If you’re exploring how AI-assisted development enhances WordPress performance, you may also want to read:
- Vibe Coding Case Study – Real-world examples of AI × human collaborative development
- High-Performance Context Design – A practical guide to structuring AI workflows in WordPress
Practical strategies from “Will It Be Sunny Tomorrow?”, a regional event & community platform built with WordPress and AI.
Managing API costs, reducing server load, and maintaining stable performance are essential for any event platform—especially for small teams or individual creators. This guide summarizes practical cost-saving strategies, API optimization methods, and scalable system design used in the operation of the regional event platform Will It Be Sunny Tomorrow? (event.r2fish.com).
Although the site runs on a single ConoHa WING shared server, this is a strategic choice to balance performance, cost, and long-term maintainability. During the design phase, a multi-server configuration with an Nginx VPS front layer was considered, but the current architecture provides the best cost-performance ratio at this stage.

1. Why Cost Optimization Matters for Event Platforms
Event platforms often depend on external APIs such as Google Search, Google Places, OCR, and AI review pipelines. These services are powerful, but:
- API usage scales unpredictably
- Costs increase as the site grows
- Server load becomes unstable during peak hours
- Inefficient requests degrade performance
Cost management is therefore both a financial and scalability strategy.
2. Current System Architecture (ConoHa WING + AI Modules)
Will It Be Sunny Tomorrow? operates on:
- WordPress (SWELL child theme)
- ConoHa WING shared hosting
- Custom modules for forms, OCR autofill, facility generation, taxonomy, AI reviews, and link crawling
- AI pipelines for classification, validation, scoring, and summarization
This architecture prioritizes simplicity, predictable cost, easy maintenance, and stable performance for regional-scale traffic.
3. Google API Cost Management Strategies
3-1. Reduce Places API Consumption
- Cache place results per region
- Avoid repeated detail lookups
- Validate URLs before calling the API
- Request minimal fields instead of full payloads
These optimizations significantly reduced API usage without lowering data quality.
3-2. Control Search API Requests
- Generate structured search queries (AI-assisted)
- Batch requests by region
- Avoid processing low-density areas unnecessarily
- Stop querying once sufficient valid URLs are found
The goal is to gather enough reliable data, not everything.
3-3. Cache All Verified URLs
Every validated URL is stored and reused for future crawls, improving speed and reducing unnecessary API calls.
4. AI Review Pipeline: Balancing Cost and Accuracy
The AI review pipeline is used for classification, region detection, URL validation, facility scoring, and more. To optimize performance:
- Use lightweight models for simple tasks
- Reserve advanced models for ambiguous or complex inputs
- Reuse previously generated outputs
- Avoid duplicate sibling URLs within the same region
This hybrid method keeps accuracy high while significantly reducing token cost.
5. Server Load Reduction Techniques
- Cache regional link JSON files
- Avoid heavy synchronous operations during front-end rendering
- Offload AI tasks to worker modules
- Lazy-load images and hero visuals
- Optimize user uploads using WebP conversion
The platform minimizes PHP load and maintains stable performance even under shared hosting.
6. Scaling Strategy for Future Growth
System growth is planned in stages while maintaining current stability:
- Phase 1 — Keep ConoHa WING as the core (current setup)
- Phase 2 — Add a VPS as a reverse proxy with Nginx
- Phase 3 — Move AI pipelines to dedicated workers
This roadmap preserves flexibility without requiring major redesign.
7. Summary: Sustainable Operation Through Smart Architecture
Cost optimization ensures that an event platform remains stable, predictable, and scalable, even when operated by a single creator.
- Use APIs intentionally
- Cache aggressively
- Separate heavy tasks from front-end rendering
- Select infrastructure based on cost-performance
- Keep architecture simple until scale requires expansion
Will It Be Sunny Tomorrow? continues to evolve with this philosophy at its core.
Related Technical Guides
- Vibe Coding Case Study – Practical lessons from AI-assisted development
- ChatGPT × WordPress: High-Performance Context Design
- Cost & Operation Optimization Tips for Event Platforms
開発者プロフィール
Rさん(福祉施設 支援員/地域情報サイト運営/元エンジニア)
福祉現場に従事しながら、余暇時間を使ってChatGPTとの共同開発を推進。
「人は検証と指示、AIはコーディング」という役割分担を確立し、短期間で多機能な地域プラットフォームを構築。
本ページの内容は、2025年9月〜にかけてRさんとChatGPTが実際に構築した
「明日は晴れるかな」プロジェクトの開発記録に基づいています。
制作:ChatGPT(AI生成)
監修:Rさん(R2Fish Project)
本ページは ChatGPT が生成した初稿をもとに、
Rさんが技術精度・構成を監修し “実務で使える形” に仕上げた共同制作コンテンツです。