Graphis vs Switchyard: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Graphis and Switchyard — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Graphis
Graphis / Graphis AI
All-in-one AI workspace that helps designers, marketers, and creators manage AI-driven content projects and collaboration.
Key features
- AI Content Project Management: Centralized workspace to create, organize, and track AI-driven content projects for campaigns and client work.
- Team Collaboration: Shared project spaces and access controls that enable creatives and agency teams to work together on prompts, assets, and iterations.
- Creative Workflow Consolidation: Brings prompts, outputs, assets, and project history into a single environment to streamline iteration and review.
- Multi-role Support for Agencies: Designed to support agency workflows with organization-level project coordination and client-focused content pipelines.
- Built-for-Creatives UX: Interface and tooling crafted by creatives to match the needs of designers, marketers, and content creators integrating AI into their processes.
- Simple Authentication and Access: Supports modern sign-in flows (e.g., Google login) for quick team onboarding and access management.
- Web-based collaborative workspace for creative teams
- AI content project management and organization
- Tools to integrate generative workflows into design and marketing tasks
- Team accounts and authentication with Google single sign-on
- Centralized management of creative projects and assets
Best for
- Agency Campaign Management: Plan and manage AI-generated assets across client campaigns, keeping briefs, prompts, iterations, and final outputs organized.
- Social Content Production: Rapidly generate and iterate social posts, captions, and visuals with a shared workspace for marketers and designers to review.
- Creative Iteration and Review: Store AI outputs and version history to enable designers to compare generations, refine prompts, and finalize assets.
- Cross-functional Team Collaboration: Allow designers, copywriters, and marketers to co-manage projects, comment on outputs, and align creative direction.
- Standardizing AI Workflows: Create repeatable processes for prompt reuse, output curation, and asset management to maintain brand consistency.
- Agency Client Delivery: Package and present AI-driven deliverables in organized project spaces for client review and approval.
- Agency-level management of AI-generated campaigns and creative projects
- Design teams incorporating generative content into production workflows
- Marketing teams producing and iterating on AI-assisted assets and copy
- Cross-functional collaboration on content projects with centralized access
- Organizing and tracking AI-driven creative deliverables for clients
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.