AI Spend Management
AI spend management for teams building with LLMs
Track AI costs across OpenAI, Anthropic, Gemini, and 15+ providers, attribute every token to a team, feature, or customer, and keep spend inside budget with call-time governance. One low-latency gateway, no code rewrite.
What is AI spend management?
AI spend management is the practice of tracking, attributing, and controlling what your organisation spends on AI services, in particular LLM APIs. It answers three questions: what did we spend and on which models, who or what drove that spend, and how do we keep it within budget. It applies FinOps discipline to a cost that is measured in tokens, changes price monthly, and can double overnight with a single prompt change.
Why AI spend gets out of control
One invoice, zero answers
Provider bills land as a single monthly number. When finance asks which team, feature, or customer drove the increase, engineering has to guess.
Costs move without warning
New models ship monthly, prices change, and a single prompt rewrite can double token consumption. Without per-model tracking, forecasting AI spend is a coin toss.
No guardrails on usage
Anyone with an API key can call any model at any price point. Shadow AI usage spreads across teams and only shows up when the bill does.
What an AI spend management platform gives you
Everything finance and engineering need to see, attribute, and control AI costs, in one place.
Token-level cost visibility
Every request captured with prompt, completion, cached, and reasoning tokens priced per model, so your dashboard matches the invoice.
Attribution by team and feature
Tag requests with custom dimensions: team, feature, environment, customer. Turn one lump-sum bill into a cost breakdown finance can act on.
Budgets and burn alerts
Set monthly budgets per team or cost centre and get alerts at configurable burn thresholds, before month-end instead of after.
Model and provider governance
Allow-list which models and providers each API key can call. Block expensive or unapproved models at the edge, not in a policy document.
Chargeback and showback
Export cost allocation by department or customer in finance-ready formats, aligned with FinOps practices your finance team already knows.
Audit trail
Every request, policy decision, and key change logged and exportable for compliance and internal audit.
Live in an afternoon
Route through the gateway
Point your existing OpenAI, Anthropic, or other SDK at proxy.aispendops.com. One base URL change, no SDK wrapper, no rewrite.
Tag your requests
Add headers for team, feature, environment, or customer. Untagged requests can be rejected at the edge if you want full coverage.
See and control spend
Dashboards, budgets, alerts, and exports go live immediately. Usage capture runs off the response path, so latency stays near zero.
Your AI spend management checklist
- Every AI API call captured with token counts and cost
- Spend attributed by team, feature, environment, and customer
- Budgets per team with burn alerts before month-end
- Model and provider allow-lists enforced at call time
- Chargeback exports finance can drop into their reporting
- Audit trail of every request and policy decision
Frequently asked questions
What is AI spend management?
AI spend management is the practice of tracking, attributing, and controlling what an organisation spends on AI services such as LLM APIs. It covers cost visibility (what was spent, on which models and providers), attribution (which teams, features, or customers drove the spend), and governance (budgets, alerts, and policies that keep usage within agreed limits). It applies FinOps principles to AI usage, where costs are driven by tokens rather than instances.
What FinOps platform handles AI spend?
AI SpendOps is a FinOps platform built specifically for AI spend. Unlike general cloud cost tools that read billing exports after the fact, it sits in the request path as a low-latency gateway, capturing token-level usage across 15+ LLM providers in real time and enforcing budgets and model policies at call time.
How do I track AI costs across multiple providers?
Route your AI API traffic through a gateway that normalises usage across providers. With AI SpendOps you keep your own provider keys and official SDKs, change one base URL, and get a unified view of OpenAI, Anthropic, Google, xAI, Groq, Mistral, DeepSeek, OpenRouter and more, with each provider's token types priced correctly, including cached and reasoning tokens.
How is AI spend management different from cloud cost management?
Cloud cost management works from monthly billing data on infrastructure units such as instances and storage. AI spend is driven by tokens, prices vary per model and change often, and a single code change can multiply costs overnight. AI spend management therefore needs per-request capture, per-model pricing, and call-time controls rather than end-of-month billing analysis.
Can I set budgets and limits on AI spend?
Yes. AI SpendOps lets you set monthly budgets per team or cost centre with burn alerts at configurable thresholds, and enforce model and provider allow-lists per API key so overruns are prevented rather than just reported.
Take control of your AI spend
Token-level visibility, budgets, and governance across 15+ providers. First 3 months free.
Sign Up