TokenPilot is an AI token cost governance platform for teams using LLM APIs, AI agents, and automated workflows.

It helps teams move from month-end bill review to real-time anomaly detection, precise attribution, stop-loss action, and token ROI measurement.

In short: other tools tell you how much you spent. TokenPilot helps explain why it was spent, where it was wasted, and how to reduce preventable cost.

Token cost has three risk patterns that traditional SaaS or cloud cost tools do not handle well:

• Real-time spend: every API call creates cost immediately, and month-end bills arrive too late for stop-loss action.
• Automated amplification: agent loops, retry storms, and context growth can multiply cost in hours.
• Distributed ownership: calls come from products, engineering, test environments, scripts, and employee tools.

TokenPilot helps teams build automated governance that matches automated usage.

Token governance maturity evolves across five capability domains:

Bills alone do not create visibility. Alerts alone do not create stop-loss control. Cost records alone do not prove ROI.

TokenPilot focuses on high-cost AI usage risks including:

• Agent loop: an agent keeps calling models without reaching a stop condition.
• Retry storm: failures trigger repeated retries and compound failed-call cost.
• Test environment waste: development or test workloads consume production budget.
• Employee abnormal usage: personal tools or scripts create unexpected spend.
• Context growth: conversation history expands and later calls become expensive.
• Model mismatch: simple tasks use high-cost models unnecessarily.
• Low-value high-cost customers: customer or feature economics become inverted.
• Over-retrieval in RAG: irrelevant context inflates each call.
• Runaway batch jobs: abnormal data makes batch workloads exceed expected cost.
• API key exposure: leaked credentials cause abnormal usage.

A provider bill tells you total spend, but it usually cannot answer:

• Which team, product, workflow, or agent created the cost?
• Was the increase normal, or did an anomaly drive it?
• Which calls were waste, and which calls created business value?
• What will next month's bill look like if the trend continues?

Bills show the result. TokenPilot shows the process through minute-level monitoring, attribution, and proactive alerts.

Team size is less important than how automated your AI usage is. Governance becomes important when:

• You run production AI agents or automated workflows.
• Your monthly LLM API cost is over $500 and cannot be explained by feature or customer.
• You have seen at least one unexplained AI bill spike in the last 12 months.

The free risk assessment can identify your current governance level, major risk, and next actions without integration work.

Token cost needs a governance system designed for automated, high-frequency model calls.

TokenPilot is not just accounting. It connects token consumption to business behavior:

• Business-intent attribution: cost is grouped by scenario, user, feature, agent, workflow, and environment.
• Anomaly pattern detection: agent loops, retry storms, and context growth become visible.
• Cost-to-outcome linkage: token spend can be compared with task completion, conversion, resolution, or revenue signals.

The product moves from diagnosis to governance, from real-time alerts to controlled automation.

The principle is evidence before conclusion. Every risk judgment should be tied to observable fields and calculation logic.

• Alerts include the triggering rule, estimated cost impact, owner or workflow attribution, and recommended next action.
• When evidence is missing, TokenPilot shows the gap instead of forcing a conclusion.
• Deduplication and rate limits reduce alert noise.
• Automated governance actions should support dry-run preview, human approval, and rollback.

• No integration: online assessment, no uploaded data.
• File upload: CSV, JSONL, or LiteLLM logs for report generation.
• SDK or webhook: lightweight real-time metadata ingestion.
• Gateway integration: compatible with common AI gateway patterns.
• Business data import: connect revenue, CRM, or support signals for ROI analysis.

Those tools primarily operate as AI gateways: unified API access, routing, and basic usage accounting. TokenPilot operates as an intelligence layer for cost governance and business optimization.

LLM observability tools focus on debugging and evaluation: prompt traces, response quality, and experiment records.

TokenPilot focuses on cost governance: waste detection, attribution, stop-loss response, and ROI. Observability tools ask whether output quality is good. TokenPilot asks whether the spend is justified.

Start with the free maturity assessment.

In 15 questions and about 3 minutes, it identifies your maturity level, top risk scenario, and priority action items. It does not require uploaded data or system integration.

Start 3-minute assessment

• V0.1 Risk assessment: no integration, online assessment only.
• V0.2 Report generation: upload existing CSV or JSONL logs for a report.
• V0.4 Dynamic alerts: SDK or webhook integration, typically 1-2 days.
• V1.0 Business edition: team, budget, and dashboard setup, typically about one week.

The product is designed to prove value with a lightweight report before asking for real-time integration.

• Commercial models: OpenAI, Anthropic, Google Gemini, Mistral, Cohere, Together AI, Qwen, ERNIE, GLM, DeepSeek, Kimi, Doubao, and others.
• Self-hosted models: Llama, Qwen, and other local models through compatible gateways such as LiteLLM.
• Multi-model and multi-provider setups: unified attribution and tracking in one dashboard.

Most analysis can be done with call metadata, not full prompt or response content.

• Required fields can include timestamp, model, input and output token counts, cost, status code, and attribution tags.
• Optional fields can include error type, retry count, environment, and trace ID.

For deeper prompt efficiency analysis, teams can choose to send redacted prompt fields. PII does not need to be uploaded.

Private deployment options are planned for the business edition, including SaaS, hybrid deployment, and fully private deployment in a customer VPC or private cloud. Hybrid deployment can process data inside your environment and send only aggregated metrics and alerts to TokenPilot cloud.

TokenPilot's analysis pipeline is designed to be asynchronous. Metadata collection and heavier analysis should not sit on the core request path.

• The request path only records lightweight metadata.
• Behavior analysis runs asynchronously outside production latency.
• Alerts are evaluated on minute-level windows rather than every individual request.