Custom Access

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What is Custom Access?

• You’re calling an LLM from your own code and want that traffic governed.
• You use a client that supports an OpenAI-compatible endpoint but has no Unbound integration page of its own.
• You want to try Unbound with curl before wiring up a real tool.

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Prerequisites

• Unbound account — Sign up at gateway.getunbound.ai if you don’t have one
• A client that can point at a custom base URL — most OpenAI-compatible clients and SDKs can

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Get your values

unbound setup custom-access

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Configuration

curl https://api.getunbound.ai/v1/chat/completions \
  -H "Authorization: Bearer $UNBOUND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o",
    "messages": [{ "role": "user", "content": "Say this is a test" }]
  }'

from openai import OpenAI

client = OpenAI(
    base_url="https://api.getunbound.ai/v1",
    api_key="<UNBOUND_API_KEY>",
)

completion = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Say this is a test"}],
)

export OPENAI_BASE_URL="https://api.getunbound.ai/v1"
export OPENAI_API_KEY="<UNBOUND_API_KEY>"

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Verify it’s working

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What you get

• Guardrails — PII and secrets detection on prompts, with block, redact, or route actions
• Model and cost policies — restrict which models this key can reach, and cap what it can spend
• Analytics — usage, cost, and token consumption, attributed and searchable