Track LLM costs
by feature and customer.
See token usage, request cost, and margin drivers across OpenAI, Anthropic, Gemini, Amazon Bedrock, and more—in one dashboard.
No credit card · Setup in minutes · TypeScript, Python, .NET, and Java SDKs
Cost intelligenceOverview
Cost by workflowApr 01 — Apr 30
Cost signal detectedinitial_draft drives 79.3%
Example workspace184,293 requests
Multi-provider LLM cost tracking
OpenAIAnthropicGeminiAmazon Bedrock
Provider dashboards stop at totals
Your AI bill shows what you spent.
Not what caused it.
Model and project totals cannot show which feature, workflow, or customer is eroding margin.
Turn usage into product economics
Connect every token to business context.
Attach your existing metadata, then filter LLM costs by feature, workflow, customer, project, or campaign.
- Request-level token usage and cost
- Provider-aware pricing when each event arrives
- Custom dimensions that match your product
Live cost signals
02
AI cost monitoring in real time
Find cost drift
before invoice day.
Set thresholds by project or model. See which request and workflow caused a spike while your team can still respond.
03
LLM cost tracking without guesswork
Turn model usage into
decisions your team can act on.
Track, attribute, investigate, and alert on AI spend from one request-level source of truth.
Catch cost spikes before month-end
Set spend or usage thresholds by project and model. Alert your team while there is still time to act.
Explain every dollar
Trace each event by provider, model, status, token usage, cost, and attached business context.
Measure feature and customer margin
Group AI spend by feature, workflow, customer, campaign, or any dimension your product understands.
Compare providers without cleanup
Normalize token usage across OpenAI, Anthropic, Gemini, Amazon Bedrock, and mixed provider stacks.
Keep historical costs explainable
Tie each request to pricing used at ingest, so past costs stay clear after model prices change.
Add tracking without a rewrite
Use SDK wrappers or HTTP interception without changing your existing provider call path.
Keep your provider stack
Add token usage analytics.
Do not rebuild your AI calls.
SDK wrappers and HTTP interception capture usage across your existing stack and send normalized cost events to one dashboard.
View integration guidesTypeScript · Python · .NET · Java
- 500
- free requests each month
- 4
- SDK languages
TypeScriptPython.NET
01import { AiTokenTracker } from "@ai-token-tracker/sdk";0203const tracker = new AiTokenTracker({04 authToken: process.env.AI_TOKEN_TRACKER_KEY,05 enableAutoInterception: true,06});0708tracker.addCustomFilters({09 Feature: "initial_draft",10 CustomerId: "cust_019",11});
Event ingestedgpt-5.6-sol · $0.0032
Metadata attachedFeature: initial_draft
Shared AI spend attribution
Give every team
the cost answer it needs.
Engineering
Find expensive requests by model, status, and metadata without reconstructing provider logs.
Request-levelcost receipts
Product
Compare feature and customer economics before usage growth turns into margin loss.
Any fieldas a cost dimension
Finance
Forecast AI spend and explain changes with shared request-level usage data.
Real timebudget signals
See your first cost event in minutes
Stop guessing where
your AI budget goes.
Track 500 LLM requests every month for free. No credit card required.