps while preserving a unified experience. This lets teams scale faster without bottlenecks.
Q: What is Vercel doing in the AI space?
Beyond AI Gateway, Vercel’s AI Cloud combines streaming infra, observability, and first-party security features. Enterprises are using it for assistants, personalization, and internal AI tools — all integrated into the same workflows as their web apps.
Vercel is also iterating and improving its AI infrastructure by building its own AI product, v0. That work feeds directly back into the platform, so every team can benefit from enterprise-grade AI infra.
On AI with Vercel, you get:
Streaming: serverless streaming of AI responses
Fluid Compute with active CPU pricing: pay only for CPU usage, not idle time during AI response generation
AI SDK & Chat SDK: developer-friendly abstractions for building AI features quickly
AI observability: monitor requests, latency, and costs across providers
AI templates: ready-to-deploy examples to jump-start projects
Vercel Sandbox: safe environment to experiment with AI features
BotID: invisible verification replacing CAPTCHAs, built on request fingerprinting
Vercel Queues (Beta): distributed job queues for async tasks and background work in AI apps
Vercel MCP (Model Context Protocol): a protocol that standardizes how AI models access external tools and data, opening new possibilities for enterprise AI apps
Streamdown: a package for building and rendering streaming Markdown responses, designed to make AI and real-time apps more reliable and user-friendly
This ecosystem makes Vercel one of the most complete platforms for building AI-native web applications.
Godspeed
https://x.com/dom_sipowicz
https://www.linkedin.com/in/dominiksipowicz/
Top comments (2)
Loved the Picard rolling release analogy—finally a deploy where red alerts stay on dashboards, not pagers. The AI SEO + microfrontends breakdown was super practical. Time to migrate at warp… in 5%, 10%, 20% increments. Engage!
Thanks! That day I recorded two podcasts, but only one has been released so far. The second one will be about how our team delivers Professional Services for Enterprise customers at Vercel, things like code review audits, workshops, architecture reviews, pair programming, and Next.js consulting.
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