See every model call, govern who can use which models, attribute every dollar, and cap budgets in real time. Behest is the control plane for enterprise AI, so usage, spend, governance, and model access stay under control.
✓ Real-time visibility and control across production AI workloads
Who this is for
Behest sits between your apps and your AI providers, so IT, engineering, finance, and security read from the same source of truth.
Your company is racing to adopt AI, and every team is calling providers on its own. You own the rollout, but usage, spend, and risk are sprawling out of your control.
Centralize model access, enforce budgets and policy, attribute usage by team or application, and give your AI workloads a single controlled path to providers.
Learn moreAI spend is growing across providers, applications, teams, and agents, but the invoice does not tell you what created the cost.
Behest attributes spend to the workload and cost center that generated it and gives you enforceable budgets before the bill arrives.
Learn moreAI adoption is expanding faster than security teams can govern it.
Behest puts model access, PII protection, prompt-injection defense, policy, and auditability directly into the AI request path.
Learn moreCore capabilities
Behest is the control plane for enterprise AI: one place to see every model call, attribute its cost to the right owner, enforce budgets and policy in real time, govern model access, and route workloads intelligently. AI Token FinOps is its cost-control layer.
Four core capabilities work together: See usage, Control spend with AI Token FinOps, Govern access with AI Governance, and Optimize routing. Behest Radar extends visibility to supported AI activity on employee and developer machines (early access).
Per-request capture of model, tokens, cost, user, and project, attributable to the session that drove it, so a runaway workload shows up as a row, not a quarter-end surprise.
Hard token and dollar budgets on the request path, per user, project, and tenant. Stop overruns before the provider invoice lands.
PII scrubbing, prompt-injection defense, tenant model allowlists, and audit trails, inline on the AI request path.
Route eligible workloads to lower-cost models by policy, without rewriting application logic.
The control plane
An AI control plane is a shared control layer for enterprise AI traffic, putting AI usage under visibility, cost attribution, budgets, governance, and smart routing across models, applications, teams, and workloads.
Instead of scattering those controls across each app and provider, the enterprise routes its AI through one plane where cost and policy are observed and enforced in the same place.
The cost-control layer
AI Token FinOps is the cost-control layer of an enterprise AI control plane. It brings attribution, budgets, forecasting, and real-time enforcement down to the model-call level, so companies can understand and control AI spend before the provider invoice arrives.
Behest combines AI Token FinOps with governance, model access, security controls, and routing in one platform.
Product proof
Production applications are only part of enterprise AI usage. Developers use coding assistants. Employees use desktop AI tools. Agents create increasingly variable workloads.
Behest gives you request-level visibility and control across production AI traffic. Behest Radar extends that visibility to supported AI activity happening directly on individual employee and developer endpoints (early access, per machine today).
Model approvals, private-data scrubbing, prompt-attack defense, and per-session cost all land in one control plane, so security and finance read from the same record.
| Session | User | Model | Tokens | Cost |
|---|---|---|---|---|
| sess_8a2c…42b | u_91f3 | Gemini 2.5 | 4,201 | $0.038 |
| sess_b1d4…77e | u_27a8 | Claude 4.7 | 12,840 | $0.117 |
| sess_c9f2…1a3 | u_91f3 | GPT-5 | 8,415 | $0.084 |
| sess_d4e1…99c | u_55b2 | Gemini 2.5 | 2,108 | $0.019 |
| Total | 27,564 | $0.258 | ||
Sample data
Questions
The questions buyers ask first. For deeper technical or security questions, see the docs.
Looking for plans and tiers? See pricing.
See usage, enforce budgets and policy, govern model access, and understand what your production AI workloads are costing.
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