Budgets

Real-Time AI Budget Controls: Pre-Flight Spend Limits & Token Quotas

Cloptima applies low-latency LLM budget checks in the request path and keeps the final usage trail ready for cost reporting.

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app.cloptima.ai/llm/policies
Gateway policies for providers, models, and spend
Approve providers, models, and spend scopes — enforced in the request path.
app.cloptima.ai/llm/policies
Rate limits, token limits, and request enforcements
Tighter controls: daily budgets, rate limits, and token limits per request.
app.cloptima.ai/llm/policies
Recent policy violations blocked by the gateway
Recent violations — blocked before provider egress, with full context.
The problem

Alerts arrive after the spend already happened

Traditional budget alerts arrive after usage already happened. LLM workloads need controls that stop runaway loops, unexpected model upgrades, and high-volume experiments before they become material spend.

  • Provider alerts arrive after the money is spent
  • A runaway agent loop burns through budget unnoticed
  • An unexpected model upgrade changes the cost of every call
  • Experiments share one bill with production
How it works

Budgets enforced before the call, not after

Cloptima reserves each request's estimated cost before the call and settles the actual cost after, so every budget reflects what is in flight.

  • Daily and monthly budgets per team, app, environment, or virtual key
  • One org-wide ceiling on all AI gateway spend
  • Alert only or Block at the budget, chosen per policy
  • The same budgets enforced on the cloud gateway and every edge gateway
Day one

Limits first, approval gates second

Create default development limits, protect production with higher thresholds, and require approval before teams use expensive models or unusually large context windows.

Architecture

Reserve, then finalize

Fast pre-flight checks, durable reservations, and usage finalization mean Cloptima never relies only on delayed logs or monthly provider exports. Edge gateways draw a share of the same budget and enforce it locally, so one budget holds everywhere your traffic runs.

Developer quickstart

Keep your SDK, change the base URL

The gateway speaks the OpenAI and Anthropic protocols. Point your existing client at it and add attribution headers.

bash
curl https://api.cloptima.ai/v1/ai/chat/completions \
  -H "Authorization: Bearer $CLOPTIMA_PAT" \
  -H "X-Cloptima-Team: data-platform" \
  -H "X-Cloptima-App: record-enrichment" \
  -H "X-Cloptima-Environment: production" \
  -H "Content-Type: application/json" \
  -d '{"model": "gpt-5.6-luna", "messages": [{"role": "user", "content": "Classify record batch"}]}'

# When daily/monthly budget ceiling is exhausted, gateway rejects pre-flight (0 provider spend):
# HTTP/1.1 403 Forbidden
# {"error": "Daily budget limit exceeded", "reason": "gateway_daily_budget_exceeded", "violations": ["gateway_daily_budget_exceeded"]}
Get started

Launch path

Define policy scopes, set monthly and daily limits, bind policies to virtual keys or teams, and review over-limit events from the audit view.

Comparisons

How the Cloptima gateway compares

Cloptima vs LiteLLM

OpenAI-compatible gateway routing vs. full FinOps control plane, attribution, and hot-path team budget limits.

Cloptima vs Portkey

Routing and guardrails vs. pre-flight budget enforcement, finance ledger, and p95 7–9ms end-to-end latency.

Cloptima vs Cloudflare AI Gateway

Edge proxying vs. enterprise attribution, team quota enforcement, and cost reporting.

Cloptima vs Helicone

LLM observability logs vs. active request-path budget controls, response caching, and unit economics.

FAQ

Questions teams ask

Put your first governed call through in five minutes

Point your SDK at the gateway, bind a policy, and watch the first call land on the ledger.

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