Know what changed, and why.
One analytics layer over cloud and AI spend, so answering why is this up takes a minute instead of an afternoon.
- 5 min
- setup, per provider
- 90 days
- history, instantly
- Same-day
- anomaly alerts
- Read-only access
- 14-day free trial
- No credit card required
Cost Health
▲ 6 this month82
Good
Cost Health over time
Last 30 days: 64 → 82
What is StackSpend for Cost Observability?
StackSpend is an AI and cloud cost observability platform. It unifies spend from every cloud and AI provider into one analytics layer, then attributes cost by provider, service, model, team, and feature — so you can see what changed, why it changed, and what it will cost by month-end. Cost observability adds the analysis, anomaly detection, and forecasting that raw billing dashboards leave out.
Why is this spend hard to control?
01
Billing portals show numbers, not observability. You can read today's total in Cost Explorer or the OpenAI usage page, but you cannot see how spend is trending, what drove a change, or where the month will land — across providers, in one place.
02
Cost data is fragmented across clouds, AI APIs, and dev tools, so there is no single analytics layer to query. Answering "what is our AI cost per feature this quarter?" means exporting CSVs and building a spreadsheet.
03
Without an observability layer, cost is only ever explained after the invoice. There is no live dashboard that ties a spend change back to the service, model, or deploy that caused it.
What does StackSpend show?
Answer a spend question without a spreadsheet.
Filter and group by any dimension over any range, drill down through a breadcrumb trail, and export exactly the slice you queried. The investigation that used to be a spreadsheet afternoon becomes a few clicks.
How it worksEvery dollar has an owner.
Auto-tagging rules label costs as they are ingested, matching provider, account, service and project patterns in priority order. By the time someone asks who owns the spend, the answer is already on the data — filterable and groupable in the explorer.
Team plan and above
How it worksSee this running against your own bill by tomorrow morning.
Read-only · 5 minutes per provider
Who is this for?
- Teams that want daily visibility into spend without manually checking billing portals.
- Buyers replacing spreadsheets and fragmented native dashboards with one monitoring workflow.
- Operators who need read-only setup, alerts, and forecasting before overrun becomes month-end reality.
What's included?
- StackSpend builds one cost-observability layer across AWS, GCP, Azure, Snowflake, Vercel, ClickHouse Cloud, OpenAI, Anthropic, Claude, Cursor, GitHub, Hugging Face, Grok (xAI), and Twilio — normalized into a single analytics model.
- Attribute every dollar by provider, service, model, region, tag, team, and feature, and slice it on an interactive cost dashboard. Compare week-over-week, month-over-month, and year-over-year without exporting anything.
- Anomaly detection explains what changed and pace-to-forecast shows where the month lands — turning a static dashboard into a live observability signal in Slack, Teams, or email.
- For teams searching for an AI cost analytics platform or cloud cost observability, this is the layer that sits above billing exports and makes spend queryable, explainable, and forecastable.
What we track
- Unified spend across every connected cloud and AI provider
- Attribution by provider, service, model, region, tag, team, and feature
- Interactive cost dashboard with week/month/quarter and YoY comparison
- Anomaly detection with cited cause attribution
- Pace-to-forecast and budgets across the full stack
- 90 days of history, normalized into one analytics model
What are the most common cost triggers?
- A spend change appears and no dashboard can tie it back to the service, model, or deploy that caused it
- The board asks for AI cost per feature or per customer and it takes a day of spreadsheet work to answer
- Multi-cloud and multi-AI spend can only be compared by exporting CSVs from each portal
- A model-mix shift quietly changes unit economics with no analytics layer to surface it
Why teams outgrow the native billing consoles
Native tools are built for investigation. StackSpend is built for prevention.
Provider billing dashboards + manual spreadsheets
- Numbers without analytics — no trend, attribution, or forecast across providers
- No single queryable layer; cross-provider analysis means exporting CSVs
- No anomaly detection that explains what changed and why
- Retrospective only — nothing ties a spend change to its cause in real time
StackSpend
- One cost-observability layer across every cloud and AI provider
- Attribution by provider, service, model, team, and feature on an interactive dashboard
- Anomaly detection with cited cause attribution
- Pace-to-forecast so spend is explainable before the invoice, not after
Native tools show you last month. StackSpend tells you tomorrow.
Connect read-only in about five minutes. 90 days of history loads automatically, and the first daily signal arrives tomorrow morning.
Read-only access · Flat plans, never a % of your bill · No credit card required
What do you get when you connect?
- Setup time
- Most teams can connect and validate setup in about 5-10 minutes.
- Access model
- Read-only credentials only. StackSpend does not modify provider resources or billing settings.
- Signals
- Daily Slack or email updates, anomaly alerts, and budget tracking in one workflow.
- History and forecast
- Historical spend context plus pace-to-forecast so overruns are visible before month-end.
Cost Observability, answered
How do I see what changed in our spend and why, across every provider?
StackSpend unifies spend from every cloud and AI provider into one analytics layer, then attributes cost by provider, service, model, team, and feature — so you can see what changed, why it changed, and where the month will land. Cost observability adds the trend analysis, anomaly detection, and forecasting that raw billing portals leave out, so "what drove this?" is answerable in seconds rather than by exporting CSVs.
What is AI cost observability?
AI cost observability is the practice of unifying AI spend from every provider into one analytics layer so you can see what changed, why it changed, and what it will cost — not just read a total. StackSpend attributes AI cost by provider, model, team, and feature, detects anomalies with cited causes, and forecasts month-end spend, going beyond what a billing dashboard shows.
How is cost observability different from cost monitoring?
Cost monitoring tells you the number and alerts when it moves; cost observability adds the analytics layer that explains it — attribution by service, model, team, and feature, ad-hoc querying, trend comparison, and forecasting. StackSpend does both: daily monitoring signals plus an observability and analytics layer above them.
What is an AI cost analytics platform?
An AI cost analytics platform normalizes spend from every AI and cloud provider into one queryable model so cost can be sliced by provider, model, region, tag, team, and feature and compared across periods. StackSpend is an AI cost analytics platform that covers generative-AI and cloud spend together, with anomaly detection and pace-to-forecast built in.
Does StackSpend provide an AI cost dashboard?
Yes. StackSpend provides an interactive AI cost dashboard that shows total spend and breakdowns by provider, service, model, team, and feature, with week-over-week, month-over-month, and year-over-year comparison — plus a daily green/amber/red signal in Slack, Teams, or email.
Does it cover cloud cost observability too?
Yes. The same observability layer covers cloud cost observability across AWS, GCP, Azure, Snowflake, Vercel, and ClickHouse Cloud alongside AI providers — so cloud and AI spend are attributed, queried, and forecast in one platform rather than two separate tools.
Tomorrow morning: one number, in Slack.
Connect read-only today. 90 days of history loads automatically, and the first daily signal arrives with breakfast — green means nobody has to think about cost at all.