Why I Built SaaS Price Pulse: The Story Behind 18 Years of Pricing Data

It started with a price hike I missed. A competitor raised prices 3 months before we noticed. By that time, I'd already locked our roadmap into a competitive positioning that was suddenly wrong. That day, I realized we were flying blind—and I wasn't alone.

The Problem Nobody Wants to Admit

Here's what I learned working in product and growth: every SaaS company cares obsessively about competitor pricing. But nobody actually monitors it systematically.

We'd get blindsided by pricing changes. Some competitor would introduce a $15/month tier and we'd only notice when customers mentioned it in support tickets. Or we'd spend weeks building a feature that another tool just bundled into their free plan.

I started a manual process: every Monday, I'd visit 20 competitor pricing pages, screenshot them, write notes. But this approach had massive problems:

• It's not scalable: 20 tools max. What about new competitors? What about tools in adjacent categories?
• It's error-prone: Did I miss a tier change? Are these prices current or 6 months old?
• It's invisible: Only I have this data. It's not shared across product, marketing, finance teams.
• It's reactive: By the time I notice a price change, it's already been live for weeks.
• It has no history: I have no context. Did Notion raise prices 15% this year or 3%? When?

That conversation changed everything. I realized: this isn't a personal productivity problem. It's a team visibility problem. And if we needed this, every other SaaS company probably did too.

The Research Phase: "Is Anyone Else Solving This?"

I spent a week looking for existing solutions. There were product intelligence tools (Paddle, Chainalysis, PricingGauge), but they were either:

• 🔴 Too expensive: $1,000+/month for enterprise plans
• 🔴 Too broad: Focused on everything except pricing (features, customer reviews, usage data)
• 🔴 Too limited: Only 20-30 tools in their database
• 🔴 No history: Only showed current prices, not trends

Nobody was building "track competitor pricing changes automatically and alert me immediately." That's when I started thinking I could build something better.

But here's the key insight: I needed a data advantage. Otherwise, I'd just be building a faster version of my spreadsheet. That's when I had the epiphany that changed everything.

The Epiphany: Archive.org's Hidden Treasure

One Tuesday evening, I was thinking about historical pricing data. Competitors probably had press releases about price changes, but who archives press releases? Then it hit me:

"What if Archive.org has 15+ years of competitor pricing pages?"

I spent 30 minutes researching. The answer: yes. Archive.org has been crawling every major SaaS pricing page since 2006. They have:

• 📸 18 years of snapshots for ChatGPT, Notion, Slack, GitHub, Figma, and 260+ other tools
• 📊 Multiple snapshots per year for most tools (quarterly or monthly)
• 🔓 Completely public and free to access via their API
• ❌ Zero competitors using this data for pricing research

That's when I knew this could be something real. Not just a better spreadsheet. A data moat.

Building the MVP: 3 Weeks of Obsession

I took a week off work and started building. The tech stack was straightforward:

• Frontend: Next.js + React (I know this stack well)
• Database: Supabase (PostgreSQL, no DevOps overhead)
• Crawling: Playwright (automate browser for JavaScript-rendered sites)
• AI: Gemini API (extract structured pricing from HTML)
• Deployment: Vercel (auto-deploy from git, perfect for side projects)

But the technical build was easy. The hard part was data quality.

Extracting pricing from a pricing page sounds simple until you realize:

• Some pages have 3 plans. Some have 15. Some have variations (per-seat, per-month, annual billing).
• Prices in USD, EUR, GBP—sometimes on the same page.
• Billing periods: monthly, annual, quarterly, per-event, per-user-per-month.
• Promotional pricing, volume discounts, "contact for pricing" tiers.
• 60% of sites block basic HTTP requests with bot detection.

I spent the first week just working on data quality. Implementing Playwright with stealth mode, building an AI prompt that understands all these variations, creating validation logic that flags suspicious prices (e.g., $50/month that should be $50/year).

By week 3, I had:

• ✅ Automated daily crawls of 30 tools
• ✅ Archive.org API integration for historical data
• ✅ AI extraction of pricing with 95%+ accuracy
• ✅ Change detection comparing current vs previous price
• ✅ A simple web UI to view results

Why This Matters to Every SaaS Team

Here's what I've learned building this:

Pricing intelligence is a competitive advantage

Teams that know competitor pricing immediately can:

• Adjust positioning: "They lowered their price to $29—should we?" (within hours, not weeks)
• Plan roadmap: "They bundled X into their free tier—how does that affect our strategy?"
• Coach sales: "Here's exactly how your pricing compares at each company size"
• Track trends: "AI tools raised prices 40% this year. Is that sustainable?"
• Negotiate renewals: "I can show you when they dropped prices—here's your negotiation window"

History is more valuable than you think

When I first loaded 18 years of Notion pricing data:

• 2008: Free tier only ($0)
• 2013: Free + Pro ($15/month)
• 2019: Free + Pro + Team ($8-80/user/month)
• 2023: Free + Pro + Team + Enterprise (Free + $10-80/user/month)
• 2025: Free + Plus + Business + Enterprise (Free-$14/user/month or $300+/month flat)

That progression tells you everything: Notion started free, experimented with per-user pricing, then moved to flexible per-team and annual plans. If you're a competitor, you can learn from exactly what worked and what they iterated on.

Nobody else is doing this (yet)

I searched extensively. Price2Spy, PricingMonitor, Competiscan—they all focus on *current* pricing only. None of them have built historical analysis because Archive.org data requires:

• Crawling 260+ tools × multiple snapshots = 2,300+ pricing extractions
• Building robust AI extraction that works across different website designs
• Creating a clean database schema that supports all pricing variations
• Manual QA to validate accuracy

It's 4-6 months of engineering work. That's why nobody's done it. That's why it's valuable.

The Vision: Pricing Transparency for Everyone

Here's what I want to build:

• 🎯 Public pricing database: Anyone can search "How much is Notion?" and get current price + 18-year history
• 🔔 Alerts: Teams get notified immediately when competitors change pricing
• 📊 Analytics: Discover pricing trends (which categories raise prices annually? Which maintain stable pricing?)
• 🔗 API access: Teams integrate real-time pricing data into internal tools
• 📈 Comparisons: "Show me the 5 most similar tools to Slack sorted by price"

The endgame: make pricing as discoverable as product features. Right now, you can read 100 reviews about Slack's features. But finding its exact price history? That should be equally easy.

Why Now? Why Me?

This idea was technically infeasible 3 years ago:

• ❌ LLMs for AI extraction didn't exist (Gemini is 2024, GPT-4 is 2023)
• ❌ Archive.org had less complete data for SaaS (pre-2020)
• ❌ Serverless infrastructure was too expensive for crawling (Vercel, Supabase pricing is new)

Now it's possible. And I'm building it because:

• ✅ I've lived the pain of not knowing competitor pricing
• ✅ I have the technical skills to build it (Next.js, databases, AI)
• ✅ I stumbled onto the Archive.org data advantage
• ✅ I'm patient enough to handle data quality (most founders would've given up)

Start Using SaaS Price Pulse Today

I built this because I needed it. I'm sharing it because I think every team needs it.

If you manage SaaS spend, own pricing strategy, or lead product—you should know immediately when competitors change pricing. Not next quarter. Not next week. Immediately.

The Journey Continues

Building SaaS Price Pulse has already taught me more than I expected. About data quality, about SaaS pricing patterns, about what teams actually need.

The next chapter: Can I help teams make better decisions using historical pricing intelligence? Can I surface patterns nobody else sees?

I don't know yet. But I'm excited to find out.

Last updated: December 22, 2025. This is the origin story of SaaS Price Pulse—written in the early days as we're still building. Check back for updates as we grow.