AI Infrastructure alternatives
The most-used ai infrastructure tools compared with Arcade.dev — pricing, strengths, and who each one is best for.
Each tool below is a same-category competitor. Click Compare for a side-by-side breakdown against Arcade.dev.
AWS's fully managed service for accessing foundation models from multiple providers, with agents, guardrails, and knowledge bases built in.
Give an agent a natural-language task and it drives a real browser to completion — open source library, or managed cloud browsers.
Databricks' suite for building, governing, and serving production AI — agents, RAG, fine-tuning, and evaluation — on top of your governed data.
AWS framework-agnostic runtime for deploying, securing, evaluating, and governing production agents at scale.
Microsoft Azure's managed access to OpenAI models, deployed within an enterprise Azure tenant with its security and compliance controls.
A platform for deploying and serving machine-learning models in production, with autoscaling, fast cold starts, and GPU infrastructure managed for you.
A developer-friendly open-source embedding database designed to make building retrieval and RAG prototypes fast and simple.
Gives agents authenticated access to 1,000+ applications, handling OAuth, tool selection, and sandboxed execution so you do not build integrations one at a time.
One AI workflow a week, with the AI Infrastructure that actually ship it — real stacks, real costs, and where each option falls down.
Browse our workflow library to see how each of these tools fits into real SMB automation stacks.