Spend controls

Per-request limits, context-window guardrails and budget management

Spend controls

Limit your spend at the request level (via max_tokens) and handle oversized prompts gracefully before you ship to production.

Budget alerts, daily hard/soft limits, and auto-suspend are on the roadmap. For now the only spend guards are per-request max_tokens and manual monitoring through the dashboard.

Per-request token cap

Set max_tokens on every request to prevent runaway generation from unbounded prompts or accidental infinite loops.

from openai import OpenAI
client = OpenAI(api_key="YOUR_RADIUM_API_KEY", base_url="https://api.radium.cloud/v1")

response = client.chat.completions.create(
    model="clarke-1.0",
    messages=[{"role": "user", "content": "Write a chapter"}],
    max_tokens=4096,
)

The absolute context-window limit depends on the model you choose:

Model	Context window
hal-1.0	250,000 tokens
clarke-1.0	1,000,000 tokens
tycho-1.0	125,000 tokens

Rejecting oversized requests

Radium returns 400 with code context_length_exceeded if the prompt exceeds the model’s context window. Handle this gracefully:

try:
    response = client.chat.completions.create(model="clarke-1.0", messages=messages)
except APIError as e:
    if e.code == "context_length_exceeded":
        # Truncate or split the prompt
        messages = messages[-10:]  # Keep last 10 messages
        response = client.chat.completions.create(model="clarke-1.0", messages=messages)

Usage audit

Export a CSV of recent requests from the dashboard for custom analysis:

Column	Description
timestamp	UTC request time
model	Model ID used
input_tokens	Tokens in the prompt
output_tokens	Tokens in the response
cost_usd	Estimated cost for this request
status_code	HTTP status

Pre-production checklist

Before shipping to users, verify:

• max_tokens is set on every request
• Your app handles context_length_exceeded errors
• Usage is reviewed regularly in the dashboard
• Budget guardrails (alerts, auto-suspend) — coming soon on our roadmap