LLM Gateway Architecture

Modern enterprises use three to five or more LLM providers simultaneously. OpenAI GPT-4o handles general reasoning. Anthropic Claude excels at long-context analysis and complex code generation. Google Gemini covers multimodal tasks. Local or on-premises models process sensitive data that cannot leave the network. Specialized models serve domain-specific needs in legal, medical, and financial workflows.

Each provider brings its own SDK and API format, authentication mechanism, pricing model (per-token, per-request, per-character), rate limits and quotas, and compliance posture around data residency and business associate agreements. The result is credential sprawl, zero unified cost visibility, no central audit trail, and inconsistent error handling across every team and application.

This is not a theoretical problem. A typical enterprise with 200 developers discovers 15-30 different API keys scattered across personal accounts, CI/CD environments, and production deployments — each representing an untracked, ungoverned connection to an external AI provider.

An LLM gateway is a reverse proxy purpose-built for LLM API traffic. It sits between your applications and LLM providers, intercepting every request to apply governance, routing, caching, and observability before forwarding to the appropriate model.

Think of it like an API gateway (Kong, AWS API Gateway) but specialized for AI. A generic API gateway counts requests and enforces rate limits by request volume. An LLM gateway understands tokens, models, prompt-response pairs, and AI-specific concerns that generic gateways cannot address.

The key differences from generic API gateways include: token-aware rate limiting instead of just request counts, model-aware routing that sends requests to the best model based on task characteristics, prompt and response logging for audit compliance, cost tracking at the token level across multiple pricing models, semantic caching that deduplicates similar prompts, and PII detection and redaction in prompt payloads before they reach external providers.

A production-grade LLM gateway provides eight core capabilities, each addressing a distinct operational concern. Together, they transform ungoverned, direct API calls into a managed, observable, and cost-optimized AI infrastructure layer.

1. Unified API

One endpoint, any provider. Applications send requests in a single format (typically OpenAI-compatible), and the gateway translates to each provider's native API. Switching from GPT-4o to Claude requires a configuration change, not a code change.

2. Intelligent Routing

Route requests based on cost optimization, latency requirements, model capability, data sensitivity classification, and team quotas. A gateway can automatically select the cheapest model that meets quality requirements, or route sensitive prompts to on-premises models while allowing general queries to go to cloud providers.

3. Failover & Retry

Automatic provider fallback when a model is down or rate-limited. Circuit breaker patterns prevent cascading failures. If OpenAI returns 429 (rate limited), the gateway transparently retries with Anthropic — the application never sees the failure.

4. Credential Management

Centralized, encrypted API keys. Applications authenticate with the gateway using internal tokens; they never hold provider credentials directly. When you rotate an OpenAI API key, you update it in one place — not in 30 different deployment configurations.

5. Token-Aware Rate Limiting

Limit by tokens-per-minute, not just requests-per-second. A single LLM request can consume anywhere from 100 to 128,000 tokens, making request-based rate limiting meaningless. Per-team, per-project, and per-agent quotas ensure fair resource allocation and prevent runaway costs.

6. Prompt Caching

Exact-match and semantic caching reduces duplicate LLM calls. When multiple users or agents send similar prompts, the gateway returns cached responses instead of making redundant API calls. Organizations typically see 20-40% cost savings from caching alone.

7. PII Redaction

Scan prompts for sensitive data — Social Security numbers, credit card numbers, email addresses, patient identifiers — before they reach external LLM providers. Redaction happens inline, ensuring compliance with HIPAA, PCI-DSS, and GDPR without requiring application-level changes.

8. Cost Attribution

Track spend by team, project, agent, and model with real-time dashboards. Know exactly which application consumed how many tokens on which model, and attribute costs back to business units. Set budget alerts at 80% utilization and hard caps to prevent cost overruns from agent loops or inefficient prompting.

There are three primary deployment architectures for LLM gateways, each with distinct trade-offs in complexity, performance, and operational overhead. The right choice depends on your organization's scale, latency requirements, and data sovereignty needs.

Most enterprises should start with the centralized pattern. It provides the simplest path to governance — a single deployment that handles all LLM traffic, with one audit trail, one policy configuration, and one monitoring dashboard. High availability is achieved through standard load balancing and replicas, not architectural complexity.

As requirements mature, organizations can evolve toward sidecar or mesh patterns. The key principle is that governance comes first — the specific deployment topology is a secondary concern that can be adjusted without changing governance policies.

Beyond architecture patterns, the hosting model for your LLM gateway determines your data sovereignty posture, operational overhead, and time to deployment. Three options exist, each serving different organizational requirements.

SaaS (Cloud-Hosted)

Fastest to deploy, managed by the vendor, no infrastructure to maintain. Data passes through the vendor's infrastructure, which may be acceptable for non-regulated workloads. Ideal for teams that want governance immediately without provisioning infrastructure.

Self-Hosted (On-Prem / VPC)

Deploy in your own infrastructure for full data sovereignty. All prompt data, responses, and audit logs remain within your network boundary. Required for some regulated industries (healthcare, financial services, government). Higher operational overhead but maximum control.

Hybrid

Gateway logic runs in your VPC, management plane runs in SaaS. LLM traffic never leaves your infrastructure, but you get managed updates, dashboards, and configuration through a cloud control plane. Balances data sovereignty with operational convenience.

An LLM gateway is only as valuable as the visibility it provides. Governance requires observability, and observability requires the right metrics, audit trails, and integration points to turn raw data into actionable insights.

Key Metrics

Monitor latency percentiles (P50, P95, P99) to catch performance degradation before it impacts users. Track tokens per second for capacity planning. Measure cost per request for budget attribution. Watch error rates by provider and model to identify reliability issues. Monitor cache hit rates to quantify cost savings from deduplication.

Audit Trail Requirements

Every request through the gateway should log: who called the model (user or agent identity), which model was invoked, what the prompt contained, what the response contained, how much it cost in tokens and dollars, which application originated the request, and the full request-response latency breakdown. This audit trail is the foundation for compliance reporting.

Integration Points

Enterprise observability stacks require the gateway to export data in standard formats. Prometheus metrics for dashboarding in Grafana. OpenTelemetry traces for distributed tracing. Structured logs for SIEM integration with Splunk or Datadog. Automated SOC 2 evidence collection and HIPAA audit log generation for compliance workflows.

Governance built in, not bolted on

Axiom's LLM Gateway ships with governance as a first-class feature. Every request is automatically logged, cost-tracked, and policy-checked. Unified dashboards show real-time cost attribution, latency metrics, and compliance posture across all providers — no additional tooling or integration required.

Explore LLM Gateway

Migrating from direct API keys to a governed LLM gateway follows a six-step process. The key principle is incremental adoption: start with visibility, then layer on controls. No application code changes should be required if the gateway provides an OpenAI-compatible API.

Step 1: Inventory

List all applications making LLM API calls. Check environment variables, secrets managers, CI/CD configurations, and developer workstations. The goal is a complete map of every AI touchpoint in your organization.

Step 2: Centralize Credentials

Move all provider API keys from individual applications to the gateway. Applications should authenticate with the gateway using internal tokens — never hold provider credentials directly.

Step 3: Point Clients at Gateway

Change the base URL from api.openai.com to gateway.your-domain.com. With an OpenAI-compatible API, this is the only change needed — no SDK updates, no code modifications.

Step 4: Enable Monitoring

Start with observe-only mode. Let all traffic flow through without blocking policies. This phase builds your baseline: typical usage patterns, cost distribution, latency profiles, and model preferences across teams.

Step 5: Add Policies Gradually

Layer policies incrementally: rate limits first, then cost alerts, then PII redaction, then access controls. Each policy should run in audit mode (log violations without blocking) before switching to enforcement mode.

Step 6: Validate

Confirm all traffic flows through the gateway. Verify no direct provider connections remain. Run a final audit of API keys — any keys still embedded in applications should be rotated and removed.

Axiom's LLM Gateway is a Kubernetes-native reverse proxy purpose-built for enterprise AI traffic. It provides an OpenAI-compatible API that applications can adopt with a single base URL change, while delivering the full governance stack: intelligent routing, credential management, token-aware rate limiting, prompt caching, PII redaction, and real-time cost attribution.

What sets it apart is integration with the broader Axiom platform. The LLM Gateway works alongside the MCP Gateway for agent tool governance and the A2A Gateway for multi-agent communication governance. Together, they provide a complete AI governance stack that covers inference, tool access, and agent-to-agent interactions from a single control plane.

Deployment options include SaaS, self-hosted in your VPC, or hybrid — with Helm charts for Kubernetes and GitOps-compatible configuration. The gateway is designed for zero-friction adoption: point your existing OpenAI SDK clients at the gateway endpoint, and governance is active immediately.

Full-stack AI governance from a single platform

From LLM inference to agent tool calls to multi-agent communication — Axiom governs every layer of your AI stack. Deploy the LLM Gateway in minutes, then extend with MCP and A2A gateways as your AI infrastructure matures.

See LLM Gateway details