AI Cost Tracking
AI cost tracking across every LLM provider
Token-level cost capture for every AI API call, priced in real time across OpenAI, Anthropic, Gemini, and 15+ providers. Track LLM costs per team, per feature, and per customer, with near-zero latency overhead and no code rewrite.
One-line integration
Start tracking in minutes, not sprints
No SDK wrapper, no agent, no billing export parsing. Point your existing SDK at the AI SpendOps gateway and every request is captured with full token detail. You keep your own provider keys and the official SDKs.
- Swap the base URL to proxy.aispendops.com
- Add your X-ASO-API-Key header
- Tag requests by team, feature, or customer
Every token type, priced correctly
Providers report usage differently: Anthropic counts cache tokens separately, OpenAI includes them in prompt tokens. AI SpendOps normalises each provider’s semantics so your costs are right, not roughly right.
Input priced per model, per provider
Output priced per model, per provider
Cache reads and writes priced at each provider's cache rates
Thinking tokens from reasoning models, split out
Multimodal usage captured per modality
Tool-use charges counted per request
From cost tracking to cost control
Cost per request, in real time
Every API call is priced as it happens using per-model rates, including cache and reasoning tokens, so your numbers match the provider invoice at month-end.
LLM cost tracking per team and customer
Tag requests with team, feature, environment, or customer dimensions and see exactly where every dollar of AI spend goes, down to a single feature or account.
AI cost monitoring and alerts
Budgets per team, burn alerts at configurable thresholds, and anomaly visibility so a runaway prompt or misconfigured retry loop is caught in hours, not at invoice time.
Near-zero latency overhead
Usage capture runs asynchronously after the response is sent, off the response path, so cost tracking never slows your production traffic.
Frequently asked questions
How do I track AI and LLM token costs?
The most reliable way is to capture usage in the request path rather than reconstructing it from invoices. AI SpendOps is a gateway you point your existing SDK at with one base URL change. It records prompt, completion, cached, and reasoning tokens for every request and prices them with per-model rates across 15+ providers, so cost is known per request rather than per month.
Can I monitor AI costs in real time?
Yes. Because AI SpendOps sits in the request path, every call is captured and priced as it happens. Dashboards, budgets, and burn alerts work on live data, so you see a cost spike the hour it starts instead of when the invoice arrives.
How do I track LLM costs per customer or per feature?
Tag each request with custom dimensions such as customer, feature, team, or environment via headers. AI SpendOps aggregates cost by any of these dimensions, which gives you per-customer unit economics and per-feature cost without any changes to your application logic.
Does AI cost tracking add latency to my requests?
With AI SpendOps, effectively no. The proxy streams responses straight through and does all usage extraction and pricing asynchronously after the response has been sent, so the overhead on the response path is near zero.
Which AI providers can I track costs for?
OpenAI, Anthropic, Google (Gemini), xAI, Groq, Mistral, DeepSeek, OpenRouter, Perplexity, Fireworks, DeepInfra, Cerebras, Novita, and Nebius, all through one gateway with provider-correct pricing, including each provider's cache token semantics.
Know your AI costs to the token
Real-time AI cost tracking across 15+ providers with near-zero overhead. First 3 months free.
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