Open source alternative to Microsoft Azure AI Foundry and AWS Bedrock
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Build enterprise AI agents on Google Cloud: Apache-2.0 sample notebooks, the Agent Development Kit, RAG Engine grounding, and managed deployment tools.
The Agent Development Kit and Agent Engine deployment path fit teams that need to ship AI agents as production services on GCP, with built-in scaling, session management, and traces for debugging.
Skip if your infrastructure is on AWS or Azure, or if you need a self-hosted orchestration layer. The managed platform services are GCP-specific.
RAG Engine connects private enterprise data to Gemini models with low-latency retrieval and semantic governance via Agent Gateway. Teams handling sensitive data benefit from Google Cloud IAM integration for access control.
Skip if your grounding source is a public dataset or standard vector database already managed elsewhere; the RAG Engine adds value primarily for private, changing enterprise data at scale.
The 17,000+ star repository provides Jupyter notebooks covering Gemini model capabilities, function calling, multimodal inputs, and agent reasoning patterns. Each notebook runs in Google Colab with no local server setup.
Skip if you need offline or air-gapped development; Colab and Cloud Workbench require internet access and a Google account.
Building production AI agents requires assembling multiple layers: a model API, grounding infrastructure for private data, multi-agent orchestration with state management, an evaluation framework to measure quality before shipping, and deployment tooling that handles scaling and observability. Most cloud AI platforms expose these layers as separate services with no reference showing how they connect. Teams end up writing the glue code themselves, often discovering gaps in observability or memory management only after the system is in production.
The specific pain is coordination: an agent that hallucinates because it has no grounding, or one that fails silently in a multi-step reasoning loop, is expensive to debug without traces. Google Cloud's platform components (RAG Engine, Agent Gateway, Memory Bank, evaluation suite) address each layer, but the connection points between them are not obvious from documentation alone. Sample code showing real end-to-end agent workflows is the gap this repository fills.
Modular Python framework for building agents that handle tool calling, multi-turn sessions, memory, and multi-agent reasoning loops. Model-agnostic: works with Gemini, Claude, Grok, Mistral, and open-weight models like Llama and DeepSeek available through the platform. Designed to feel like standard software development rather than ML experimentation.
Connects enterprise data to Gemini models through retrieval-augmented generation, reducing hallucinations by giving the model access to relevant internal documents at query time. Vector Search provides the index layer with auto-tuned storage and retrieval.
Deploys agents to a fully managed Google Cloud service using the Python SDK, source files, or container images. Manages scaling, session state for multi-turn conversations, and resource allocation without manual infrastructure configuration.
Traces debug complex reasoning loops and tool calls with detailed execution logs. The evaluation suite supports offline runs against static datasets and simulated user interactions, plus online monitoring in production. Topology visualization shows agent architectures and dependency graphs.
Centralized gateway for routing, securing, and monitoring agent traffic with configurable policies for safe and compliant behavior. Includes content security monitoring for agent inputs and outputs, and IAM integration for access control across projects and organizations.
Both platforms target enterprise AI agent development with managed infrastructure, model access, and deployment tooling. Azure AI Foundry is Microsoft's unified AI platform combining Azure OpenAI Service, model catalog access, and a prompt flow orchestration layer.
| Feature | Gemini Enterprise Agent Platform | Microsoft Azure AI Foundry |
|---|---|---|
| Framework license | Apache-2.0 (ADK) | Proprietary |
| Agent orchestration | Agent Development Kit (open source) | Prompt Flow (managed, closed) |
| Self-hosting | No (GCP managed) | No (Azure managed) |
| First-party models | Gemini 3.1 Pro, Veo 3.1, Lyria 3 | Azure OpenAI (GPT-4o, o3) |
| Partner models | Claude, Grok, Mistral, Llama, DeepSeek | Llama, Mistral, and others |
| Evaluation suite | Offline + online monitoring built in | Azure AI Foundry evaluations |
Teams already in the GCP ecosystem will prefer Gemini Enterprise Agent Platform for its first-party integration with Google models and an open source ADK they can inspect and extend. Azure AI Foundry is the better fit for organizations already on Azure, particularly those using Azure OpenAI Service for GPT-4o access or integrating with Microsoft 365 services.
AWS Bedrock is Amazon's managed ML platform with model access, Knowledge Bases for retrieval, and Agents for Bedrock for multi-step orchestration.
| Feature | Gemini Enterprise Agent Platform | AWS Bedrock |
|---|---|---|
| Framework license | Apache-2.0 (ADK) | Proprietary |
| Agent orchestration | ADK (open source) | Agents for Bedrock (closed) |
| Self-hosting | No (GCP managed) | No (AWS managed) |
| Retrieval | RAG Engine + Vector Search | Knowledge Bases |
| Observability | Traces, topology, online monitoring | Amazon CloudWatch integration |
| Media generation | Veo (video), Lyria (audio) | Not offered |
AWS Bedrock serves teams already on AWS with a wide model catalog including Amazon Titan and third-party models. Gemini Enterprise Agent Platform provides a more integrated full-stack agent toolchain with the ADK as an open source component, plus specialized media generation models (Veo for video, Lyria for audio) that AWS Bedrock does not offer. Teams choosing between them will typically follow their existing cloud provider unless they specifically need Google's model family or the open source ADK.
Install the sample notebooks locally by cloning the repository to run them in Google Colab or Cloud Workbench.
```bash
git clone https://github.com/GoogleCloudPlatform/generative-ai
```No. The README states the repository itself is not an officially supported Google product and the code is for demonstrative purposes only. The underlying Gemini Enterprise Agent Platform is a supported Google Cloud product; the notebooks in this repository are reference material demonstrating how to use it.
ADK is Google's open source, model-agnostic Python framework for building multi-agent systems. It handles tool calling, multi-turn sessions, memory, and agent orchestration. The framework is Apache-2.0 licensed; a companion repository of ADK samples is linked in this repository's README.
No. The managed services (Agent Engine, RAG Engine, Vector Search, Agent Gateway) run on Google Cloud infrastructure. The ADK itself can run locally for development, but deploying agents to Agent Engine requires a Google Cloud account and billing.
The platform includes Claude, Grok, and Mistral as partner models available as managed APIs, plus open-weight models including Llama, DeepSeek, Mistral, and Qwen. Gemini 3.1 Pro and Gemini 3 Flash are the first-party models optimized for agentic workflows.
Both provide managed infrastructure for building AI agents with model access, retrieval, and deployment. AWS Bedrock focuses on multi-model access across AWS-partner models with tight AWS service integration. Gemini Enterprise Agent Platform offers tighter integration with Google's model family and an open source ADK; AWS Bedrock's orchestration layer is proprietary with no open source equivalent.
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