How I Would Fix slow pages and weak Core Web Vitals in a Vercel AI SDK and OpenAI automation-heavy service business Using Launch Ready.

Opening

If your Vercel AI SDK app feels slow and your Core Web Vitals are weak, I would assume the problem is not "AI" itself. In most automation-heavy service businesses, the real issue is usually a mix of too much client-side rendering, heavy OpenAI request chains, poor caching, and third-party scripts dragging down LCP and INP.

The first thing I would inspect is the actual user journey on mobile: homepage load, first interaction, form submit, and any AI-powered quote or intake flow. If those four steps are slow, the business pays for it in lower conversion, more support tickets, and wasted ad spend.

Triage in the First Hour

1. Check the live site in Chrome DevTools Lighthouse for mobile.

• Record LCP, CLS, INP, total blocking time, and main bundle size.
• If LCP is above 2.5s or INP is above 200ms, treat it as a launch risk.

2. Open Vercel Analytics and Web Vitals.

• Look for real-user p75 and p95 metrics by route.
• Identify which page has the worst bounce rate and slowest interaction.

3. Inspect Vercel function logs.

• Find slow server actions, API routes, and OpenAI calls.
• Note any requests above 1s on simple pages or above 3s on AI flows.

4. Review the browser network waterfall.

• Check for large JS chunks, repeated API calls, font delays, image overfetching, and third-party scripts.
• Confirm whether the page waits on AI data before rendering anything useful.

5. Audit the AI SDK integration files.

• Look at streaming setup, prompt construction, tool calls, retries, and error handling.
• Check whether the UI blocks while waiting for completion instead of showing partial output.

6. Review Cloudflare and Vercel settings.

• Confirm caching headers, compression, image optimization, redirects, SSL status, and WAF rules.
• Make sure DNS is stable and there are no redirect loops or mixed-content issues.

7. Inspect secrets and environment variables.

• Verify OpenAI keys are only server-side.
• Check that no secret is exposed to the client bundle or logged in plaintext.

8. Check dependency health.

• Look for recent package updates affecting React rendering, AI SDK behavior, or build output size.
• Run a quick audit for vulnerable packages before touching production.

npm run build
npm run lint
npx lighthouse https://your-domain.com --preset=mobile

Root Causes

| Likely cause | What it looks like | How I confirm it | |---|---|---| | Over-rendering on the client | Blank page until JS loads; poor LCP | Check if key content is behind client-only components | | Large bundles from AI UI code | Slow first load; high JS execution time | Inspect bundle analyzer and route chunk sizes | | Uncached OpenAI requests | Every page load hits model APIs again | Review server logs and response headers | | Blocking tool chains | User waits while multiple tools run serially | Trace request timing in logs and measure each step | | Third-party script bloat | Slow INP and layout shifts | Disable non-essential scripts one by one | | Weak asset delivery | Images/fonts delay paint; CLS spikes | Check image dimensions, font loading strategy, CDN caching |

The most common failure I see is this: founders wire OpenAI into the critical path of page rendering. That turns a marketing page or intake form into an API-dependent experience that fails slowly under load.

Another common issue is trying to make one route do everything. The same page handles marketing content, auth state, prompt input, tool execution, chat history, analytics tags, and billing prompts. That creates a big bundle and a brittle UX.

The Fix Plan

1. Separate marketing pages from app logic.

• Keep landing pages static where possible.
• Move AI workflows behind authenticated app routes only when needed.

2. Make the first paint fast without waiting on OpenAI.

• Render headline, CTA, proof points, and form shell immediately.
• Load AI-generated content after initial paint or on user action.

3. Stream responses instead of blocking full completion.

• Use Vercel AI SDK streaming so users see progress early.
• Show partial output with clear loading states rather than a frozen spinner.

4. Reduce client-side JavaScript.

• Convert non-interactive sections to server-rendered or static content.
• Split heavy editor/chat components into lazy-loaded chunks.

5. Cache what can be cached safely.

• Cache marketing content at the edge where appropriate.
• Reuse stable prompts or templates server-side if they do not contain private user data.

6. Put expensive work behind queues when needed.

• If an automation triggers multiple steps like enrichment, email generation, CRM update, and webhook fanout, do not run all of it inline on the request thread.
• Return fast confirmation first; process background tasks separately.

7. Tighten Cloudflare delivery settings.

• Enable compression and caching rules for static assets.
• Use image optimization and set proper cache headers for fonts and media.

8. Remove unnecessary third-party scripts.

• Kill any tracker you cannot justify with revenue impact or compliance need.
• Delay non-essential analytics until after consent or after interaction where required.

9. Harden security while fixing speed.

• Keep OpenAI keys server-side only.
• Validate all user input before sending it to models or downstream tools.
• Add rate limits to protect against abuse that can spike costs and degrade performance.

10. Improve error handling so failures do not feel broken.

• Show retry buttons for model timeouts.
• Give users a fallback path if generation fails after 10-15 seconds.

My bias here is simple: fix rendering architecture before tuning prompts. A faster prompt will not save a page that ships 900 KB of JS before showing anything useful.

Regression Tests Before Redeploy

1. Run mobile Lighthouse again on the changed routes.

• Target: LCP under 2.5s on key landing pages.
• Target: CLS under 0.1 and INP under 200ms on core interactions.

2. Test slow network conditions.

• Use Fast 3G throttling in DevTools.
• Confirm above-the-fold content still appears quickly without waiting for AI responses.

3. Verify AI flow behavior under timeout conditions.

• Simulate OpenAI latency spikes or failed requests.
• Acceptance criteria: user sees a friendly error within 15 seconds max with retry available.

4. Check logs for secret leakage risk.

• Confirm no API keys appear in browser console output or server logs.

5. Test forms end to end with realistic edge cases.

• Empty input
• Very long input
• Special characters
• Duplicate submissions
• Network interruption mid-request

6. Recheck redirects and SSL behavior after deployment changes.

• Confirm HTTP to HTTPS redirect works once only.
• Confirm subdomains resolve correctly with no loop or mixed-content warnings.

7. Verify uptime monitoring alerts are firing correctly.

• Acceptance criteria: alert arrives within 5 minutes of downtime detection.

8. Run one manual QA pass on mobile Safari and Chrome Android if your audience uses both heavily. Discrepancies there often hide layout shift bugs that desktop testing misses.

Prevention

Use a small performance budget per route:

• JS bundle budget: under 250 KB compressed for marketing pages
• LCP target: under 2.5s p75
• CLS target: under 0.1
• INP target: under 200ms

Add code review checks focused on behavior instead of style:

• Did this change add client-side rendering where server rendering would do?
• Did we introduce another blocking script?
• Are we calling OpenAI during initial page load?
• Are secrets staying server-side?
• Is there a fallback when AI fails?

For cyber security guardrails:

• Keep least privilege on environment variables and deployment access
• Validate inputs before passing them to models or tools
• Rate limit public endpoints
• Log failures without logging sensitive prompts or tokens
• Review dependency updates before merging them into production

For UX guardrails:

• Always show loading states that explain what is happening
• Keep primary CTAs visible before any AI content loads
• Avoid burying forms below long generated sections
• Make empty states useful instead of blank

For observability:

• Track p95 latency by route
• Track model call duration separately from total request duration
• Alert when error rate exceeds 2 percent over 15 minutes
• Watch conversion drop-off after deploys so performance fixes are tied to revenue impact

When to Use Launch Ready

Use Launch Ready when you need me to get the foundation production-safe in 48 hours without turning it into a drawn-out redesign project. This sprint is built for founders who already have something working but need domain setup, email deliverability support via SPF/DKIM/DMARC, Cloudflare hardening, SSL cleanup, deployment sanity checks, secrets handling, caching basics, DDoS protection settings review if relevant to your stack size, uptime monitoring setup, redirects/subdomains cleanup, plus a handover checklist.

• domain connection
• email authentication
• Cloudflare setup
• SSL verification
• deployment check
• environment variables review
• secret handling review
• uptime monitoring
• handover notes

What you should prepare: 1. Access to Vercel admin 2. Domain registrar access 3. Cloudflare access if already connected 4. OpenAI account access if API keys need rotation or review 5. A list of critical pages and automations 6. Any current errors from logs or screenshots

If your site is already live but slow pages are hurting signups or booked calls now, Launch Ready is the right first move before bigger optimization work starts.

Delivery Map

References

1. roadmap.sh frontend performance best practices: https://roadmap.sh/frontend-performance-best-practices 2. roadmap.sh backend performance best practices: https://roadmap.sh/backend-performance-best-practices 3. roadmap.sh api security best practices: https://roadmap.sh/api-security-best-practices 4. Vercel Web Analytics docs: https://vercel.com/docs/analytics/web-vitals 5. OpenAI API docs: https://platform.openai.com/docs

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Take the next step

If this is a problem in your product right now, here is what to do next:

• [Use the free Cyprian tools](/tools) - estimate cost, score app risk, check launch readiness, or pick the right service sprint.

• [Book a discovery call](/contact) - I will tell you honestly whether you need a sprint or if you can DIY the next step.

*Written by Cyprian Tinashe Aarons - senior full-stack and AI engineer helping founders rescue, launch, automate, and scale AI-built products.*