Forecast, budget, detect, act, and save on cloud and AI spend.

Problem: Finance, founders, and engineering leaders need to know where the month is heading before cloud or AI usage turns into a closed invoice.

What StackSpend shows: StackSpend projects month-end spend across AWS, GCP, Azure, Snowflake, OpenAI, Anthropic/Claude, Cursor, GitHub, Vercel, ClickHouse Cloud, Hugging Face, Grok (xAI), and Twilio.

What to do next: Review the forecast, identify the provider moving the number, and decide whether the current pace is acceptable before month-end.

Problem: Visibility alone does not create control. Teams need provider and stack-level guardrails before spend drifts beyond the plan.

What StackSpend shows: StackSpend compares current pace and forecast against provider budgets for infrastructure, AI APIs, data warehouses, developer tooling, and communications providers.

What to do next: Set monthly budgets for the biggest or fastest-moving providers first, then tune thresholds as usage patterns become clearer.

Problem: Cost reviews usually happen too late because nobody wants to open every billing portal every morning, so a spike only surfaces on the invoice.

What StackSpend shows: StackSpend learns a statistical baseline per provider and service, then flags anomalies, threshold breaches, and provider movement the same day — surfaced in a daily Slack or email signal showing yesterday, month-to-date, forecast, and budget status. For example, an AWS NAT Gateway spike at $891 against $286 expected is flagged in hours, not on the invoice.

What to do next: Use the daily signal to spot what changed across AWS, GCP, Azure, Snowflake, OpenAI, Anthropic/Claude, Cursor, GitHub, and the rest of the stack, and open the anomaly with enough context to investigate.

Problem: A spike is only useful if the right person can pick it up quickly and decide what to do next — in the tools they already work in.

What StackSpend shows: StackSpend can route an anomaly to Slack and create an issue in Linear or Jira, assigned to the owner with priority set from severity and synced both ways. For the NAT Gateway example, that is a #eng-costs alert and a Linear ticket, ENG-157, to investigate the spike.

What to do next: Investigate the provider, fix the spike, tune the budget, or brief finance — then resolve the issue, and the status mirrors back to StackSpend.

Problem: Savings are easy to claim and hard to prove, so cost work rarely gets credit and recurring waste creeps back.

What StackSpend shows: Resolving an anomaly records the amount caught on the Savings Ledger, and Signal recommends the fix — for the NAT Gateway example, a VPC endpoint for S3 and moving traffic off the gateway, with the caught and projected-avoided amounts kept on the record.

What to do next: Apply the recommended fix, confirm the saving holds, and use the ledger to show finance and the board what cost work actually returned.