ngrok AI Gateway vs Router by Ramp: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ngrok AI Gateway and Router by Ramp — 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.
Router by Ramp
Ramp
Ramp's LLM gateway routes each request to the cheapest model meeting your quality bar, cutting inference costs ~40% behind one endpoint and one bill.
Key features
- Cost-Aware Automatic Routing: Every request is matched to the lowest-cost model that still meets your performance requirements, reported to cut inference spend by about 40% on average.
- One Key for Every Model: Closed and open-source models from vetted providers sit behind a single endpoint, key and invoice.
- Rolling Strategy Updates: New cost-saving routing strategies and newly benchmarked default models roll in automatically without changing your integration.
- Score Versus Spend Reporting: Built-in benchmarking shows metric distributions and model summaries so you can see quality and cost side by side.
- Flex Tier Routing Share: A tunable split between default and flexible routing lets you dial how aggressively requests are shifted to cheaper models.
- US-Hosted Providers with ZDR: All vetted providers are US-hosted, with zero-data-retention options for sensitive workloads.
- Switchyard Integration: Works with Switchyard for model and provider routing, surfaced directly in the CLI's cost display.
- One-Command CLI Setup: Install and configure with a single curl command from agents.ramp.com, with an agent-friendly copy-paste flow.
Best for
- Trimming Production Inference Spend: Route high-volume, low-difficulty requests to cheaper models while keeping frontier models for the hard ones — Delphi reports a 92% model cost reduction across billions of tokens.
- Multi-Provider Consolidation: Replace separate OpenAI, Anthropic and open-model integrations with one endpoint and one bill.
- Model Benchmarking Before Migration: Test candidate models against your real workloads and compare score against spend before switching defaults.
- Finance and Engineering Alignment: Give CFOs a single, attributable AI spend line while engineers keep the best model for each workload.
- Compliance-Constrained Deployments: Keep inference on US-hosted providers with zero-data-retention options for regulated data.
- Agent Cost Control: Cap the runaway token spend of long-running agent loops by routing their routine steps to cheaper models automatically.