Quaso vs Switchyard: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Quaso and Switchyard — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Quaso
Notte Labs
Notte is browser infrastructure for AI agents: fast concurrent browser sessions, prompt-driven browser agents, and serverless browser functions.
Key features
- Browser Sessions at Scale: Launch 1000+ concurrent browser instances on a global edge network with sub-50 ms latency and 99.9% uptime.
- Prompt-driven Browser Agents: Describe a task in one prompt, no selectors or maintenance, with a reported >90% success rate and 3-line setup.
- Browser Functions Runtime: Deploy serverless scripts colocated with browsers for 0 ms network hop and <200 ms cold start, with cron scheduling.
- Agent Vaults: Encrypted AES-256 credential storage with scoped-per-session access, automatic rotation, and full access log for agent workflows.
- Agent Identities: Real dedicated inbox and SMS number per agent to intercept OTPs and pass 2FA on any platform.
- Session Profiles: Save full browser state, auto-persist on exit, and reuse across parallel sessions in a safe read-only mode.
- Drop-in SDK Compatibility: Works with Playwright, Puppeteer, Selenium, browser-use, and Stagehand across Python, TypeScript, Node.js, and Docker.
- Antibot and Residential Proxies: Undetectable browsing with autosolve and a global residential proxy network with fixed IPs or BYO.
Best for
- Automated checkout flows: Have a Browser Agent complete an e-commerce checkout end-to-end without hand-written selectors.
- Invoice and document fetching: Fire a task to pull an invoice or receipt from a vendor portal and hand the file back to the agent.
- Subscription cancellations: Cancel a subscription through the live UI with the agent handling OTPs via Agent Identities.
- Large-scale scraping: Fan out thousands of concurrent Browser Sessions with residential proxies for real-time market data.
- Authenticated agent workflows: Snapshot a logged-in Session Profile once and reuse it across every future run to skip re-authentication.
- Serverless web tasks: Ship a scheduled Browser Function that colocates automation logic with the browser for sub-200 ms cold starts.
- Cross-framework migration: Drop Notte in behind existing Playwright, Puppeteer, or Selenium code without rewriting the stack.
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.