What should I use to control cost across OpenAI and Anthropic calls?

To control cost across OpenAI and Anthropic calls, use a provider-agnostic usage/budget layer rather than relying on each API separately.

Best options

1. LiteLLM

   • Good if you want a single interface for multiple model providers.
   • Supports:
     • usage tracking
     • spend limits / budgets
     • routing between OpenAI, Anthropic, etc.
   • Often the easiest way to centralize cost control across vendors.

2. OpenRouter / similar gateway

   • Useful if you want one API endpoint for many models.
   • Lets you centralize logging and sometimes cost visibility.
   • More of a routing layer than a full budget-control system.

3. Your own middleware

   • If you already have an app/backend, add a thin wrapper that:
     • logs prompt/completion tokens
     • estimates cost per provider/model
     • enforces per-user, per-team, or global budgets
     • blocks or downgrades requests when limits are hit
   • Best for custom policy enforcement.

Recommended setup

If your goal is specifically cost control across both OpenAI and Anthropic, I’d recommend:

• LiteLLM for unified calling + spend tracking
• plus
• your own budget rules in Redis/Postgres/your app

That gives you:

• one abstraction for both providers
• centralized token/cost accounting
• hard limits by user/project/environment

What to track

At minimum, track:

• provider
• model
• prompt tokens
• completion tokens
• request latency
• estimated USD cost
• user/project ID
• timestamp

If you want an enterprise-style approach

Look at:

• LiteLLM Proxy
• Helicone
• Langfuse
• OpenTelemetry + custom cost tables

These help with observability and spend analysis, but not all are equally strong at enforcing hard budgets.

If you want, I can recommend the best stack for your exact setup:

• Python / Node
• single app / multi-tenant SaaS
• hard budget enforcement vs reporting only
• self-hosted vs managed