Build with AI
Ship AI with the guardrails already on.
Launch new AI products, workflows, and internal tools with routing, policy, and redaction in place from the first useful build instead of bolting them on later.
Blueprint Hero
The first protected path should look simple.
OPENAI_BASE_URL=https://proxy.spendplane.com/v1
SPENDPLANE_POLICY=builder-default
SPENDPLANE_BUDGET_LIMIT=25
SPENDPLANE_ROUTE=auto
Run this path
- Point your model base URL at Spendplane
- Set a project policy and budget limit
- Ship the same code path into production
Builder
Prompt + app logic
Spendplane
Policy + route + redact
Provider
Approved execution
Provider switchboard
Put the control plane in the build path before AI becomes architecture.
New projects should feel structured from day one. Instead of a provider hardcoded into the app, the project starts with a reusable path that can survive growth, security review, and provider changes.
Builder surface
Replit, Bolt, v0, internal tooling, and local dev flows.
Spendplane control plane
Policy, routing, redaction, budget control, and audit traces.
Model boundary
OpenAI, Anthropic, OpenRouter, or local runtimes with a safer path.
Policy as code
Build path active
route.default = "balanced"
budget.monthly = 25
redaction.email = true
fallback = ["local-llama", "gpt-4o-mini"]
[check] config validated
[route] default lane provisioned
[policy] redaction enabled
[status] safe to ship preview
Prototype-to-production gap
Security debt starts during the exciting part.
Routing debt
Hardcoded provider paths get expensive to unwind as AI spreads across projects.
Prompt debt
Secrets, customer details, and internal notes leak into prompts long before anyone calls it production.
Budget debt
Without a shared control layer, the first successful experiment becomes the default spend path.
Developers
- One golden path for prompts, config, and provider routing.
- A practical way to test protections before the first launch.
Platform
- A reusable control layer instead of per-project AI plumbing.
- Provider boundaries and cost controls before teams fragment the stack.
Security
- Policy checks and redaction events instead of manual prompt hygiene.
- A smaller cleanup problem once AI reaches production pressure.
Enterprise readiness
Start with controls that still make sense when the project becomes a platform.
Even a greenfield AI workflow should not need a future rewrite just to add governance. Spendplane gives new projects a control layer that can grow into RBAC, private deployment, and audit-ready operations.