One bill of materials for your GenAI stack.
LLM, image and embedding workloads in one place, priced the day they run.
- 5 min
- setup, per provider
- 90 days
- history, instantly
- Same-day
- anomaly alerts
- Read-only access
- 14-day free trial
- No credit card required
Daily Spend by Provider
$24,321 total
What is StackSpend for Generative AI Cost Management?
StackSpend manages generative AI costs across OpenAI, Anthropic, Claude, Cursor, Hugging Face, and Grok — including chat, embedding, and inference workloads. Get one combined view, model-level breakdown, daily signals, anomaly detection, and pace-to-forecast so GenAI spend is controlled as it scales.
Why is this spend hard to control?
01
Generative AI workloads scale unpredictably — a single launch can multiply inference spend overnight.
02
GenAI cost spans many providers and workload types (chat, embeddings, image, fine-tuning) with no shared view.
03
Finance and product cannot tie generative AI cost back to the features and customers driving it.
What does StackSpend show?
Every model you run, in one lens.
Usage by base model, project and user, in tokens and in API-equivalent value. Estimated and billed usage stay separate, so the numbers never double-count and never pretend to be your invoice.
Business plan
How it worksSwitch to a cheaper model that scores as well.
StackSpend checks the models you run against a priced, benchmarked catalogue every day. When a cheaper one scores as well, you get the swap, the evidence, and the monthly saving at your real token mix.
Business plan
How it worksSee this running against your own bill by tomorrow morning.
Read-only · 5 minutes per provider
Who is this for?
- Product and engineering teams that need model-level visibility before AI bills surprise them.
- Buyers consolidating OpenAI, Anthropic, Claude, Cursor, or open-model spend into one operating view.
- Teams that need alerts and forecasting, not just retrospective usage dashboards.
What's included?
- StackSpend consolidates generative AI spend across providers and workload types into one normalized dashboard.
- Model-level and workload-level breakdown shows exactly which GenAI use case is driving cost.
- Daily signals, anomaly detection, and forecasting keep generative AI spend controlled as adoption grows.
What we track
- LLM, embedding, and inference spend across providers
- Cost by provider, model, and workload
- Daily signals and anomaly alerts
- Budgets and pace-to-forecast
- 90 days of history
What are the most common cost triggers?
- A generative feature launches and inference spend multiplies overnight
- An embeddings pipeline reprocesses the full corpus on every change
- Image or fine-tuning workloads scale without a budget
- GenAI cost crosses a threshold with no forecast warning
Why teams outgrow the native billing consoles
Native tools are built for investigation. StackSpend is built for prevention.
Per-provider GenAI usage dashboards
- No combined view across GenAI providers and workload types
- Retrospective reporting, not same-day alerts
- No allocation to features or customers
- No forecast against a GenAI budget
StackSpend
- One view of generative AI spend across providers and workloads
- Workload- and model-level cost breakdown
- Anomaly detection and forecasting for GenAI usage
- Daily signals so launches do not become invoice surprises
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.
Generative AI Cost Management, answered
How do I manage generative AI costs across providers?
Connect your generative-AI providers to StackSpend and it consolidates LLM, embedding, and inference spend across OpenAI, Anthropic, Claude, Cursor, Hugging Face, and Grok into one normalized view. A model- and workload-level breakdown shows which GenAI use case drives cost, while daily signals, anomaly detection, and pace-to-forecast keep spend controlled as generative-AI adoption scales.
What is generative AI cost management?
Generative AI cost management is the practice of tracking and controlling spend across generative workloads — chat, embeddings, image, inference, and fine-tuning — that scale unpredictably and span many providers. It unifies those costs into one view, breaks them down by model and workload, and adds budgets, anomaly alerts, and forecasting so a single launch cannot multiply inference spend without warning.
How do I tie generative AI cost to features and customers?
StackSpend attributes GenAI spend by provider, model, and workload, then lets you tag it to a feature, product, environment, or customer. That connects generative-AI cost back to what drives it, so product and finance can see cost-per-feature and cost-per-customer for chat, embedding, and inference workloads instead of reading one undifferentiated provider total.
How do I stop a generative AI launch from becoming an invoice surprise?
StackSpend backfills 90 days of history on connect and sends a daily green/amber/red signal, so a launch that multiplies inference spend shows up the same day rather than at month-end. Statistical anomaly detection tuned to bursty AI bills names the likely driver, and pace-to-forecast projects where the month lands against your GenAI budget while there is still time to react.
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.