Ask HN: List of SaaS with number of users?

#python #typescript #react #saas

{
  "title": "SaaS User Count Database: How to Track and Benchmark Active Users Across Popular Tools",
  "slug": "saas-user-count-database-benchmarking-guide",
  "meta_description": "Comprehensive list of SaaS user counts plus how to build your own tracking system. Benchmarking data for developers, founders, and product teams.",
  "tags": ["saas", "metrics", "benchmarking", "product-analytics", "developer-tools"],
  "body": "# SaaS User Count Database: How to Track and Benchmark Active Users Across Popular Tools\n\nIf you're building a SaaS product, understanding how your user count stacks up against competitors is crucial. Yet finding reliable, up-to-date user numbers for SaaS products is surprisingly difficult. Most companies guard this data jealously, and when they do share numbers, it's often in press releases designed to paint the rosiest picture possible.\n\nThis guide provides both a curated list of SaaS user counts and—more importantly—a practical framework for tracking and benchmarking user metrics yourself.\n\n## Why SaaS User Counts Matter (And Why They're Hard to Find)\n\nUser counts serve multiple purposes for founders and product teams:\n\n- **Competitive benchmarking**: Understanding where you stand in your market segment\n- **Investor storytelling**: Contextualizing your growth trajectory\n- **Market sizing**: Validating TAM assumptions with real-world data\n- **Feature prioritization**: Larger competitors' user counts can indicate market demand\n\nThe challenge? Public companies must disclose paying customers in earnings reports, but private companies have no such obligation. Even when numbers are shared, definitions vary wildly: registered users vs. active users vs. paying customers.\n\n## Current SaaS User Count Data (2024)\n\nHere's a curated list of SaaS products with publicly available user data, focused on developer tools and productivity software:\n\n### Developer Tools & Infrastructure\n\n- **GitHub**: 100M+ developers (2023)\n- **GitLab**: 30M+ registered users (2023)\n- **Vercel**: 1M+ developers (2023)\n- **Railway**: 100K+ developers (2023)\n- **Supabase**: 1M+ developers (2024)\n- **Clerk**: 100K+ users (2023)\n- **PlanetScale**: 100K+ databases created (2023)\n\n### Productivity & Collaboration\n\n- **Slack**: 20M+ daily active users (2023)\n- **Notion**: 30M+ users (2023)\n- **Linear**: 20K+ companies (2023)\n- **Superhuman**: 1M+ on waitlist, est. 500K+ active (2023)\n- **Cron (Notion Calendar)**: Acquired before public numbers\n- **Height**: 10K+ users (2023)\n\n### Development Platforms\n\n- **Replit**: 25M+ users (2023)\n- **CodeSandbox**: 4M+ developers (2023)\n- **StackBlitz**: 3M+ developers (2023)\n\n### Analytics & Monitoring\n\n- **PostHog**: 50K+ deployments (2023)\n- **Sentry**: 4M+ developers (2023)\n- **Mixpanel**: 8K+ customers (2022)\n- **Amplitude**: 2K+ customers (2022)\n\n**Note**: These numbers come from company announcements, press releases, and earnings calls. Treat them as approximate and be aware that \"users\" definitions vary.\n\n## Building Your Own SaaS Tracking System\n\nRather than relying on stale data, build a system to continuously track competitor metrics. Here's a practical Python implementation using web scraping and API monitoring:\n\n```

python\nimport requests\nfrom datetime import datetime\nimport sqlite3\nfrom typing import Dict, Optional\n\nclass SaaSMetricsTracker:\n    def __init__(self, db_path: str = \"saas_metrics.db\"):\n        self.db = sqlite3.connect(db_path)\n        self._init_db()\n    \n    def _init_db(self):\n        self.db.execute(\"\"\"\n            CREATE TABLE IF NOT EXISTS metrics (\n                id INTEGER PRIMARY KEY,\n                company TEXT,\n                metric_type TEXT,\n                value INTEGER,\n                source TEXT,\n                timestamp DATETIME,\n                UNIQUE(company, metric_type, timestamp)\n            )\n        \"\"\")\n        self.db.commit()\n    \n    def track_github_stars(self, repo: str) -> Optional[Dict]:\n        \"\"\"Track GitHub stars as a proxy for developer interest\"\"\"\n        try:\n            response = requests.get(\n                f\"https://api.github.com/repos/{repo}\",\n                headers={\"Accept\": \"application/vnd.github.v3+json\"}\n            )\n            data = response.json()\n            \n            metric = {\n                \"company\": repo.split(\"/\")[1],\n                \"metric_type\": \"github_stars\",\n                \"value\": data[\"stargazers_count\"],\n                \"source\": f\"github:{repo}\",\n                \"timestamp\": datetime.now()\n            }\n            \n            self._save_metric(metric)\n            return metric\n        except Exception as e:\n            print(f\"Error tracking {repo}: {e}\")\n            return None\n    \n    def track_npm_downloads(self, package: str) -> Optional[Dict]:\n        \"\"\"Track NPM weekly downloads for TypeScript/React tools\"\"\"\n        try:\n            response = requests.get(\n                f\"https://api.npmjs.org/downloads/point/last-week/{package}\"\n            )\n            data = response.json()\n            \n            metric = {\n                \"company\": package,\n                \"metric_type\": \"npm_weekly_downloads\",\n                \"value\": data[\"downloads\"],\n                \"source\": f\"npm:{package}\",\n                \"timestamp\": datetime.now()\n            }\n            \n            self._save_metric(metric)\n            return metric\n        except Exception as e:\n            print(f\"Error tracking {package}: {e}\")\n            return None\n    \n    def _save_metric(self, metric: Dict):\n        self.db.execute(\"\"\"\n            INSERT OR REPLACE INTO metrics \n            (company, metric_type, value, source, timestamp)\n            VALUES (?, ?, ?, ?, ?)\n        \"\"\", (\n            metric[\"company\"],\n            metric[\"metric_type\"],\n            metric[\"value\"],\n            metric[\"source\"],\n            metric[\"timestamp\"]\n        ))\n        self.db.commit()\n    \n    def get_growth_rate(self, company: str, metric_type: str, days: int = 30) -> Optional[float]:\n        \"\"\"Calculate growth rate over specified period\"\"\"\n        cursor = self.db.execute(\"\"\"\n            SELECT value, timestamp FROM metrics\n            WHERE company = ? AND metric_type = ?\n            ORDER BY timestamp DESC LIMIT 2\n        \"\"\", (company, metric_type))\n        \n        results = cursor.fetchall()\n        if len(results) < 2:\n            return None\n        \n        new_value, old_value = results[0][0], results[1][0]\n        return ((new_value - old_value) / old_value) * 100\n\n# Usage example\ntracker = SaaSMetricsTracker()\n\n# Track developer tools\ntracker.track_github_stars(\"vercel/next.js\")\ntracker.track_github_stars(\"supabase/supabase\")\ntracker.track_npm_downloads(\"react\")\ntracker.track_npm_downloads(\"@clerk/clerk-react\")\n\n# Calculate growth\ngrowth = tracker.get_growth_rate(\"next.js\", \"github_stars\")\nprint(f\"Next.js star growth: {growth:.2f}%\")\n

```\n\nThis tracker gives you:\n\n- **Persistent storage** of competitor metrics over time\n- **Growth rate calculations** to spot trending tools\n- **Multiple data sources** (GitHub, NPM, extensible to others)\n- **Historical comparison** capabilities\n\n## Alternative Proxy Metrics When User Counts Aren't Public\n\nWhen companies don't publish user numbers, track these proxies:\n\n### For Developer Tools\n- **GitHub stars and fork counts**: Strong signal for developer interest\n- **NPM/PyPI download trends**: Weekly downloads indicate adoption\n- **Stack Overflow questions**: Growing question volume = growing user base\n- **Job postings mentioning the tool**: Companies hiring for specific tools\n\n### For SaaS Products\n- **LinkedIn employee count growth**: Hiring patterns indicate revenue growth\n- **Domain authority and organic traffic**: Use Ahrefs/SEMrush APIs\n- **Chrome extension users**: Many SaaS tools have browser extensions with public install counts\n- **Twitter/X follower growth**: Weak signal but easy to track\n\n### Building a TypeScript Dashboard\n\nFor continuous monitoring, build a simple React dashboard:\n\n```

typescript\nimport { useQuery } from '@tanstack/react-query';\nimport { LineChart, Line, XAxis, YAxis, Tooltip } from 'recharts';\n\ninterface Metric {\n  company: string;\n  value: number;\n  timestamp: string;\n}\n\nconst fetchMetrics = async (company: string): Promise<Metric[]> => {\n  const response = await fetch(`/api/metrics/${company}`);\n  return response.json();\n};\n\nexport const CompetitorDashboard = ({ companies }: { companies: string[] }) => {\n  const queries = companies.map(company => \n    useQuery(['metrics', company], () => fetchMetrics(company))\n  );\n\n  return (\n    <div className=\"grid grid-cols-2 gap-4\">\n      {companies.map((company, idx) => {\n        const data = queries[idx].data || [];\n        return (\n          <div key={company} className=\"border rounded p-4\">\n            <h3 className=\"font-bold mb-2\">{company}</h3>\n            <LineChart width={400} height={200} data={data}>\n              <XAxis dataKey=\"timestamp\" />\n              <YAxis />\n              <Tooltip />\n              <Line type=\"monotone\" dataKey=\"value\" stroke=\"#8884d8\" />\n            </LineChart>\n          </div>\n        );\n      })}\n    </div>\n  );\n};\n

\n\n## The Real Value: Understanding Context, Not Just Numbers\n\nRaw user counts mean nothing without context. A B2B tool with 5,000 enterprise customers might generate more revenue than a free developer tool with 5 million users. \n\nWhat matters:\n\n- User quality over quantity: 1,000 paying customers beats 100,000 tire-kickers\n- Growth trajectory: 10% MoM growth matters more than absolute numbers\n- Market position: Being #3 in a $10B market beats being #1 in a $100M market\n- Engagement metrics: DAU/MAU ratio reveals product stickiness\n\nUse the data above and tracking systems as a starting point, but dig deeper. Read earnings calls, analyze pricing pages, monitor team hiring patterns. The most valuable insights come from synthesizing multiple data sources, not from any single metric.\n\n## Conclusion\n\nWhile finding exact SaaS user counts remains challenging, the combination of public data points, proxy metrics, and automated tracking gives you a solid foundation for competitive analysis. Build your own tracking infrastructure, focus on growth trends over absolute numbers, and remember that sustainable SaaS success comes from solving real problems—not from hitting arbitrary user count milestones.\n\nThe Python tracker and

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