AI Spend Management
AI spend management for LLM and API costs
For teams building with LLMs, not for procurement spend analysis. Track token costs across OpenAI, Anthropic, Gemini, and 16+ 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. Because most of that cost is LLM API usage, you will also hear it called LLM spend management or GPT spend management: the same discipline, applied to token-based API bills.
AI spend management, not procurement spend analysis
These two share a vocabulary and almost nothing else. If you arrived looking for supplier or tail spend analysis, a procurement suite is what you want.
Procurement spend analysis
AI is the tool. Models classify supplier invoices, consolidate vendors, and surface tail spend across everything an organisation buys. The unit of cost is a purchase order.
AI spend management (this page)
AI is the cost. We measure what you pay OpenAI, Anthropic, Google and others for token-based API usage, per request and per model. The unit of cost is a token.
Why LLM API spend gets out of control
One invoice, zero answers
Your OpenAI and Anthropic bills land as a single monthly number. When finance asks which team, feature, or customer drove the increase, engineering has to guess.
Token costs move without warning
New models ship monthly, per-token prices change, and a single prompt rewrite can double consumption. Without per-model tracking, forecasting LLM API spend is a coin toss.
No guardrails on API 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 LLM API costs, in one place.
Token-level API cost visibility
Every LLM API request captured with prompt, completion, cached, and reasoning tokens priced per model, so your dashboard matches the provider invoice.
Attribution by team and feature
Tag API calls with custom dimensions: team, feature, environment, customer. Turn one lump-sum provider 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 gateway, not in a policy document.
Chargeback and showback
Apportion token costs by department or customer in finance-ready views, aligned with FinOps practices your finance team already knows.
Audit trail
Every API request, policy decision, and key change logged and kept 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 API calls
Add headers for team, feature, environment, or customer. Untagged requests can be rejected at the gateway if you want full coverage.
See and control token spend
Dashboards, budgets, and burn alerts go live immediately. Usage capture runs off the response path, so latency stays near zero.
Your AI spend management checklist
- Every LLM API call captured with token counts and cost
- Token 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 figures finance can drop into their reporting
- Audit trail of every API 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.
Is this the same as AI spend analysis in procurement?
No, and the two get confused because they share a vocabulary. Procurement spend analysis uses AI to classify supplier invoices, consolidate vendors, and tackle tail spend across an organisation's purchasing. AI SpendOps does the opposite: AI is the thing being spent on, not the tool doing the analysis. We measure what you pay providers such as OpenAI and Anthropic for token-based API usage, per request and per model. If you are looking to categorise supplier or indirect spend, a procurement suite is the right tool. If your AI provider bill is the line item you cannot explain, this is the right tool.
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 16+ LLM providers in real time, tracking and alerting on budgets, and enforcing 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, so you hear about an overrun while there is still time to act rather than at month-end. Budgets are tracked and alerted on rather than used to block traffic. Model and provider allow-lists are enforced per API key at call time, so unapproved or expensive models are blocked outright.
What is LLM spend management?
LLM spend management is AI spend management applied specifically to large language model APIs: tracking token usage and cost per request, attributing it to teams, features, or customers, tracking budgets, and enforcing model policies. AI SpendOps is built for exactly this, covering OpenAI, Anthropic, Google, and 16+ other LLM providers through one gateway.
Can I manage GPT and OpenAI API spend?
Yes. Point the official OpenAI SDK at the AI SpendOps gateway with a one-line base URL change and every GPT API call is captured with token counts, priced per model, attributed to a team or feature, and governed by budgets and model allow-lists. Note this covers GPT API spend, not ChatGPT seat subscriptions, which are a licence cost rather than usage-based API spend.
Take control of your AI spend
Token-level visibility, budgets, and governance across 16+ providers. First 3 months free.
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