Cost Observability

Cost observability and analytics for cloud and AI spend — see, attribute, and explain every dollar in one platform.

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.

Start free trialView setup guide
Read-only access·14-day free trial·No credit card required·Setup in under 5 minutes
See it in action

One score for whether spend is under control.

The product’s Cost Health score and trajectory — coverage, budget pacing, and how fast you respond to spikes, rolled into a single number your team and board can track over time.

StackSpend dashboard
Cost Health
+6 this month
82
Good
Cost Health over time
+6 this month
Last 30 days: 64 → 82
May 1May 6May 11May 16
The challenge

Why this spend is 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.

The product

What StackSpend shows

  • 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 providerAttribution by provider, service, model, region, tag, team, and featureInteractive cost dashboard with week/month/quarter and YoY comparisonAnomaly detection with cited cause attributionPace-to-forecast and budgets across the full stack90 days of history, normalized into one analytics model
Failure modes

Common cost triggers

Real scenarios that cause spend to spike — often silently.

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

Native tools vs StackSpend

Provider billing dashboards + manual spreadsheets

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
ICP

Who this is 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.

From day one

What 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.

Compare alternatives

All StackSpend comparisonsStackSpend vs VantageStackSpend vs CloudZeroStackSpend vs Langfuse
Questions

Frequently asked

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.

Start seeing your full stack spend.

Connect cost observability in under 5 minutes. 90 days of history loaded automatically. Daily signals from day one.

Start free trialView setup guide
14-day free trial · No credit card required · Read-only access
AI & Cloud Cost Observability Platform — StackSpend