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 moreThe short version
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.
Core capabilities
Behest is the control plane for enterprise AI, bringing four core capabilities together: See usage, Control spend with AI Token FinOps, Govern access with AI Governance, and Optimize routing. Behest Guard extends visibility to supported endpoint AI activity, and an AI Backend is available for teams that need browser-callable AI.
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.
Behest Guard · Early access
Behest Guard provides local visibility into AI services used directly on a developer or employee machine: coding assistants, desktop applications, and CLI tools that centralized AI infrastructure may never see. Guard is in early access and runs per machine today (no central fleet dashboard yet).
Deployment
Behest runs as SaaS in Behest’s cloud on every tier, live in minutes with nothing to operate. On the Enterprise tier, self-hosting is an option: deploy Behest via Helm on GKE or any Kubernetes cluster in your own environment.
With the self-hosted option, prompts, completions, and provider keys stay inside your own VPC, under your KMS, egress, and backup policy.
Optimization
Apply routing policy based on cost, model capability, or enterprise requirements. Use lower-cost models where the workload allows 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.
Governance and security layer
AI Governance puts security and policy controls inline on the AI request path: PII scrubbing before a request reaches an external model, prompt-injection defense, tenant model allowlists, and audit trails on every request.
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 Guard extends visibility to supported AI activity on an individual machine where it is installed.
Together, they extend visibility from controlled production traffic to supported AI activity on individual endpoints.
Model allowlists, PII scrubbing, and Sentinel pair with session attribution: one gateway for CFO and CISO KPIs.
| 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
Free tool · 2 minutes
Enter a few numbers and see your projected annual AI cost, how much is unattributed, and the budget you could overrun, all before the invoice arrives.
Bring your own provider keys, or use models through Behest at pass-through cost. Behest is a predictable software license, not a percentage of your AI spend.
Behest combines inline control over production AI traffic with endpoint visibility through Guard (early access). Legacy tools mostly report after the fact.
| Capability | Behest | Legacy Gateways | Legacy FinOps | Build your own | Direct LLM OpenAI, Anthropic |
|---|---|---|---|---|---|
| User-level attribution on controlled AI traffic | Partial | Partial | |||
| Endpoint AI discovery | Guard, early access | ||||
| Exact usage classification (team, project, cost center) | Partial | Partial | |||
| Real-time spend controls (hard budgets, kill switches) | Partial | ||||
| Per-session cost attribution | Partial | ||||
| CIO chargeback (per-cost-center allocation) | Partial | Partial | Partial | ||
| Token budgets enforced inline | |||||
| PII redaction pre-LLM | Partial | ||||
| Prompt-injection defense | Partial | ||||
| AI Governance (model approval, audit trail) | Partial | ||||
| Easy setup & key management (no per-user API keys) | Partial | ||||
| Self-hosting option (your cloud) | |||||
| Pass-through pricing (no token markup) |
"Partial" indicates the capability exists in a narrower form (e.g., metadata-based attribution without cost-center hierarchy, or provider-restricted prompt-injection coverage). "?" indicates the capability is not documented in publicly available materials at the verification date. "n/a" means the capability does not apply to that category. "Guard, early access" indicates the capability is delivered by Behest Guard, which is in early access and runs per machine today (no central fleet dashboard yet).
Traditional gateways see the traffic that passes through them, but typically have limited visibility into AI usage that bypasses the gateway. Behest pairs inline control with endpoint visibility through Behest Guard (early access).
Traditional FinOps tools are primarily designed around reporting and allocation after consumption. Behest operates directly in the AI request path, allowing budgets and policy to be enforced as usage happens.
User-level attribution on controlled traffic, cost-center allocation, PII Shield, Sentinel, and AI Governance take significant engineering effort to build well. Behest provides these as a managed platform, run as SaaS or self-hosted in your own cloud.
Questions
The questions buyers ask first. For deeper technical or security questions, see the docs.
See usage, enforce budgets and policy, govern model access, and understand what your production AI workloads are costing.
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