Deploying LLM-backed services to production exposes systems to a critical financial vulnerability: unbounded API consumption. A runaway recursive agent loop, bad retry logic, or a sudden spike in traffic can generate thousands of dollars in upstream provider bills in a matter of hours. LLMCostGuard addresses this by running as a high-performance Go sidecar that intercepts outbound requests and enforces token policy before traffic hits upstream endpoints.
Architecture and Routing Model
Positioned between your application services and upstream providers like OpenAI, Anthropic, and Groq, LLMCostGuard tracks prompt and completion tokens in real-time. It evaluates sliding window budgets, executes exact-match request caching, and rejects or reroutes calls when configured thresholds are reached.
Python Integration via SDK Redirection
Because LLMCostGuard exposes API-compatible endpoints, integrating it into existing Python applications requires no changes to core business logic. Simply point the provider client's base URL to the local proxy proxy port:
import os
from openai import OpenAI
# Direct traffic through the local LLMCostGuard reverse proxy
client = OpenAI(
base_url="http://localhost:8080/v1",
api_key=os.environ.get("OPENAI_API_KEY")
)
try:
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Summarize system error logs."}],
extra_headers={"X-CostGuard-Budget-ID": "backend-dev-daily"}
)
print(response.choices[0].message.content)
except Exception as err:
# Triggered when budget hard caps are hit or fallback fails
print(f"Proxy request rejected: {err}")Failover Cascades and Observability
When an upstream API experiences rate limits (429) or service outages (5xx), LLMCostGuard dynamically reroutes the payload to fallback models or secondary providers configured in the cascade rules. Native Prometheus endpoints expose token consumption, latency distribution, and cache hit metrics directly to your Grafana stack.