Oxlo.ai vs Switchyard: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Oxlo.ai and Switchyard — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Oxlo.ai
Oxlo
Privacy-first inference platform to run Kimi K2.6, DeepSeek, and 45+ open-source models on a flat-priced, OpenAI-compatible API.
Key features
- OpenAI-compatible API: Drop-in API that serves 45+ open-source models so existing OpenAI client code works without rewrites.
- Flat monthly pricing: A fixed subscription instead of per-token billing, keeping inference bills predictable at any scale.
- Privacy-first inference: Zero data retention and no training on your data, so prompts and outputs stay private.
- Unlimited agentic tool calls: Run agent workflows with tool calling without metered per-call charges.
- Secure failover: Automatic routing and failover across models to keep agents reliable under load.
- Cost calculator: Compare your current inference spend against Oxlo and competing providers before committing.
- Broad model catalog: Access frontier open models like Kimi K2.6, DeepSeek V4 Flash, GLM-5, Llama, and Qwen plus Whisper, TTS, and image models.
Best for
- Building chatbots and AI assistants for support and internal tools on open models.
- Powering document Q&A and retrieval-augmented generation over PDFs and knowledge bases.
- Generating, rewriting, and summarizing text inside apps and internal systems.
- Running image understanding tasks such as classification and object detection.
- Cutting and stabilizing inference costs for AI teams with high, variable token usage.
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.