AI Gateway Patterns: Building a Single Inference API in 2026
Production AI systems route across many models. The gateway pattern that abstracts provider differences without lock-in.
Production AI systems in 2026 rarely use a single model from a single provider. Different workloads need different models — frontier for complex reasoning, smaller fine-tuned models for routine tasks, vision models for multimodal work, embedding models for retrieval. Different providers have different strengths, pricing, regional availability, and reliability characteristics. Going direct to each provider produces tightly-coupled code that’s hard to change, hard to optimize, and hard to govern.
The AI gateway pattern abstracts provider differences behind a single inference API. By 2026 the pattern is standard for any meaningful-scale AI deployment.
What an AI gateway does#
A typical AI gateway provides several capabilities.
Unified API. Applications call the gateway with a consistent interface; the gateway handles provider-specific request formatting, authentication, and response normalization. Switching providers is a configuration change rather than a code change.
Routing. Different requests can route to different models based on workload characteristics, cost optimization, quality requirements, latency requirements, or compliance constraints. The routing logic lives in the gateway, not in every application.
Caching. Semantic caching, prompt caching, and response caching all live at the gateway. Multiple applications share the cache; cache effectiveness improves with consolidation.
Rate limiting and quotas. Provider rate limits affect application behavior. The gateway enforces application-level quotas while managing provider-level rate limits.
Cost tracking. Per-application, per-team, per-customer cost attribution becomes possible when all inference flows through the gateway.
Observability. Centralized logging, tracing, and metrics. Latency distributions, token counts, error rates — all measurable consistently.
Fallback and failover. When a primary provider has issues, traffic fails over to alternatives transparently.
Security and governance. API key management, prompt filtering, PII detection, output filtering — all centralized.
Compliance. Audit logging for regulated use cases. Data residency enforcement. PII handling.
The vendor landscape#
Several products implement AI gateway patterns:
Portkey — purpose-built AI gateway with routing, caching, and observability.
Helicone — gateway plus observability with strong dashboard.
LiteLLM — open-source library that provides unified interface, often used as building block.
OpenRouter — gateway service with broad model coverage.
Cloudflare AI Gateway — Cloudflare’s offering.
Aporia — AI security and governance gateway.
Custom — many sophisticated teams build their own gateway, using LiteLLM or direct provider SDKs underneath.
For most production deployments, vendor-provided gateways are reasonable. For teams with specific routing, governance, or cost requirements, custom builds make sense.
The routing patterns#
Several routing patterns are common.
Cost-aware routing. Simple queries route to cheaper models; complex queries route to frontier models. The routing decision can be heuristic (input length, query type detection) or learned (a classifier trained on which model handles which queries well).
Capability routing. Vision queries to vision models, audio to audio models, code to code-tuned models. The routing matches request modality to model capability.
Latency routing. Time-sensitive requests route to faster models; background processing routes to slower-but-cheaper alternatives.
Quality routing. High-stakes requests (final customer-facing output) route to frontier models; low-stakes (internal drafts) route to cheaper alternatives.
Compliance routing. EU customer data routes through EU-resident models; healthcare data routes through HIPAA-compliant providers; defense workloads route through US-sovereign infrastructure.
Cascading retry. Try cheap fast first; escalate to more capable model if quality is insufficient. Particularly effective when “quality is insufficient” can be detected programmatically.
A/B routing. Some percentage of traffic to candidate models for evaluation. Critical for model upgrades.
The cache strategy#
Caching at the gateway produces substantial cost savings.
Exact-match cache — identical requests return cached responses. Effective for chat-style applications with substantial query repetition.
Semantic cache — similar requests (above a similarity threshold) return cached responses. Cost more to evaluate but catch more reuse.
Prompt caching — the major frontier providers offer prompt caching for repeated prefixes. The gateway can manage prompt structure to maximize cache hits.
Response caching with TTL — for content that’s reasonable to cache for hours or days, TTL-based caching dramatically reduces repeat inference.
The cost dimension#
AI gateways pay for themselves quickly in production deployments.
A typical mid-sized AI deployment might spend $50-200K/month on inference. Gateway-level optimizations — caching, cost-aware routing, request consolidation — typically reduce this 30-50% over time. The gateway itself costs a fraction of the savings.
What we typically see at clients#
Common patterns:
Direct provider calls everywhere. Application code calls OpenAI or Anthropic directly. Switching providers requires touching every application. Cost is opaque. Switching cost is high.
Gateway built but not used. The team built a gateway but new applications keep going direct because it’s easier short-term. The gateway atrophies.
No routing logic. All requests go to the most-expensive model regardless of complexity. Cost is 2-3x what it could be.
No caching. Every request hits the provider. Cache-hit-rate-of-zero applications run.
The fixes are straightforward but require discipline.
Where pdpspectra fits#
Our AI engineering practice builds gateway architecture into client engagements. Either deploying a vendor gateway or building custom infrastructure depending on requirements.
Related reading: the LLM router pattern post, the LLM cost optimization post, and the prompt caching post.
AI gateway is now standard production infrastructure. Talk to our team about your AI architecture.