Observed Signal · Apr 12, 2026 · Technical Article · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
LLM Gateway vs Proxy vs Router Explained
This developer article defines three distinct layers used when integrating large language models: Proxy (transport), Router (decision), and Gateway (policy). It provides concrete Go code examples for each layer, explains routing strategies (cost-based, failover, metadata/tag-based), and shows how a gateway enforces identity-aware policies such as auth, rate limits, budgets, and audit logging. The post maps existing products to these layers (LiteLLM, Helicone, Portkey, Langfuse, Preto.ai) and offers a practical decision framework: single-team/one-model setups can call SDKs directly, multi-model teams should add a proxy+router for cost visibility and routing, and multi-team or compliance-sensitive environments need a gateway for governance and audit trails. The author notes Preto.ai is building an integrated proxy+router+gateway with cost intelligence and a free tier up to 10K requests.
Practical technical guidance on LLM integration layers matters to engineering teams building AI-driven products—impacts cost control, governance, and compliance—but it's an educational developer post rather than a major platform policy or product launch.
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Key Takeaways & Evidence Grounding
- Proxy = transport layer that forwards HTTP requests to LLM providers and is stateless with respect to callers.
- Router = decision layer that selects model/provider per request (supports cost-based routing, failover, metadata/tag routing) and is pure business logic.
- Gateway = policy layer that includes identity, auth, per-tenant rate limits, budget enforcement, and audit logging; it is stateful and enforces governance.
- The article maps products to these layers: LiteLLM, Helicone, Portkey, Langfuse, and Preto.ai (Preto.ai aims to provide all three layers plus cost intelligence).
- Author claims teams typically see 20–40% cost reduction within the first week after enabling model routing.
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When to Implement an AI Gateway
The article explains what an AI gateway is — a centralized layer between applications and LLM providers that handles routing, authentication, rate limiting, observability, cost tracking, and safety guardrails. It describes the common progression from direct SDK usage to simple proxies and finally to a full AI gateway as teams scale across multiple models and use cases. Triggers for adopting a gateway include multiple teams using different models, finance and compliance demands (e.g., HIPAA/GDPR/SOC 2), lack of cost visibility, and operational risk from provider outages. A production setup centralizes provider credentials, enforces per-team budgets and rate limits, logs prompts/responses/tokens/costs, applies PII filtering and prompt-injection checks, supports provider failover, and can run in VPC/on-prem. The author cites TrueFoundry as a practical example and notes performance claims (350+ RPS on a single vCPU with sub-3ms latency) and Gartner recognition of the category.
Unified AI API: Single Endpoint for Multiple LLMs
The article explains unified AI APIs — single endpoints that abstract multiple large language model (LLM) providers behind one interface — and why enterprises are adopting them to reduce integration, billing, and operational complexity. It defines managed gateways (e.g., OpenRouter, Eden AI) versus self-hosted proxies (e.g., LiteLLM), compares six platforms (PremAI, OpenRouter, LiteLLM, Portkey, Eden AI, Vercel AI SDK), and offers an evaluation framework focused on routing vs. full lifecycle needs (fine-tuning, evaluation, sovereign deployment). The guide cites enterprise adoption and spending trends, deployment options (cloud, private cloud, self-hosted), observability and compliance features, and trade-offs such as latency overhead and infrastructure management.
LLM Gateway Proxy with Security and Observability
A developer built an open LLM Gateway Proxy that sits between client applications and the OpenAI API to centralize security, compliance, and observability. The gateway applies layered checks — PII sanitization, heuristic prompt-injection detection, and response validation — before forwarding safe requests to the model. It records request-level metrics (latency, token usage, estimated cost) to a CSV ledger and exposes an interactive Streamlit dashboard for an experimental playground and operational metrics. The project is containerized with Docker and includes a GitHub Actions CI workflow; the full source code is published on GitHub. The author outlines trade-offs and future improvements including NER-based PII detection, embedding-based semantic guardrails, caching, persistent storage, distributed tracing, and production-grade monitoring.
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