Building Your Own LLM Proxy: The Insider Story of Model Calls You Didn't Know

Why Do You Need Your Own LLM Proxy?

On Hacker News, a short question sparked a discussion: "If you don't run your own LLM proxy, how do you know what's happening?" The post had only 13 points and 5 comments, but it hit a key pain point: when we rely on third-party APIs to call large language models, we know almost nothing about how requests are actually processed.

An LLM proxy is a middle layer between your application and LLM providers. It logs all requests and responses, letting you monitor latency, cost, errors, and model behavior. Without it, you're flying blind—you only see the input and output, not what happens in between.

The Hidden Risks of Opaque API Calls

Most developers call OpenAI, Anthropic, or other providers' APIs directly and assume everything is fine. But the problem is: you can't see how requests are handled inside the provider. For example, has the model been downgraded? Was content filtering triggered? Did response times slow down due to rate limiting? These details never appear in official dashboards.

More seriously, when anomalous outputs occur, you can't trace back the full request context. A proxy can capture the original request, response headers, token usage, and any intermediate errors, allowing you to quickly pinpoint the issue.

Rate Limiting is a common traffic control mechanism used by LLM providers, typically based on requests per minute (RPM) or tokens per minute (TPM). When an application hits rate limits, the API returns a 429 status code, potentially with increased response latency or rejected requests. Official dashboards often show only aggregated success rates, without exposing whether individual requests were queued or downgraded due to rate limiting. A proxy can log each request's response headers (such as x-ratelimit-remaining) and status codes, helping developers precisely identify when and how often rate limiting occurs, then adjust retry strategies or request higher quotas.

Three Major Benefits of a Self-Built Proxy

1. Observability and Debugging

With a proxy, you can log the complete lifecycle of each request. This includes request bodies, response bodies, status codes, latency, and token counts. When the model returns unexpected results, you can inspect the raw data directly instead of guessing.

2. Cost Control and Optimization

A proxy can aggregate token usage, letting you track costs by project, user, or feature. You can also set budget alerts or even automatically switch models when thresholds are reached.

Token metering is the foundation of LLM cost control. Different models price input and output tokens differently, and some providers have differentiated pricing for cache hits, batch requests, and other scenarios. A proxy can count input and output tokens before and after forwarding requests, and calculate the actual cost of each call based on model unit prices. Additionally, a proxy can identify duplicate requests and enable caching to avoid repeated billing for the same prompts. By associating token usage with business dimensions (such as user ID or feature module), teams can allocate costs more precisely and discover optimization opportunities.

3. Flexible Routing and Fallback

A proxy lets you route traffic to different models based on request content (e.g., simple queries to small models, complex tasks to large models). If a provider fails, the proxy can automatically switch to a backup service to ensure availability.

Model Routing refers to dynamically selecting the most appropriate model based on request characteristics (such as prompt length, task type, or user tier). For example, classification or summarization tasks can be routed to lower-cost small models, while complex reasoning tasks go to large models. Fallback means automatically switching to a backup model or provider when the primary model is unavailable or times out, ensuring service continuity. The advantage of implementing routing and fallback at the proxy layer is transparency to the application—business code doesn't need to know about underlying model switching logic; it just calls a unified proxy interface.

How to Implement a Simple LLM Proxy

Implementing a proxy isn't complicated. You can use lightweight frameworks like FastAPI or Express to intercept requests and forward them to upstream APIs. Core features include:

• Request logging: Record request and response details.
• Authentication management: Centrally manage API keys to avoid exposing them on the client side.
• Rate limiting: Prevent abuse.
• Caching: Return cached results for duplicate requests to save costs.

Open-source solutions like LiteLLM, Helicone, and Portkey offer ready-made proxy capabilities that you can deploy quickly.

LiteLLM is an open-source Python library and proxy server that supports a unified interface for calling over 100 LLM APIs (including OpenAI, Anthropic, Azure, etc.), and provides load balancing, fallback, budget control, and logging. Helicone is an observability platform that logs requests, latency, costs, and token usage via a proxy, and offers visual dashboards. Portkey provides AI gateway features, supporting routing, caching, rate limiting, and guardrails. These tools can all be deployed quickly via Docker, suitable for teams of different sizes to choose according to their needs.

Conclusion: Take Control of Your LLM Calls

The question on Hacker News reminds us that in LLM application development, observability is not optional. A self-built proxy lets you move from passive response to proactive control. Whether for debugging, cost optimization, or high availability, a simple proxy layer can bring enormous value.

Don't wait until problems arise to regret it—build your LLM proxy now.

One-line Summary

A self-built LLM proxy solves the black-box problem of third-party API calls, enabling observability, cost control, and flexible routing.

Paragraph Summary

Starting from a discussion on Hacker News, the article points out that when developers directly call third-party LLM APIs, they lack visibility into request processing, like flying blind. A self-built LLM proxy, as a middle layer between the application and providers, can log the complete request lifecycle and brings three major benefits: observability and debugging, cost control and optimization, and flexible routing and fallback. Implementing a proxy isn't complicated; you can use lightweight frameworks like FastAPI or Express, with core features including request logging, authentication management, rate limiting, and caching. Open-source solutions like LiteLLM, Helicone, and Portkey offer ready-made capabilities for quick deployment. The conclusion emphasizes that observability is not optional, and a self-built proxy lets developers move from passive response to proactive control.