AI MODEL AGGREGATION / SMALL-TEAM GUIDES
How to Troubleshoot and Debug TokenBison API Integrations
Learn how to use response metadata, request identifiers, and system logs to troubleshoot your TokenBison integration and resolve API errors effectively.
The short answer
To troubleshoot a TokenBison API integration, developers should capture request identifiers for technical support, monitor OpenAI-compatible response fields such as model and usage, and review logged technical metadata including timestamps, durations, and error information.
Inspecting OpenAI-Compatible Response Fields
TokenBison returns responses using an OpenAI-compatible request shape, which allows for direct inspection of technical metadata. Developers can verify that their application is processing the expected payload properties by reviewing the 'model' and 'usage' fields returned with each individual response. This check helps confirm that the request is utilizing the intended model ID from the catalog and accurately tracking token consumption.
Utilizing Request Identifiers for Support
When issues arise that cannot be resolved through local log inspection, developers are advised to capture the unique request ID associated with the API call. Including this request identifier when contacting support at hi@tokenbison.com facilitates targeted troubleshooting. This ID allows for a precise review of the specific request, prompts, or outputs necessary to resolve integration failures or unexpected behaviors.
Analyzing Logged Technical Metadata
TokenBison records specific technical and usage information that can be reviewed for debugging and performance monitoring. This data includes API key names, selected models, token counts, request counts, timestamps, and request durations. Reviewing these logged metrics, along with error information and security signals, enables developers to identify latency issues or incorrect API key usage within their integration environment.
Evidence and boundaries
These are documentation references and proposed evaluation workflows, not measured model rankings or a guarantee of savings. Check the current catalog and policies before use.
TokenBison API documentation · reviewed 2026-09-28
Authorization: Bearer $TOKENBISON_API_KEY Base URL https://tokenbison.com/v1 Keep your key on the server. Never expose it in client code. Examples Send a chat completion Select a model below to generate a ready-to-copy example. Select model Loading... cURL Python JavaScript Copy code curl https://tokenbison.com/v1/chat/completions \ -H "Authorization: Bearer $TOKENBISON_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "YOUR_MODEL_ID", "messages": [{"role": "user", "content": "Hello!"}] }' Need help? Include the request ID when available. hi@tokenbison.com
TokenBison verification and limitations · reviewed 2026-09-28
TokenBison Trust Verify before you build on it. Trust comes from checks you can run yourself, not claims you have to take on faith. Get your API key What TokenBison makes visible Information you can inspect at every step. Catalog IDs you can inspect Model IDs are shown in the catalog so you can select and test the exact ID used in your request. OpenAI-compatible requests Use the same request shape in your application and inspect the model and usage fields returned with each response. Clear pricing and usage Review model prices before you call, then track token usage and cost from one console.
TokenBison privacy policy · reviewed 2026-09-28
Usage and technical information: API keys and key names, model selected, token and request counts, timestamps, request duration, error information, IP address, device and browser information, log data, and security signals. API content: prompts, inputs, files, code, and outputs submitted through or generated by the Services, where necessary to provide the API, usage logs, troubleshooting, abuse prevention, and support. Cookies and similar technologies: information needed to operate the website, keep you signed in, remember preferences, measure performance, and protect the Services.
Put the guide into practice
Review current model prices, then make a small API request with public or non-sensitive sample data. Compare the output against your own acceptance criteria.