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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.
finally something that ties the spike back to the actual deploy instead of leaving me guessing at 11pm
Love that you matched costs back to the deploy that likely caused them, that's the kind of detail most cost tools skip and it's exactly what engineering leads actually need on a Monday morning.
One thing I'd love to see is grouping spend by team or cost center automatically, so we can see which squads are driving the spikes without manually tagging everything. That would make the anomaly alerts way more actionable for us.
Would love to see cost data broken down by feature flag or experiment variant, not just by service or deploy. That way we can tell which specific rollout is burning through the OpenAI budget, not just which deploy roughly correlated. Feels like a natural next step for the anomaly detection side.
Would love to see a Slack or Teams notification when a deploy gets flagged as the likely cause of a cost spike so my on-call can jump on it without logging into another dashboard.
the deploy-to-anomaly matching is genuinely clever, most tools just hand you a bill and wish you luck
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
StackSpend was featured in Analytics (172.8k followers), Developer Tools (515.9k followers) and Artificial Intelligence (473.8k followers) on Product Hunt. Together, these topics include over 199k products, making this a competitive space to launch in.
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.
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Andrew Day