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StackSpend
Real Time Cost Control for the Modern AI Stack
Cost management for the modern AI engineering stack and the cloud it runs on. Same-day anomaly alerts matched to the deploy that likely caused them, forecasts, and a daily Slack digest. track → forecast → budget → detect → save
I didn't set out to build a cost tool. I built StackSpend because our own AI bills kept ambushing us.
Hey Product Hunt 👋 I'm Andrew. We're a growing AI company, and over the last year our spend got away from us in ways nobody caught until the invoice landed:
• A release went out with a bad change and our OpenAI cost spiked overnight.
• A Google Cloud account got compromised — someone hammered the Gemini API and ran up ~$9,000 before we noticed.
• Google deprecated a Gemini Flash Lite model and migrated our workload onto the newer Flash — about 3x the price for the same calls.
• Our engineers adopted Cursor fast. Great for velocity — but some were quietly burning hundreds of dollars a day, each.
Every one of these was invisible until the monthly invoice. By then the money was gone and I was reverse-engineering what happened weeks after the fact.
We didn't need another dashboard. We needed to track spend across the whole stack, forecast and budget for what our teams would actually burn, and catch anomalies the day they happen — tied to the change that caused them, not discovered a month later.
So we built that:
📊 Track every dollar across the AI stack and the cloud it runs on — read-only connect in ~5 min, 90 days backfilled, 14+ providers
📈 Forecast month-end and set budgets, with days-to-risk warnings before you breach
🚨 Catch anomalies same-day — matched to the deploy that likely caused them. The AI reads the diff and points at the suspect change; we flag it as likely and let an engineer confirm. We never assert the cause.
🎯 Spikes become assigned tickets in Linear/Jira — owned, not watched
🤖 Or just ask Signal: "why did we spike on Tuesday?" — answers with the numbers cited
Flat pricing from $29/mo, never a % of your bill. Full 14-day trial, no card.
Ask me anything — including the messy bits: what we still don't cover, how we avoid blaming the wrong PR, and what that $9k lesson actually cost us.
About StackSpend on Product Hunt
“Real Time Cost Control for the Modern AI Stack”
StackSpend was submitted on Product Hunt and earned 10 upvotes and 12 comments, placing #91 on the daily leaderboard. Cost management for the modern AI engineering stack and the cloud it runs on. Same-day anomaly alerts matched to the deploy that likely caused them, forecasts, and a daily Slack digest. track → forecast → budget → detect → save
On the analytics side, StackSpend competes within Analytics, Developer Tools and Artificial Intelligence — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how StackSpend performed against the three products that launched closest to it on the same day.
Who hunted StackSpend?
StackSpend was hunted by Andrew Day. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of StackSpend including community comment highlights and product details, visit the product overview.
Andrew Day