LLM Proxy: What It Is, When to Use One & Common Tools

An LLM proxy (sometimes called an LLM gateway) is a middleware layer that sits between an application and one or more large language model providers — OpenAI, Anthropic, Google, Mistral, open-source models, and others. Rather than calling each provider's API directly, the application calls the proxy, which handles routing, authentication, retries, caching, observability, and cost management on behalf of the application.

What an LLM proxy actually does

Provider routing: directs requests to the right model based on cost, latency, capability, availability, or A/B test rules.
Unified API surface: exposes a single interface (often OpenAI-compatible) that abstracts away provider differences. The application doesn't need to rewrite code when switching from one provider to another.
Authentication and key management: proxies hold API keys centrally rather than distributing them to every service or developer.
Rate limiting and retries: handles per-tenant rate limits, automatic retries on transient failures, and backoff logic.
Caching: caches responses to identical requests (especially useful for embeddings and deterministic outputs) to reduce cost and latency.
Fallback chains: if one provider is down, route to another; if a primary model rate-limits, route to a backup. Resilience without application code changes.
Observability: logs every request and response, with token usage, cost, latency, and error tracking — essential for debugging and cost control.
Cost tracking and budgets: attributes spend by team, feature, or customer, with hard limits to prevent runaway costs.
Content filtering and safety: applies pre/post-request guardrails for prompt injection, PII redaction, and policy compliance.

Why an LLM proxy matters at scale

For an application with one developer and one feature using one model, a proxy is unnecessary overhead. For a platform running dozens of AI features across hundreds of services, calling LLMs directly creates problems that compound fast: API keys scattered across codebases, no centralised cost visibility, no graceful degradation when a provider has an outage, and no easy way to swap models when a better or cheaper one ships.

The proxy solves all of these in one layer. It's the LLM equivalent of putting a load balancer in front of a backend service — most teams don't need it on day one, but at scale it's not optional.

How large platforms like Shopify use LLM proxies

Shopify operates AI features across millions of merchants — Shopify Magic for content generation, Sidekick for the merchant assistant, AI-powered search, semantic recommendations, support automation, and many internal tools. At that scale, calling a single LLM provider directly from each feature creates structural risk:

A multi-region outage at any one provider would take multiple Shopify features offline simultaneously.
Cost optimisation across providers would be impossible without rewriting each feature individually.
Per-feature observability, rate limiting, and budget controls would require duplicate infrastructure across every team.
Compliance and PII handling would have to be re-implemented per-feature rather than enforced centrally.

Platforms at this scale typically build (or adopt) an internal LLM proxy layer that all AI features call instead of individual providers. The proxy handles provider selection, fallback, rate limiting, observability, and cost tracking centrally — letting product teams ship AI features without solving infrastructure problems each time. Shopify has discussed this pattern publicly through engineering blog posts and conference talks; the same architecture is standard across other large platforms running production AI at scale.

Where LLM proxies fit for ecommerce merchants

Most Shopify merchants don't run their own LLM proxy — they use products that do. Klaviyo's AI features, Gorgias's AI agents, Shopify Magic, Sidekick, and most app-store AI tools sit on top of LLM proxies the vendor operates. The merchant experiences the result without managing the infrastructure.

Brands building custom AI features in-house — bespoke product description generators, internal merchandising tools, or AI shopping assistants beyond what platforms offer — typically end up needing a proxy once they have multiple models in play, multi-team usage, or production-scale traffic.

Common LLM proxy tools

LiteLLM: open-source proxy with the broadest provider support. Common starting point for teams building their own gateway.
Portkey: hosted gateway with strong observability, caching, and routing features.
Helicone: observability-focused proxy popular for cost and usage tracking.
Cloudflare AI Gateway: edge-based proxy with caching and analytics, integrated into the Cloudflare stack.
OpenRouter: aggregator that exposes many providers behind one API; closer to a marketplace than a self-hosted proxy.
Vercel AI Gateway: hosted proxy on Vercel's infrastructure, integrated with their AI SDK. Common in Next.js application stacks.
AWS Bedrock / Google Vertex AI: cloud-provider-managed gateways that route across the providers each cloud supports.
Internal builds: larger platforms (Shopify, Stripe, Notion, and similar) typically build their own proxy layer for tight integration with internal observability, identity, and cost-attribution systems.

When to use an LLM proxy

Multiple AI features in production. Once two or more features rely on LLMs, a proxy starts paying back through shared observability, key management, and cost tracking.
Multiple LLM providers in use. If the application calls both OpenAI and Anthropic (or wants to), the proxy's unified API and routing logic save substantial code duplication.
Production reliability concerns. Provider outages happen regularly. Brands needing resilience benefit from automatic failover, which is much harder to build per-feature.
Cost is material. Once monthly LLM spend reaches meaningful levels, cost attribution and budget controls become essential — and that's what the proxy provides.
Compliance and governance. Centralised logging, PII redaction, and provider audit trails are easier to enforce in a proxy than per-feature.

When you don't need one

Single feature, single provider, low traffic — a direct API call is simpler and adequate.
Prototyping. Adding proxy infrastructure before product-market fit is premature.
Hobby projects or internal tools where provider lock-in and outage risk don't matter.

Common LLM proxy mistakes

Building a proxy before you need one. Premature infrastructure investment. The right answer for early-stage products is direct provider calls; the proxy comes when scale demands it.
Treating the proxy as transparent. Every proxy adds latency. Carelessly designed proxies can add 50–200ms per request, which matters for user-facing features. Measure and optimise the proxy layer itself.
Skipping observability. A proxy without good logging is just a slower direct API call. The observability is most of the value.
Hardcoding provider-specific behaviour. If the application code still depends on OpenAI-specific or Anthropic-specific quirks even when calling the proxy, the abstraction isn't actually doing its job.
Not handling streaming. Many LLM features rely on streaming responses. Proxies that don't preserve streaming behaviour break the user experience for chat-like features.
Naive caching without semantic awareness. Caching only on exact prompt match misses the long tail of near-identical queries. Semantic caching (matching on embedding similarity rather than exact text) produces materially better hit rates but requires more setup. Most teams that turn caching on stop at exact-match and leave most of the savings on the table.
Sending PII to providers without redaction. The proxy is the right place to enforce data minimisation — strip names, emails, payment details, and customer addresses before they hit the model. Brands that don't redact at the proxy layer often discover compliance gaps later, when regulators or customers ask what happened to their data.