Table of contents
Unified cost dashboard
See all cloud and AI spend across every connected provider in one view, updated daily.
Cost Explorer
Slice and drill into spend by provider, service, project, or team to find where money goes.
Cost anomaly detection
Automatically flags unusual spikes the moment they happen โ not at month-end.
Cost-to-code correlation
Ties each anomaly back to the pull request, deploy, or config change that caused it.
Anomaly routing & ownership
Routes each anomaly to the team or person who owns it, with a workflow to resolve it.
Cost forecasting
Projects month-end spend against budget so you catch overrun before it happens.
Daily cost reports
Delivers a daily spend summary to email and Slack for whole-team visibility.
Source-control integration
Connects GitHub to power cost-to-code correlation and change attribution.
Jira & Linear integration
Links cost anomalies to issue trackers so fixes get assigned and tracked.
REST API
Programmatic access to your cost data for custom reporting and automation.
Legacy FinOps tools were built for the AWS-only era: they stop at a chart, bill you a percentage of your cloud spend, and can't explain what changed. StackSpend is different on three fronts โ it explains the cause of every spike (cost-to-code correlation), it covers AI/LLM spend as a first-class citizen alongside cloud, and it uses flat, predictable per-tier pricing so your cost-management bill never grows just because your cloud bill did. Setup takes minutes, and a 14-day free trial doubles as a free cost-health audit.
StackSpend traces every dollar of cloud and AI spend back to the code, team, and pull request that caused it. Where traditional cost tools show you that spend moved, StackSpend's cost-to-code correlation shows you why โ automatically tying each anomaly to the deploy, config change, or PR behind it. It unifies traditional cloud (AWS, Azure, GCP, Snowflake) and modern AI spend (OpenAI, Anthropic, Cursor) in one view, detects anomalies daily instead of at month-end, and works from day one without a data team building dashboards.
StackSpend traces every dollar of cloud and AI spend back to the code, team, and pull request that caused it. Where traditional cost tools show you that spend moved, StackSpend's cost-to-code correlation shows you why โ automatically tying each anomaly to the deploy, config change, or PR behind it. It unifies traditional cloud (AWS, Azure, GCP, Snowflake) and modern AI spend (OpenAI, Anthropic, Cursor) in one view, detects anomalies daily instead of at month-end, and works from day one without a data team building dashboards.
Engineering and finance teams who share responsibility for cloud and AI spend โ platform/DevOps engineers, engineering leaders, and FinOps or finance practitioners. It's built for teams running a mix of cloud infrastructure and AI/LLM services who need daily visibility and a shared source of truth, from fast-moving startups through mid-market and enterprise organizations.
StackSpend was built by engineers who spent years watching cloud bills climb โ and then watching AI make them climb faster. Founder Andrew Day spent a decade building large-scale systems in regulated banking, where every dollar of infrastructure was accounted for, then eight years in AI startups where teams spent across OpenAI, Anthropic, Cursor, and a dozen cloud services with no way to say why the bill jumped. The cause was almost always a code change โ a PR that flipped a model or widened a query โ but finance dashboards never connected spend to the code behind it. So StackSpend was built to close that gap and turn a monthly surprise into a daily signal.
StackSpend is a TypeScript monorepo (Turborepo). The web app is built with Next.js 15, React 19, and Tailwind CSS, deployed on Vercel. The backend API is a Node.js/Express service on Railway, with Supabase (PostgreSQL) for data and auth. Cost forecasting is powered by a Python FastAPI service using Prophet, pandas, and NumPy. AI/LLM features run through a dedicated agents service (Anthropic Claude), and the platform ingests cost data via native provider APIs and the open FOCUS standard.
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