AI Infrastructure alternatives
The most-used ai infrastructure tools compared with E2B — 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 E2B.
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.
One control point for the actions agents take in real systems — user-scoped auth, 8,000+ permission-aware tools, and an audit trail per action.
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.
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.