Cursor realtime

Polling Causing Excessive API Calls in Cursor-Built App

Your Cursor-generated application uses client-side polling (setInterval with fetch) to check for updates, but the polling is far too aggressive. Your API is receiving thousands of unnecessary requests per minute, driving up server costs, hitting rate limits, and degrading performance for all users.

Cursor often generates polling as the simplest way to achieve "real-time" updates — a setInterval that calls the API every 1-2 seconds. This works fine with one user in development, but in production with hundreds or thousands of concurrent users, each polling at 1-second intervals, the request volume becomes unsustainable. A seemingly innocent feature like checking for new notifications can generate millions of API calls per day.

The problem compounds because the polling continues even when the browser tab is in the background, the user is idle, or there's no new data to fetch, wasting bandwidth and battery on mobile devices.

Error Messages You Might See

429 Too Many Requests Rate limit exceeded: retry after 60 seconds API quota exhausted Error: Network request failed (ERR_INSUFFICIENT_RESOURCES) Server responded with status 503 Service Unavailable
429 Too Many RequestsRate limit exceeded: retry after 60 secondsAPI quota exhaustedError: Network request failed (ERR_INSUFFICIENT_RESOURCES)Server responded with status 503 Service Unavailable

Common Causes

  • Polling interval too short — Cursor set a 1-second or 500ms polling interval when 15-30 seconds would suffice for most use cases
  • No visibility check — Polling continues at full speed when the browser tab is hidden or the user switches to another app
  • No change detection — Every poll fetches the full dataset even when nothing has changed, wasting bandwidth and server resources
  • Multiple polling loops running — Component re-renders create duplicate setInterval timers without clearing the previous ones, multiplying request volume
  • No backoff on errors — When the server returns errors (500, 429), the client keeps polling at the same rate, making the overload worse
  • Polling on every page — The polling code runs globally instead of only on pages where real-time updates are needed

How to Fix It

  1. Increase polling interval — For most features, 15-30 second intervals are sufficient. Notifications can poll every 60 seconds. Only stock tickers or live gaming truly need sub-second updates
  2. Pause when tab is hidden — Use the Page Visibility API: document.addEventListener('visibilitychange', () => { if (document.hidden) clearInterval(poll); else startPolling(); })
  3. Implement ETag/If-Modified-Since — Return 304 Not Modified when data hasn't changed, saving bandwidth and server processing. Use the ETag or Last-Modified headers
  4. Clean up intervals on component unmount — In React: return a cleanup function from useEffect. In Vue: clear the interval in onUnmounted. This prevents duplicate polling loops
  5. Add exponential backoff on errors — When the server returns 429 or 5xx, double the polling interval each time (2s, 4s, 8s, 16s...) and reset to normal when requests succeed
  6. Switch to push-based updates — Replace polling with SSE, WebSockets, or a service like Supabase Realtime, Firebase, or Pusher for truly real-time features

Real developers can help you.

Meïr Ankri Meïr Ankri Full-stack developer specializing in React / Next.js / Node.js with 6+ years of experience. I've worked across various sectors including automotive (Reezocar/Société Générale), healthcare (Medical Link SaaS), and e-commerce (Glasman). I build web apps end-to-end, from architecture to production, with a focus on scalability, performance, and code quality. I also mentor junior developers and contribute to technical decisions and code reviews. hanson1014 hanson1014 Full-stack developer experienced in fixing and deploying AI-generated apps from Lovable, Bolt.new, Cursor, and Replit. I specialize in debugging Supabase integration issues (auth flows, RLS policies, database connections), fixing broken deployments, resolving routing/blank screen problems, and cleaning up messy React/Vite codebases. I also build production apps with the Claude API and have shipped a Mac desktop dev tool (Nexterm from scratch. Based in Hong Kong, fast turnaround. Kingsley Omage Kingsley Omage Fullstack software engineer passionate about AI Agents, blockchain, LLMs. zipking zipking I am a technologist and product builder dedicated to creating high-impact solutions at the intersection of AI and specialized markets. Currently, I am focused on PropScan (EstateGuard), an AI-driven SaaS platform tailored for the Japanese real estate industry, and exploring the potential of Archify. As an INFJ-T, I approach development with a "systems-thinking" mindset—balancing technical precision with a deep understanding of user needs. I particularly enjoy the challenge of architecting Vertical AI SaaS and optimizing Small Language Models (SLMs) to solve specific, real-world business problems. Whether I'm in a CTO-level leadership role or hands-on with the code, I thrive on building tools that turn complex data into actionable value. Milan Surelia Milan Surelia Milan Surelia is a Mobile App Developer with 5+ years of experience crafting scalable, cross-platform apps at 7Span and Meticha. At 7Span, he engineers feature-rich Flutter apps with smooth performance and modern UI. As the Co-Founder of Meticha, he builds open-source tools and developer-focused products that solve real-world problems. Expertise: 💡 Developing cross-platform apps using Flutter, Dart, and Jetpack Compose for Android, iOS, and Web. 🖋️ Sharing insights through technical writing, blogging, and open-source contributions. 🤝 Collaborating closely with designers, PMs, and developers to build seamless mobile experiences. Notable Achievements: 🎯 Revamped the Vepaar app into Vepaar Store & CRM with a 2x performance boost and smoother UX. 🚀 Launched Compose101 — a Jetpack Compose starter kit to speed up Android development. 🌟 Open source contributions on Github & StackOverflow for Flutter & Dart 🎖️ Worked on improving app performance and user experience with smart solutions. Milan is always happy to connect, work on new ideas, and explore the latest in technology. Prakash Prajapati Prakash Prajapati I’m a Senior Python Developer specializing in building secure, scalable, and highly available systems. I work primarily with Python, Django, FastAPI, Docker, PostgreSQL, and modern AI tooling such as PydanticAI, focusing on clean architecture, strong design principles, and reliable DevOps practices. I enjoy solving complex engineering problems and designing systems that are maintainable, resilient, and built to scale. Nam Tran Nam Tran 10 years as fullstack developer ISHANTDEEP SINGH ISHANTDEEP SINGH Senior Software Engineer with 7+ years of experience in React, JavaScript, TypeScript, Next.js, and Node.js. I’ve also worked as a tech lead for startups, owning end-to-end technical execution including architecture, development, scaling, and delivery. I bring a strong mix of hands-on coding, product thinking, and technical leadership, and I’m comfortable building products from scratch as well as improving and scaling existing systems. rayush33 rayush33 JavaScript (React.js, React Native, Node.js) Developer with demonstrated industry experience of 4+ years, actively looking for opportunities to hone my skills as well as help small-scale business owners with solutions to technical problems PawelPloszaj PawelPloszaj I'm fronted developer with 10+ years of experience with big projects. I have small backend background too

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Frequently Asked Questions

How often should I poll my API?

It depends on the use case. Chat messages: 5-10 seconds (or switch to WebSockets). Notifications: 30-60 seconds. Dashboard data: 30 seconds to 5 minutes. Always consider using push-based alternatives (SSE, WebSockets) for true real-time needs.

How do I calculate the API load from polling?

Multiply: (concurrent users) x (polls per minute) x (endpoints polled). Example: 1000 users polling 3 endpoints every 5 seconds = 1000 x 12 x 3 = 36,000 requests per minute. This adds up fast and is why push-based solutions are preferred at scale.

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