AI Infrastructure alternatives
Google Vertex AI alternatives
The most-used ai infrastructure tools compared with Google Vertex AI — pricing, strengths, and who each one is best for.
The 8 best alternatives to Google Vertex AI
Each tool below is a same-category competitor. Click Compare for a side-by-side breakdown against Google Vertex AI.
Amazon BedrockPaidAWS's fully managed service for accessing foundation models from multiple providers, with agents, guardrails, and knowledge bases built in.
- Multiple model providers through one managed service
- Stays inside AWS security, IAM, and compliance
Databricks Mosaic AIEnterpriseDatabricks' suite for building, governing, and serving production AI — agents, RAG, fine-tuning, and evaluation — on top of your governed data.
- AI built directly on governed lakehouse data
- Unity Catalog lineage and access control extend to AI
Microsoft Azure's managed access to OpenAI models, deployed within an enterprise Azure tenant with its security and compliance controls.
- OpenAI models inside the Azure governance boundary
- Private networking and Entra ID identity
BasetenPaidA platform for deploying and serving machine-learning models in production, with autoscaling, fast cold starts, and GPU infrastructure managed for you.
- Production model serving without managing GPUs
- Autoscaling with fast cold starts
ChromaFreemiumA developer-friendly open-source embedding database designed to make building retrieval and RAG prototypes fast and simple.
- Extremely fast to get started
- Minimal setup and clean Python API
GroqFreemiumAn inference provider whose custom LPU hardware delivers exceptionally low-latency responses for open-weight models.
- Exceptional inference speed and low latency
- OpenAI-compatible API, easy to adopt
LiteLLMFreemiumAn open-source LLM gateway that gives you one consistent API and proxy across 100+ model providers, with key management and spend tracking.
- One consistent API across 100+ providers
- Proxy adds key management, budgets, and spend logging
ModalFreemiumA serverless cloud for running AI and data workloads — define infrastructure in Python and get on-demand GPUs without managing servers.
- Define and scale infrastructure directly in Python
- On-demand GPUs with no cluster management
Not sure which is the right fit?
Browse our workflow library to see how each of these tools fits into real SMB automation stacks.