Aegisora vs Switchyard: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Aegisora and Switchyard — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Aegisora
Aegisora
Narrow control plane for AI agents: enforce least-privilege on tool/API calls, block PII leaks, and generate human-readable audit logs.
Key features
- Sidecar proxy: deploys inside your VPC as a lightweight proxy so raw payloads and PII never touch third-party infrastructure.
- Least-privilege enforcement: block unauthorized tool calls and API requests based on runtime policies.
- Fail-closed compliance: on proxy or network disruption, policies default to fail-closed to prevent unverified execution.
- Immutable audit logs: every tool call, payload interception, and policy decision recorded as structured, human-readable logs.
- PII masking: intercept and mask PII in agent payloads before they leave your perimeter.
- Broad LLM & tool integrations: works with OpenAI, Anthropic, Azure AI, AWS Bedrock, GitHub, Slack, and Vercel out of the box.
- Governance controls: rule-based and dynamic policy libraries, plus RBAC/SSO and multi-tier approvals in Enterprise.
- Compliance-ready: SOC 2 / ISO compliance suite and custom SIEM integration in the Enterprise VPC tier.
Best for
- Security team enforces which tools an internal agent can call and blocks anything outside its least-privilege scope.
- Compliance officer generates SOC 2 / ISO evidence from immutable audit trails of every agent decision.
- CISO deploys AI copilots in a regulated environment while keeping raw payloads and PII inside the corporate VPC.
- Platform team masks PII in outgoing prompts before they hit a third-party LLM provider.
- SecOps investigates an agent incident using a full timeline of tool calls, prompts, and policy decisions.
- Enterprise standardizes runtime governance across many agents, LLM providers, and business units.
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.