How to ship an early warning system for SaaS dependency collapse in a weekend.
If you were using Heroku in November 2022, you remember the feeling. A Reddit thread titled 'Did Heroku just die?' hit 552 upvotes. Engineers were frantically migrating apps with zero warning. The signals were there months earlier — support degradation, job postings drying up, changelog activity dropping off a cliff — but nobody was watching.
That's the gap this idea tries to close.
The core product is a Vendor Health Score dashboard. You plug in your stack (Vercel, Twilio, PlanetScale, whatever), and the tool aggregates public signals to give each vendor a composite health score with a 30/60/90-day trend. When a vendor's score drops sharply, you get a Slack ping before it becomes a crisis.
The signals you're monitoring:
Here's the validation test I'd run first, straight from the idea data: build a Notion template or Google Sheet that manually tracks these 6 signals for 10 common vendors. Post it free on r/devops and r/sre. Include a simple CTA at the bottom: 'We're building an automated version. Would you pay $49/mo?'
Simultaneously, find the top 20 commenters from that Heroku Reddit thread. They already proved the pain. DM them directly.
Your success threshold is concrete: 5 people agree to pay before the product exists, or 3 discovery calls where someone says they'd cancel an existing tool to use this. If you can't hit that with a spreadsheet, a coded product won't change the outcome.
I'd give yourself one week for this. If the spreadsheet gets traction, build. If it doesn't, you've lost a week, not a month.
The idea data suggests Next.js + Supabase + Vercel + Inngest for background jobs + Slack API, and that's a reasonable call. Inngest in particular is worth the choice here because you need reliable scheduled jobs for polling vendor signals, and building a custom queue with retries is a weekend-killer.
Here's what the MVP actually ships:
Vendor inventory. Manual entry plus CSV upload. Auto-enrichment pulls GitHub stats, last changelog date, and public job posting counts. Don't over-engineer this. A simple table with 10 fields is enough.
Health Score dashboard. One composite score per vendor, sparklines for 30/60/90-day trend, and the top 3 flagged signals listed underneath each vendor card. This is the aha moment: a user sees a tool already in their stack showing a declining score with specific, named reasons. That's when it clicks.
Slack alerts. Fire when a vendor's score drops by more than X% week-over-week. Include a digest mode for teams who don't want per-event pings. Alert fatigue is your biggest early retention risk, so start with conservative thresholds. Only alert on multi-signal confluence, not single data points.
That's genuinely it for week one. No API access tier, no migration runbooks, no Backstage integration. Ship the thing that proves the core value: 'I saw a real signal about a real vendor in my actual stack.'
I want to be honest about the hard part here, because pretending it doesn't exist would be doing you a disservice.
Three of your six core signal sources have structural fragility. LinkedIn aggressively blocks scrapers (there's active litigation around this). GitHub rate-limits meaningful historical queries at the free tier. Reddit's 2023 API pricing changes killed dozens of sentiment-dependent products.
If two or three of these break simultaneously — which isn't paranoid thinking given current API monetization trends — your health score model degrades badly.
The way to handle this: abstract every data source behind internal adapters from day one. When a source goes down, scores show 'partial data' with a confidence indicator rather than breaking entirely. You're also prioritizing sources with stable official APIs (GitHub has an official API, use it properly; LinkedIn Jobs API exists and is less legally fraught than scraping). Build degradation gracefully rather than building for the happy path.
This isn't a reason not to build. It's just architecture discipline you need from the start.
Three tiers:
At $99/mo average and 5% monthly churn, you're looking at roughly $1,900 LTV per customer. Break-even is 18 customers — that's achievable in the first 60 days if validation goes well.
The channels that actually work here are organic developer communities (r/devops, r/sre, Hacker News) and targeted LinkedIn outreach to platform engineers and engineering managers at Series A-B startups that raised in the last 18 months. The Crunchbase free tier gets you those company lists. Reference the Heroku pain specifically in cold DMs — it's not generic, and people who lived through it will respond.
Two real concerns beyond the data sourcing issue.
First: this is insurance, and people are bad at buying insurance when they feel safe. The Heroku incident was over two years ago. Engineers have stabilized on new platforms. The urgency fades. You'll sell best immediately after the next high-profile deprecation event, and you have no control over when that happens.
Second: most startups use the same 30-40 vendors (AWS, Stripe, Twilio, Vercel), and those vendors are extremely unlikely to pull a Heroku. The genuinely risky vendors — niche databases, single-founder open-source tools, recently acquired SaaS products — are harder to instrument. There's a real paradox where the vendors most worth monitoring are the hardest to monitor well.
Neither of these is fatal. But they shape your go-to-market. The AI API infrastructure space (Replicate, Together AI, various fine-tuning platforms) is where the genuine volatility lives right now, and those companies are small enough that your signals would actually catch something. That might be your beachhead.
The product that exists in month one is a nice dashboard. The product that exists in month 18 is something different: a proprietary dataset of historical vendor behavior patterns across 500+ vendors, labeled by what actually happened. You can build a deprecation probability model on that data that no new entrant can replicate without the same time investment.
Workflow integrations (Backstage plugin, PagerDuty webhook) add switching costs before a well-funded competitor can reposition into this space. Vanta has the budget and the buyer relationship to add operational signal monitoring as a feature. You need to own the engineering team workflow before that happens.
The move is to build fast, get integrations into developers' existing tools, and let the dataset compound. That's the actual defensible position here, not the dashboard itself.