Token caps, burst guards, and token packs.

Token caps

Token caps by model tier class

The spend boundary on flat tiers is a hard token cap per model tier class, enforced per customer over a weekly and a monthly window. A model’s tier class (budget / mid / premium / ultra) is set by its price. Caps are shared across all keys on an account — N keys draw from one bucket, closing the multi-key gaming exploit older gateways have. Exceed a class’s weekly or monthly cap and the request returns 402 token_quota_exceeded. A class with no row for a tier is not available on that tier.

Tokens count input + output. Starter runs budget models only. Pro adds mid + premium. Ultra-class models are Enterprise / PAYG territory. PAYG and Enterprise have no hard token cap — usage is governed by a soft internal cost-velocity alert plus your commercial terms.
A reserved Free tier (budget only, 1M wk / 3M mo) exists in schema but has no live signups today.
Custom per-customer overrides are available — contact [email protected].

402 vs 429

Two different boundaries

• 402 token_quota_exceeded — you spent your weekly or monthly token cap for a model tier class. The response carries model_tier_class and window (weekly | monthly). Retrying won’t help until the window rolls over — buy a token pack, upgrade your tier, or move the workload to a class you still have budget in.
• 429 rate_limit_exceeded — you tripped the RPM/TPM burst guard. Transient: back off and retry, respecting Retry-After.

Handling 429

Respect Retry-After

The OpenAI / Anthropic SDKs retry automatically by default, but they ignore Retry-After in some versions. Here’s an explicit pattern that picks server-suggested wait when present, exponential backoff otherwise:

import time
from openai import OpenAI
from openai import RateLimitError, APIStatusError

client = OpenAI(base_url="https://api.anoman.io/v1", api_key="anm-sk-...")

def call_with_backoff(messages, model="gpt-4o-mini", max_attempts=5):
    for attempt in range(max_attempts):
        try:
            return client.chat.completions.create(model=model, messages=messages)
        except (RateLimitError, APIStatusError) as e:
            if attempt == max_attempts - 1:
                raise
            # Retry-After in seconds (server-suggested wait)
            retry_after = float(e.response.headers.get("retry-after", 0))
            # Fall back to exponential backoff with jitter
            sleep = retry_after or min(2 ** attempt, 30)
            time.sleep(sleep)

Token packs

Buy extra tokens when you need headroom

On a flat tier, when a model tier class is close to its token cap, buy a token pack to add temporary headroom to that class for a fixed duration. It’s an additive bump to the class’s cap — not a change to your RPM/TPM burst guards. Costs are deducted from your prepaid balance.

import requests

# Token packs add temporary headroom to a model tier class's token cap.
# Cost is deducted from prepaid balance. Pricing varies per tier — see the
# token-pack pricing table in your dashboard.
response = requests.post(
    "https://api.anoman.io/anoman/v1/burst/activate",
    headers={"Authorization": "Bearer anm-sk-..."},
    json={"model_tier_class": "premium", "extra_tokens": 5_000_000, "duration_hours": 24},
)
response.raise_for_status()
print(response.json())
# {
#   "active": true,
#   "model_tier_class": "premium",
#   "extra_tokens": 5000000,
#   "expires_at": 1740086400,
#   "cost_usd": "7.00",
#   "balance_remaining_usd": "23.00"
# }

PAYG and Enterprise don’t need token packs — they have no hard token cap to begin with.