ngrok AI Gateway vs Switchyard: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ngrok AI Gateway and Switchyard — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
ngrok AI Gateway
ngrok
Unified LLM gateway that routes any SDK to public providers, custom endpoints, and self-hosted models behind one URL and one key.
Key features
- Unified gateway: one URL and one key routes to public LLM providers, custom endpoints, and self-hosted models.
- Drop-in SDKs: swap baseURL to gateway.ngrok.ai and your existing OpenAI / Anthropic / Vercel AI SDK code keeps working.
- Model fallback: specify a primary model plus fallbacks in one call to route through backups when providers fail or throttle.
- Local LLM access: reach self-hosted models over private connectivity without public IPs or inbound ports.
- Bring your own keys: drop in the provider keys you already pay for and route through them at your current rates.
- Access control: manage which apps, users, and keys can hit which models from one place.
- Observability: monitor usage, cost, and traffic across every model and provider in the gateway.
Best for
- AI engineering team standardizes on one base URL so app code no longer needs per-provider integrations.
- Platform team routes production traffic to a self-hosted model with automatic fallback to a public provider on failure.
- Startup consolidates OpenAI, Anthropic, and custom keys behind a single gateway for auditing and cost tracking.
- Enterprise governs which teams and services can call which models via central access controls.
- ML team exposes a local LLM cluster to app teams without opening inbound network ports.
- FinOps lead centralizes LLM spend visibility across projects instead of pulling per-provider dashboards.
Switchyard
NVIDIA
An open-source Rust proxy and library that routes LLM traffic across models and providers while preserving native OpenAI and Anthropic API compatibility.
Key features
- Protocol Translation: Converts between OpenAI Chat Completions, OpenAI Responses and Anthropic Messages formats so clients keep their native API while any backend serves the request.
- Multi-Backend Routing: Spreads traffic across vLLM, NVIDIA NIM, Ollama and any OpenAI-compatible endpoint, letting you point an existing coding agent at an open-source model without changing the agent.
- LLM Classifier Router: Uses request content to decide whether a given turn needs the weak or the strong model tier, cutting spend on turns that do not need frontier capability.
- Stage Router: Routes most turns from signals already in the conversation — tool results, errors, conversation stage — so no extra model call is needed to make the decision.
- Escalation Router: Runs every turn on the weak tier first, then has a judge read that answer and decide whether the same request should be re-sent to the strong tier.
- Random Routing for A/B Tests: Applies a fixed traffic split across targets for benchmarking, baselines and cost experiments.
- Operational Metrics: Exposes Prometheus metrics for requests, errors, latency, token counts and the overhead added by routing itself.
- Server or Library Deployment: Run it as a standalone Rust proxy configured by routes.toml, or embed switchyard-libsy in your own application so it decides the target and hands the model call back to you.
Best for
- Pointing Coding Agents at Open Models: Serve Claude Code or Codex from vLLM, NIM or Ollama without the agent knowing the API changed.
- Cost/Performance Optimization: Send routine turns to a cheap weak-tier model and reserve the strong tier for turns a classifier or judge says need it.
- Model A/B Benchmarking: Split traffic on a fixed ratio across two models to compare quality, latency and cost on real production requests.
- Provider Migration and Failover: Keep application code on one API shape while swapping or mixing the providers behind it.
- Embedding Routing in an Agent Runtime: Drop the routing algorithms into an existing gateway or agent framework via the library path without adopting a new HTTP stack.
- Operational Visibility: Track per-route latency, error rates and token spend through Prometheus to find which routes are actually costing money.