LLM Routing Layer — LiteLLM, OpenRouter, Portkey

> Provider lock-in is expensive. Different tool-calling workloads suit different models. Routing gateways give one API surface, retries, failover, cost tracking, and guardrails. Three archetypes dominate 2026: LiteLLM (open-source self-hosted), OpenRouter (managed SaaS), Portkey (production-grade, open-sourced in March 2026). This lesson names the decision criteria and walks a stdlib routing gateway.

Type: Learn

Languages: Python (stdlib, routing + failover + cost tracker)

Prerequisites: Phase 13 · 02 (function calling), Phase 13 · 17 (gateways)

Time: ~45 minutes

Learning Objectives

• Distinguish self-hosted, managed, and production-grade routing options.
• Implement a fallback chain that retries on provider failures in a defined priority order.
• Track per-request cost and token usage across providers.
• Decide between LiteLLM, OpenRouter, and Portkey for a given production constraint.

The Problem

Scenarios where provider routing matters:

1. Cost. Claude Sonnet costs 3x what Haiku costs. For a triage task, Haiku is enough; for a synthesis task, Sonnet is worth it. Route per-request.

1. Failover. OpenAI has a bad hour. Every request fails. You want automatic fallback to Anthropic without redeploying.

1. Latency. A live chat UI needs fast time-to-first-token. A batch summarizer does not. Route by latency SLA.

1. Compliance. EU users must stay in EU regions. Route by region.

1. Experimentation. A/B two models on the same workload. Route by test bucket.

Hand-coding all of this per integration is repetitive. A routing gateway gives one OpenAI-compatible API and handles the rest.

The Concept

OpenAI-compatible proxy shape

Everyone speaks OpenAI-shape. The routing gateway exposes /v1/chat/completions, accepts the OpenAI schema, and internally proxies to Anthropic / Gemini / Cohere / Ollama / anything. The client does not care.

Model aliases

Instead of claude-3-5-sonnet-20251022, your code says our_smart_model. The gateway maps aliases to real models. When Anthropic ships Claude 4, you change the alias server-side; your code does not touch a thing.

Fallback chains

primary: openai/gpt-4o
on 5xx: anthropic/claude-3-5-sonnet
on 5xx: google/gemini-1.5-pro
on 5xx: refuse

Gateways define this in a config. Retries count against a budget so fallback cascades do not explode cost.

Semantic caching

Identical-or-near-identical prompts hit a cache instead of the provider. Savings on repeated agent loops can be 30 to 60 percent. Keys are embedding-based; near-identical prompts share a cache slot.

Guardrails

Gateway-level:

• PII redaction. Regex or ML-based pass before sending prompts.
• Policy violations. Reject prompts with prohibited content.
• Output filters. Scrub completions for leaks.

Portkey and Kong both ship opinionated guardrails. LiteLLM leaves them optional.

Per-key rate limits

One API key = one team. Per-key budgets prevent one team from consuming the shared quota. Most gateways support this.

Self-hosted vs managed trade-offs

LiteLLM wins when you have an SRE team and want data sovereignty. OpenRouter wins when you want a single subscription and no infra. Portkey wins when you need guardrails and compliance out of the box.

Cost tracking

Every request carries provider, model, input_tokens, output_tokens. Multiply by per-model per-token prices (pulled from a pricing sheet the gateway maintains). Per-user / per-team / per-project aggregation.

MCP plus routing

A gateway can route both LLM calls AND MCP sampling requests. When a sampling request's modelPreferences prefer a specific model, the gateway translates to the right backend. This is where Phase 13 · 17 (MCP gateway) and this lesson's routing gateway sometimes merge into one service.

Routing strategies

• Static priority. First in list; fall back on error.
• Load balancing. Round-robin or weighted.
• Cost-aware. Pick the cheapest model meeting latency / quality.
• Latency-aware. Pick the fastest model in the last N minutes.
• Task-aware. Prompt classifier routes coding to one model, summarization to another.

Use It

code/main.py implements a routing gateway in ~150 lines: accepts OpenAI-shaped requests, translates to per-provider stubs, runs a priority fallback chain, tracks per-request cost, and applies a PII redaction pass on inputs. Run it with three scenarios: normal request, primary-provider outage triggering fallback, PII leakage caught by redaction.

What to look at:

• ROUTES dict: alias -> priority-ordered list of concrete providers.
• Fallback loop retries on 5xx.
• Cost tracker multiplies token usage by per-model rates.
• PII redactor scrubs SSN-shaped patterns before forwarding.

Ship It

This lesson produces outputs/skill-routing-config-designer.md. Given a workload profile (latency, cost, compliance), the skill picks LiteLLM / OpenRouter / Portkey and produces a routing config.

Exercises

1. Run code/main.py. Trigger the outage scenario; confirm fallback lands on the second provider and cost is attributed correctly.

1. Add semantic caching: SHA256 of the prompt is a lookup key; cache hits return instantly. Measure cost savings on a repeated call.

1. Add a prompt classifier that routes "code ..." prompts to an alias favoring intelligence and "summarize ..." prompts to an alias favoring speed.

1. Design per-team budgets: each team has a monthly spend cap; gateway refuses requests once cap is hit. Pick an enforcement granularity (per-request or windowed).

1. Read LiteLLM, OpenRouter, and Portkey docs side by side. Name the one feature each ships that the other two do not.

Key Terms

Further Reading

• LiteLLM — docs — self-hosted routing gateway
• OpenRouter — quickstart — managed routing SaaS
• Portkey — docs — production routing with guardrails
• TrueFoundry — LiteLLM vs OpenRouter — decision guide
• Relayplane — LLM gateway comparison 2026 — vendor survey