🌐 Managing API Costs and Throttling

Every team starts by calling an LLM provider’s API directly. That works great — until it doesn’t.

Once you go into production, you hit:

• Rate limits from OpenAI or Anthropic
• Unexpected spikes in cost
• Lack of visibility into which workflows are consuming tokens
• Compliance concerns around sending data to third-party APIs

This is where a gateway becomes essential.

🚪 What’s an LLM Gateway?

An LLM gateway is a proxy layer between your agents and the LLM provider. It intercepts and manages all traffic going to models like GPT-4, Claude, or your own hosted model.

Aegis includes a FastAPI-based gateway that provides:

• Request routing to different models based on workload or cost constraints
• Authentication and RBAC to enforce org/user-level access
• Usage logging per org, team, or use case
• Throttling and rate limiting
• Data masking and redaction before outbound calls

You can run it yourself or deploy it in a secure VPC.

💰 Why It Matters

💸 How LLMs Charge

Most LLM providers (like OpenAI, Anthropic, Mistral) charge based on:

• Tokens in (prompt size)
• Tokens out (response size)
• Model type (e.g. GPT-4 Turbo vs Claude Haiku)

Costs can quickly spiral when prompts get large — especially with RAG or agent workflows. And if you’re relying on just one vendor, you’re at their mercy for:

• Pricing changes
• Quotas or rate limits
• API outages

🔁 Why Evaluation Enables Switching

Once you have prompt evaluation pipelines in place, you can:

• Seamlessly compare different models on the same task
• Validate output quality vs cost tradeoffs
• Make informed decisions about vendor switching or fallback strategies

You can even A/B test model swaps in production with minimal disruption.

🛡️ Auditing + Security

In production, you’ll also need:

• Audit logs of every prompt and response
• Redaction of PII before sending to external APIs
• Throttling per tenant/user/group to prevent abuse or overuse

All of this needs to happen in one centralized control plane — the gateway.

⚙️ Using LiteLLM + Aegis

LiteLLM  is a great OSS project that provides a unified interface to multiple LLM APIs.

We optionally layer on:

• Per-tenant routing + policy enforcement
• RBAC for endpoints and workflows
• Usage reporting for orgs, users, and use cases

You get the flexibility of LiteLLM with enterprise-grade access control and observability — no vendor lock-in, no blind spots.

Without a gateway:

• You have no per-user, per-workflow billing visibility
• You can’t control or cap API usage
• You’re tied to a single LLM provider

With a gateway:

• You can swap models easily (e.g. Claude Haiku vs GPT-4 Turbo)
• You can route based on complexity
• You can self-host smaller models to save cost

This is how you keep quality high without blowing your budget.

🏫 LMS Example: Smart Routing for Grading

Let’s say your LMS does auto-grading at scale. Some tasks are:

• Simple (e.g. Yes/No, MCQs)
• Complex (e.g. open-ended essay responses)

With a gateway:

• You route simple tasks to a cheaper model (e.g. LLMLite or Claude Haiku)
• Route complex ones to GPT-4 only when needed
• Track usage by institution, course, or team
• Log all prompts/responses for evaluation and audit

You’ve now reduced cost without sacrificing quality.

A good gateway is not just infrastructure. It’s your cost control center, routing engine, and security layer all in one.

Next: How to manage security, privacy, and compliance across your agent stack.