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Headline: Partial Prerendering (PPR) in Next.js serves a static HTML shell from the CDN edge instantly, then streams Suspense-wrapped dynamic children from the origin in the same HTTP response. No full-page ISR staleness, no full-page origin latency. I shipped it on two production routes — here is the model. Key takeaways PPR serves a static HTML shell from the CDN edge , then streams dynamic Suspense children from the origin in the same response. The static shell is built at build time — outside <Suspense> renders statically; inside renders dynamically per request. PPR replaces the ISR vs. dynamic tradeoff for pages that are mostly static with isolated personalized sections. No changes to Server Components or Suspense — just experimental.ppr: 'incremental' in config and export const experimental_ppr = true per route. PPR and use cache are complementary : CDN delivery for the shell, origin memoization for dynamic islands. What does PPR actually do? PPR splits a page into two rendering phases within the same HTTP response. At build time, Next.js freezes everything that does not read dynamic request data into a static HTML shell on the CDN edge. At request time, the CDN delivers the shell at edge latency while the origin streams each <Suspense> boundary's content into the same response. On a product page: navigation, title, and description arrive at CDN speed. The in-stock badge and personalized recommendations stream from the origin a fraction of a second later. The user sees a nearly-complete page immediately. How is PPR different from ISR and streaming Suspense? Strategy First byte Dynamic freshness Staleness ISR (revalidate: N) CDN edge Whole page up to N seconds stale Full page Dynamic rendering Origin 100% fresh; waits for slowest query None Streaming Suspense (no PPR) Origin Fresh; TTFB includes origin latency None PPR CDN edge Dynamic islands 100% fresh Static shell only How do I enable PPR? // next.config.ts export default { experimental : { ppr : ' inc

When we last sat down with Jobs at TechCrunch Disrupt nearly three years ago, his firm Yosemite was brand new and biotech was still reeling from its post-pandemic crash. Now, the venture outfit has a team of 17; a cluster of blockbuster drugs are all losing patent protection in roughly the same window, creating all kinds of new opportunities; and AI has gone from a curiosity to, in Jobs's words, a huge part of what Yosemite does. "I didn't expect Yosemite to be moving this fast," he said.

Check this out: migrating Off OpenAI: A Backend Engineer's Notes From Production I still remember the morning I opened our team's monthly invoice and nearly spilled cold brew on my mechanical keyboard. We were burning through OpenAI credits like it was nobody's business — specifically, north of $500/month for what amounted to a chat-completion endpoint and some embedding lookups. As the backend engineer who had inherited the LLM integration six months prior, I felt personally responsible. So I did what any self-respecting engineer does at 2 AM with too much caffeine: I benchmarked alternatives. What I found annoyed me. DeepSeek V4 Flash was sitting there at $0.25/M output tokens while GPT-4o charges $10.00/M. That's a 40× price difference for output that, in my blind tests, 80% of users couldn't distinguish. The $500/month bill could plausibly become $12.50. My CFO would weep tears of joy. This post is the migration journal I wish I'd had before I started. fwiw, I've already done the swap across three production services. Here's what worked, what didn't, and exactly how much coffee I drank. The Math That Made Me Pick Up a Keyboard Before I show you code, let's talk numbers — because if you're going to convince your team or your boss, you'll need a slide that fits on one screen. I pulled together the pricing for the models I actually considered routing traffic through. All figures are per million tokens, USD: Model Provider Input $/M Output $/M Relative to GPT-4o GPT-4o OpenAI $2.50 $10.00 1× (baseline) GPT-4o-mini OpenAI $0.15 $0.60 16.7× cheaper DeepSeek V4 Flash Global API $0.18 $0.25 40× cheaper Qwen3-32B Global API $0.18 $0.28 35.7× cheaper DeepSeek V4 Pro Global API $0.57 $0.78 12.8× cheaper GLM-5 Global API $0.73 $1.92 5.2× cheaper Kimi K2.5 Global API $0.59 $3.00 3.3× cheaper Let me be clear about something: those numbers come straight from the provider's pricing pages at the time I ran the analysis. I have not invented, rounded up, or "adjusted" anything her