BudgetGuard
Budget-as-code for LLM traffic.
BudgetGuard is an API-compatible proxy that sits between your apps or agents and upstream model providers. It enforces spend caps, rate limits, model and tool policies, redaction rules, and audit logging before requests ever leave your stack.
Built for teams deploying AI in production, BudgetGuard makes governance part of infrastructure instead of something left to prompts, UI settings, or developer discipline. You keep your existing agent architecture and point traffic through the proxy.
What it does
- Enforces budgets by project, API key, and user
- Applies deterministic model and tool allow/deny rules
- Limits requests, token volume, input size, and output size
- Redacts sensitive content before upstream calls
- Supports OpenAI-style chat, embeddings, and responses routes
- Preserves streaming workflows
- Creates append-only audit logs for replay, review, and compliance
- Adds idempotency protection and optional response caching
Why it matters
LLM systems are powerful, but they are also easy to overspend, hard to govern, and risky to operate without clear controls. BudgetGuard gives teams a practical control plane for AI usage: predictable spend, safer defaults, and better operational visibility.
Who it’s for
Engineering teams, platform teams, and AI product teams that want to ship agents and LLM features with stronger cost control, policy enforcement, and auditability.
Coming soon
BudgetGuard is being prepared for broader release. The first release will focus on lightweight deployment, policy-as-code configuration, OpenAI-compatible integration, and production-ready guardrails for real-world agent traffic.
BudgetGuard Cost Control Pilot
Turn BudgetGuard into a scoped cost-control engagement: one spend-control proxy workflow, budget/rate/policy limits, a usage-risk report, and a handoff plan for production controls.
Join the BudgetGuard waitlist
Get updates as BudgetGuard gets closer to release.