Bluerails Discovery vs Switchyard: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Bluerails Discovery and Switchyard — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Bluerails Discovery
Bluerails
Bluerails is payment infrastructure for the agentic economy that makes businesses discoverable to AI agents and ready to be paid by them.
Key features
- Agent-Ready Checkout: Accept payments from any AI tool or assistant automatically, with no integration work required.
- Global Settlement: AI tools pay behind the scenes and you receive EUR or USD directly in your bank account.
- AI-Visibility Score: A peer-reviewed discoverability score drawn from 400 samples rather than a single one-off guess.
- Built-In Compliance: Discovery, payments, and settlement run on the rails marketplaces already use, with compliance handled.
Best for
- Publisher Monetization: EU content publishers accepting payments from AI agents for access to their content.
- Hotel Discovery: DACH hotel properties becoming discoverable and bookable by AI agents.
- SaaS Agent Commerce: SaaS tools getting paid automatically when AI assistants use them.
- Agentic Outreach: Shopify stores reaching customers through AI-powered channels.
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.