Scraping Twitter in 2025: A Developer’s Guide to Surviving the API Apocalypse

TL;DR: Tested 4 approaches to access Twitter data after APIv2 became unusable. Winner: twitterapi.io (100K free credits). DIY scraping costs $10+/GB in proxies. Code included for Next.js + Drizzle ORM. See my app that got me blocked by YC’s CEO.

The Backstory: When Twitter Pulled the Rug

Two weeks ago, my rant about Twitter’s API collapse blew up with 245K views.Got flooded in the comments with alternatives! Thank you! I spent 60+ hours stress-testing every solution under real-world conditions.

Here’s what actually works in mid-2025.

⚔️ The Contenders: 4 Paths Through the Wasteland

1. Self-Hosted: Nitter (The Idealist’s Trap)

nitter

The Promise: Open-source, privacy-focused Twitter frontend with RSS feeds.

The Reality:

# Setup pain points  
$ git clone https://github.com/zedeus/nitter  
$ docker-compose up -d # Surprise! Needs guest account pool + proxies  

✅ Pros:

• Full control over data pipeline
• No third-party rate limits

❌ Cons:

• Constant guest account rotation
• Public instances get nuked within hours
• ≈40% failure rate during my load tests

Verdict: ❤️ for hobbyists, ☠️ for production.

2. Managed APIs (The Pragmatic Choice)

🥇 twitterapi.io – My Winner

twitterapi.io

Pricing That Doesn’t Suck:

• 100,000 free credits on signup (≈6,600 tweets)
• $0.15 per 1k tweets (15 credits/tweet)
• Purchased credits never expire

Performance:

▶ ab -n 1000 -c 50 https://api.twitterapi.io/v1/user_tweets  
Requests per second: 142.51 [#/sec] (mean)  

✅ Why it wins:

• OpenAPI spec = instant openapi-typescript integration
• Crypto payments accepted
• Webhook support for real-time monitoring

❌ Gotcha: No recurring free tier after signup bonus.

🥈 Apify – The Data Scientist’s Hammer

API Twitter Scraper

Pricing:

• $5 free monthly credits
• ≈$0.45 per 1k tweets

✅ Killer features:

• Scrape followers/likes/search with point-and-click config
• Export to S3/BigQuery
• Handle 1M+ tweet jobs

❌ Warning: Costs balloon if your scraper isn’t optimized.

3. DIY Scraping (The Pain Cave)

Toolchain:

the-convocation/twitter-scraper

• Playwright
• IPRoyal residential proxies ($15/GB)

The Cold Hard Truth:

You’ll hate your life

✅ Pros:

• Ultimate flexibility

❌ Cons:

• 15+ hrs/week maintaining scrapers
• $10-$50/day in proxy costs
• Your personal Twitter account will get banned

☠️ Critical Lessons (Paid in Blood)

1. My app (@TheRoastBotApp) got limited for „inauthentic behavior“ after 48 hrs
2. Residential proxies ≠ invisibility cloaks – Twitter fingerprints browser/network stacks
3. Always abstract your data layer:

// Generic interface saved me  
interface SocialDataSource {  
  getUserTweets(userId: string): Promise<Tweet[]>;  
}  

class TwitterAPI implements SocialDataSource { ... }  
class ApifySource implements SocialDataSource { ... } // Easy swap!  

💻 Technical Deep Dive: Next.js + Drizzle ORM

Full Architecture:

graph LR  
A[Next.js App Router] --> B[twitterapi.io]  
A --> C[PostgreSQL]  
C --> D[Drizzle ORM]  
D --> E[The Roast Bot Frontend]  

Implementation:

lib/twitter.ts

import { drizzle } from 'drizzle-orm/postgres-js';  
import postgres from 'postgres';  

const connection = postgres(process.env.DATABASE_URL!);  
export const db = drizzle(connection);  

export const getTweets = async (userId: string) => {  
  const res = await fetch(  
    `https://api.twitterapi.io/v1/user_tweets?user_id=${userId}`,  
    { headers: {'x-api-key': `${process.env.TWITTERAPI_KEY}` } }  
  );  
  return res.json();  
};  

drizzle/schema.ts

import { pgTable, text, timestamp } from 'drizzle-orm/pg-core';  

export const tweets = pgTable('tweets', {  
  id: text('id').primaryKey(),  
  userId: text('user_id').notNull(),  
  content: text('content').notNull(),  
  scrapedAt: timestamp('scraped_at').defaultNow(),  
});  

app/api/ingest/route.ts

import { db } from '@/lib/db';  
import { tweets } from '@/drizzle/schema';  

export async function POST(req: Request) {  
  const { userId } = await req.json();  
  const tweetData = await getTweets(userId);  

  await db.insert(tweets).values(tweetData).onConflictDoNothing();  

  return new Response(JSON.stringify({ success: true }), {  
    status: 200,  
    headers: { 'Content-Type': 'application/json' }  
  });  
}  

🏆 The Verdict

Epilogue: The Roast Bot Rebellion

This research birthed The Roast Bot – which got me blocked by YC’s CEO in 48 hours. Worth it.

Final Advice:

„Treat Twitter data like radioactive material—minimize exposure, and always have a containment plan.“

Discussion Time:

• What’s your scraping horror story?
• Any better solutions I missed?
• Want the Bun worker code? Ask below! 👇

Like this guide? I write frequently about Next.js and scraping at @TheRoastBotApp (until Elon bans me).