Monitor token usage and costs across AI workflows with auditable logs and per-run budgeting.
Token Estim8r AI Agent analyzes token usage across any AI workflow and estimates related costs end-to-end. It counts prompt and completion tokens, retrieves pricing (static or live via Jina API), and computes total costs per run. It logs results with model details and timestamps for auditing and future optimization.
End-to-end token and cost visibility for every workflow.
Analyze the target workflow to identify token-using steps.
Estimate prompt tokens for each call.
Estimate completion tokens for model responses.
Retrieve pricing from static data or live API.
Calculate total cost per run and per model.
Log token counts, costs, and metadata to a chosen destination.
This AI Agent makes costs visible in real time and enables proactive budgeting. It provides auditable logs and per-run cost details, helping teams defend spend and optimize usage.
A simple 3-step flow anyone can use.
The agent identifies the workflow to analyze and collects relevant inputs and data sources.
The agent counts tokens for prompts and completions and applies static or live pricing to compute costs.
The agent writes results to the chosen destination and can alert stakeholders if thresholds are exceeded.
A realistic scenario showing token estimation in action.
Scenario: A data science team runs a text-generation workflow using a mix of prompts and model responses, averaging 2,500 tokens per run. They deploy Token Estim8r to estimate token counts and costs, log results to Google Sheets, and optionally fetch live pricing from the Jina API. After a day, they review the sheet to identify high-token areas and adjust prompts or models to reduce spend.
Role-based reasons to adopt Token Estim8r AI Agent.
Needs precise per-call token counts and per-model cost breakdown for production workloads.
Requires visibility of costs across automated workflows to optimize budgets.
Wants cost estimates for experiments using LLMs and prompts.
Needs cost visibility when evaluating AI features within a budget.
Manages deployment budgets and monitors token-driven costs.
Budgets AI initiatives with auditable logs and trend analysis.
Tools the agent uses to collect, store, and alert on data.
Log tokens, costs, model, and timestamp; act as the primary audit trail.
Alternative destination for structured token/cost data.
Store daily summaries and cost breakdowns.
Send alerts when costs or token usage breach thresholds.
Provide live pricing data for token costs when enabled.
Archive detailed logs for BI and auditing.
Practical scenarios where the agent adds value.
Practical answers to common concerns.
The Token Estim8r AI Agent tracks token usage and estimates costs across your AI workflows. It logs data to your chosen destination and can fetch live pricing from the Jina API. It supports multiple models and can help you budget more accurately.
Live pricing is optional. You can run with static pricing data or enable a live pricing feed via the Jina AI Pricing API to reflect current rates.
The agent is designed to work with token-based LLMs such as OpenAI, Anthropic, and other providers. It can be extended to include additional pricing mappings as needed.
Choose your destination (Google Sheets, Airtable, Notion, SQL database, etc.) and connect it in the Token Estim8r setup. The agent writes per-run token counts, costs, model, and timestamps to the destination you select.
Yes. The agent processes multiple runs in a single workflow analysis, aggregating token counts and costs by run, model, and workflow. It can produce daily or weekly reports and supports incremental logging to your destination.
Security depends on the destination you choose. The agent uses authenticated connections to your data store and respects standard access controls. Data at rest and in transit is protected by the destination’s security measures, and you control who can view logs.
To enable live pricing you must provide your Jina API Auth Header in the pricing node. The agent will fetch current prices for each model and apply them to per-run cost calculations, ensuring up-to-date budgeting.
Monitor token usage and costs across AI workflows with auditable logs and per-run budgeting.
