Knowledge Base
Install the wrapper and start tracking in under two minutes
Detailed installation guide for the Tokeven Python SDK
Make your first API call through the wrapper and see it in the dashboard
A tour of the three dashboard views - Spend, Save, Proof - and the pages around them
Reading the Overview and Cost Drivers tabs on the Spend dashboard
See which models your team uses and where to look for cheaper options
Understanding token counts, cache rates, and prompt efficiency
When your team uses AI, and who drives spend
Get your data out of Tokeven in any format
Complete API reference for the Tokeven Python wrapper
Price calls before you send them from Claude Code with the Tokeven MCP server
Connect Tokeven alerts to Slack, Teams, email, and more
Track every LiteLLM completion with one callback
Track LangChain LLM calls with a callback handler
Track every model call an agent makes with a trace processor
Track Claude, OpenAI, Gemini and Grok calls from Node and edge runtimes
How Tokeven keeps your prompts private by design
A detailed look at the data flow from your machine to Tokeven
Create, rotate, and revoke your write-only ingest tokens
See the cost before you run
How Tokeven suggests the right model for each task
Maximize cache hits to cut the cost of repeated input
How we measure and prove the dollars you save
Add team members, set roles, and manage access
How our 10% of verified savings model works
The ingest and API rate limits we actually enforce, and the one cap that is plan-dependent
How companies running chatbots, knowledge hubs, and internal AI tools can cut costs with Tokeven
A practical guide to cutting AI chatbot spend with model tiering and cache optimization