Deploying an LLM Token Cost Tracking Proxy in Production

Monitoring LLM costs can be difficult. Standard logs only show request count, which does not tell you how many tokens were actually consumed. To gain visibility into your spending, you need an llm token cost tracking proxy that parses stream contents and logs financial telemetry.

For a complete look at cost-efficient architectures, read The Complete Architecture of Cost-Efficient LLM Pipelines.

Why Traditional Proxies Fail

Standard proxies (like Nginx) cannot parse Server-Sent Events (SSE) dynamically. To track costs, a proxy must:

1. Parse incoming prompts to calculate input tokens.
2. Decode outgoing streaming chunks to count delta output tokens.
3. Apply model-specific pricing per token.

To protect your wallet from runaway loops, read stop openai runaway token billing.

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High-Precision Pricing Models

Our Rust gateway supports detailed pricing maps for all major models:

• GPT-4o: Input: \$5.00 / 1M tokens, Output: \$15.00 / 1M tokens.
• GPT-4o-Mini: Input: \$0.15 / 1M tokens, Output: \$0.60 / 1M tokens.
• GPT-4: Input: \$30.00 / 1M tokens, Output: \$60.00 / 1M tokens.

The gateway logs detailed statistics for every transaction to /dashboard and reports cost telemetry dynamically.

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Run the Telemetry Tracker

Deploy the tracker container:

docker run -d \
  --name kilovolt-tracker \
  -p 8080:8080 \
  -e KILOVOLT_PORT=8080 \
  -e KILOVOLT_DEFAULT_BUDGET=5.00 \
  yodsarun/kilovolt-proxy:latest

This sets up a tracking proxy, providing full cost analytics for your deployments.