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Bedrock gives you model access and building blocks. Rebase gives you

the enterprise AI platform your organization actually needs.

AWS Bedrock, Google Vertex AI, and Azure AI Studio provide model APIs, agent frameworks, and cloud infrastructure. Rebase provides the enterprise intelligence layer on top: cross-system context, persistent memory, deployed agents, and organizational AI governance. One is infrastructure you build on. The other is the platform you use.

Cloud AI services are building materials. Enterprise AI infrastructure is the building.

What Bedrock Provides

AWS Bedrock is a strong product. It offers access to hundreds of foundation models and AgentCore provides Runtime, Gateway, Memory, Identity, Observability, and Evaluations. But building an enterprise AI platform from these components is a major engineering undertaking. Bedrock provides the tools. Your team builds the platform.

What Rebase Does

Rebase is Enterprise AI Infrastructure. Context Engine connects your systems and builds a live knowledge graph. Agent Studio deploys and manages agents. Memory compounds organizational knowledge. AI Gateway provides model-agnostic access to Bedrock or any provider. You get the finished platform — not the building materials.

Head-to-Head Comparison

Dimension

AWS Bedrock + AgentCore

Rebase

What It Is

Cloud AI services: model access, agent building framework, deployment infrastructure. Developer tools for building AI applications.

Enterprise AI platform: cross-system intelligence, agent deployment, persistent memory, model gateway, governance. Ready to use, not ready to build.

Model Access

Hundreds of FMs. Claude, GPT, Llama, Mistral, Cohere, Amazon Nova, and more. Fine-tuning and evaluation tools.

AI Gateway: model-agnostic, any provider including Bedrock, BYOK. Route by cost, capability, compliance. Provider-independent.

Agent Building

AgentCore: Runtime, Gateway, Memory, Identity, Observability, Evaluations, Policy. You design, build, and maintain the agents.

Agent Studio: build, deploy, manage agents with human-in-the-loop. Agents built on Context Engine intelligence. Platform manages the lifecycle.

Intelligence Layer

None. Bedrock provides model access, not organizational intelligence. Your team builds the knowledge representation and system connections.

Context Engine: connects 50+ systems, builds live knowledge graph mapping relationships, dependencies, ownership. Intelligence is the product.

System Integration

AgentCore Gateway connects to tools via MCP, Lambda, APIs. You build and maintain each integration.

Context Engine connects via API, CLI, MCP with pre-built connectors for 50+ enterprise systems. Integrations maintained by the platform.

Memory

AgentCore Memory: session and long-term memory for individual agents. You design the memory architecture.

Organizational Memory: persistent, private, shared across agents. Intelligence compounds at the organizational level. Architecture built-in.

Engineering Requirement

High. 2-4 senior engineers for 6-12 months to build, plus ongoing maintenance. Your team owns architecture, integrations, operations.

Low. Connect systems, configure agents, deploy. Platform handles architecture, integrations, operations. Your team focuses on use cases.

Cloud Dependency

AWS-only. Deep integration with AWS services. Locked to AWS ecosystem.

Cloud-agnostic. BYOC on any cloud, on-prem, air-gapped. Can use Bedrock as one model source among many.

Time to Value

Months. Building a production enterprise AI system on Bedrock is a significant engineering project.

Weeks. Connect systems, deploy agents, start generating value.

Best For

Engineering teams building custom AI applications. Companies with dedicated AI engineering capacity who want maximum control.

Organizations that need enterprise AI Infrastructure without building it from scratch. Companies that want results, not a construction project.

Head-to-Head Comparison

Dimension

AWS Bedrock + AgentCore

Rebase

What It Is

Cloud AI services: model access, agent building framework, deployment infrastructure. Developer tools for building AI applications.

Enterprise AI platform: cross-system intelligence, agent deployment, persistent memory, model gateway, governance. Ready to use, not ready to build.

Model Access

Hundreds of FMs. Claude, GPT, Llama, Mistral, Cohere, Amazon Nova, and more. Fine-tuning and evaluation tools.

AI Gateway: model-agnostic, any provider including Bedrock, BYOK. Route by cost, capability, compliance. Provider-independent.

Agent Building

AgentCore: Runtime, Gateway, Memory, Identity, Observability, Evaluations, Policy. You design, build, and maintain the agents.

Agent Studio: build, deploy, manage agents with human-in-the-loop. Agents built on Context Engine intelligence. Platform manages the lifecycle.

Intelligence Layer

None. Bedrock provides model access, not organizational intelligence. Your team builds the knowledge representation and system connections.

Context Engine: connects 50+ systems, builds live knowledge graph mapping relationships, dependencies, ownership. Intelligence is the product.

System Integration

AgentCore Gateway connects to tools via MCP, Lambda, APIs. You build and maintain each integration.

Context Engine connects via API, CLI, MCP with pre-built connectors for 50+ enterprise systems. Integrations maintained by the platform.

Memory

AgentCore Memory: session and long-term memory for individual agents. You design the memory architecture.

Organizational Memory: persistent, private, shared across agents. Intelligence compounds at the organizational level. Architecture built-in.

Engineering Requirement

High. 2-4 senior engineers for 6-12 months to build, plus ongoing maintenance. Your team owns architecture, integrations, operations.

Low. Connect systems, configure agents, deploy. Platform handles architecture, integrations, operations. Your team focuses on use cases.

Cloud Dependency

AWS-only. Deep integration with AWS services. Locked to AWS ecosystem.

Cloud-agnostic. BYOC on any cloud, on-prem, air-gapped. Can use Bedrock as one model source among many.

Time to Value

Months. Building a production enterprise AI system on Bedrock is a significant engineering project.

Weeks. Connect systems, deploy agents, start generating value.

Best For

Engineering teams building custom AI applications. Companies with dedicated AI engineering capacity who want maximum control.

Organizations that need enterprise AI Infrastructure without building it from scratch. Companies that want results, not a construction project.

Why Enterprises Choose Rebase

Platform vs Building Blocks

Bedrock provides excellent AI building blocks: model access, agent runtime, tool gateway, memory services. Building an enterprise AI platform from components is a major undertaking. Rebase IS the enterprise platform. Context Engine, Agent Studio, Memory, AI Gateway — all working together as a cohesive system.

Cross-System Intelligence

Bedrock provides access to the best foundation models. But a model without organizational context is just a smart tool. Rebase's Context Engine connects your actual systems and builds a live knowledge graph. When a Rebase agent answers a question, it draws on real-time intelligence from across your entire operation.

Cloud-Agnostic vs Cloud-Locked

Bedrock is an AWS service. Your AI capabilities are tied to your AWS infrastructure. Rebase is cloud-agnostic. Deploy on AWS, Azure, GCP, on-prem, or air-gapped. Use Bedrock models through AI Gateway alongside models from any other provider. No vendor lock-in for your AI strategy.

Engineering Cost: Build vs Buy

Building on Bedrock costs 2-4 senior engineers for 6-12 months, plus ongoing maintenance. That's a permanent commitment. Deploying Rebase costs weeks and doesn't lock your engineers into infrastructure maintenance forever. Your team focuses on product features, not platform building.

The Broader Cloud AI Landscape

This applies equally to Google Vertex AI and Azure AI Studio. All cloud AI services are developer tools. None are enterprise platforms. Rebase works across all of them — using models from any provider while providing the organizational intelligence layer that no cloud service includes.

Rebase doesn't replace Bedrock. Rebase makes Bedrock useful at the organizational level.

The ideal architecture for many enterprises: run Rebase on AWS infrastructure, use Bedrock models through Rebase's AI Gateway alongside models from other providers, leverage AWS security and compliance capabilities, and add Rebase's Context Engine, Agent Studio, and Memory on top.

Your engineering team uses Bedrock for custom AI applications and specialized workloads. Rebase handles enterprise-wide AI Infrastructure: cross-system intelligence, business agents, organizational memory, governance. Both run on AWS. Both serve different needs. They're complementary layers of your AI architecture.

The real question isn't build vs buy. It's: should your engineers spend 12 months building infrastructure that exists, or 12 months building AI capabilities that differentiate your business?

Better Together Scenario

Rebase + Bedrock deployed on AWS infrastructure. Bedrock for model access and custom ML workloads. Rebase for enterprise AI platform and organizational intelligence. They work as a complete stack.

When Each Makes Sense

Bedrock Makes Sense When

Engineering team has dedicated AI capability and wants maximum control
Building custom ML models or fine-tuning proprietary models
Single-use-case AI applications vs enterprise-wide transformation
Your team has 6-12 months and 2-4 senior engineers available
AWS is your standard infrastructure

Rebase Makes Sense When

You need enterprise AI Infrastructure without building it from scratch
Cross-system intelligence and organizational context matter
Multiple departments need AI capabilities, not just one team
Time to value is critical (weeks, not months)
Multi-cloud or cloud-agnostic requirements
Data governance and compliance are priorities

When Each Makes Sense

Bedrock Makes Sense When

Engineering team has dedicated AI capability and wants maximum control
Building custom ML models or fine-tuning proprietary models
Single-use-case AI applications vs enterprise-wide transformation
Your team has 6-12 months and 2-4 senior engineers available
AWS is your standard infrastructure

Rebase Makes Sense When

You need enterprise AI Infrastructure without building it from scratch
Cross-system intelligence and organizational context matter
Multiple departments need AI capabilities, not just one team
Time to value is critical (weeks, not months)
Multi-cloud or cloud-agnostic requirements
Data governance and compliance are priorities

Why Teams Choose Rebase

DEPLOY IN WEEKS, NOT MONTHS

Production-ready enterprise AI infrastructure from day one. No 6-12 month engineering project. Your team deploys in weeks and starts generating value immediately.

WORKS WITH ANY CLOUD AI SERVICE

Bedrock, Vertex AI, Azure AI Studio — Rebase works with all of them. Use Bedrock for model access, add Rebase for the enterprise layer. Cloud-agnostic approach means you're not locked into one provider's ecosystem.

ENTERPRISE READY

SOC 2 Type II. 50+ native integrations. HIPAA and GDPR ready. BYOC, on-prem, or air-gapped deployment. Your data never leaves your environment.

FAQS

REBASE vs AWS BEDROCK / CLOUD AI

Our engineering team says they can build everything on Bedrock. Should we still consider Rebase?

A: They can build it. The questions are: how long will it take, how much will it cost, and will they maintain it forever? Building enterprise AI infrastructure on Bedrock is a 6-12 month project requiring 2-4 senior engineers. Rebase deploys in weeks. Your engineers can focus on building product features instead of building and maintaining AI infrastructure.

We're an AWS shop. Doesn't Bedrock make more sense for us?

Can Rebase work alongside our existing Bedrock setup?

What about Google Vertex AI or Azure AI Studio instead of Bedrock?

How much engineering effort does Bedrock actually require vs Rebase?

Bedrock's AgentCore has Memory, Gateway, Runtime, Identity. How is Rebase different?

Ready to build on
AI-native infrastructure?

Open standards. Your cloud. Live in weeks. No proprietary lock-in.

Ready to build on
AI-native infrastructure?

Open standards. Your cloud. Live in weeks. No proprietary lock-in.

Ready to become

an AI-first company

Ready to become

an AI-first company

Ready to become

an AI-first company

Privacy

Terms

Security

Bedrock gives you model access and building blocks. Rebase gives you

the enterprise AI platform your organization actually needs.

Cloud AI services are building materials. Enterprise AI infrastructure is the building.

Cloud AI services are building materials. Enterprise AI infrastructure is the building.

What Bedrock Provides

What Rebase Does

Head-to-Head Comparison

Head-to-Head Comparison

Head-to-Head Comparison

Why Enterprises Choose Rebase

Why Enterprises Choose Rebase

Platform vs Building Blocks

Cross-System Intelligence

Cloud-Agnostic vs Cloud-Locked

Engineering Cost: Build vs Buy

The Broader Cloud AI Landscape

Rebase doesn't replace Bedrock. Rebase makes Bedrock useful at the organizational level.

Rebase doesn't replace Bedrock. Rebase makes Bedrock useful at the organizational level.

Better Together Scenario

When Each Makes Sense

When Each Makes Sense

Bedrock Makes Sense When

Rebase Makes Sense When

When Each Makes Sense

Bedrock Makes Sense When

Rebase Makes Sense When

Why Teams Choose Rebase

Why Teams Choose Rebase

DEPLOY IN WEEKS, NOT MONTHS

WORKS WITH ANY CLOUD AI SERVICE

ENTERPRISE READY

REBASE vs AWS BEDROCK / CLOUD AI

REBASE vs AWS BEDROCK / CLOUD AI

Our engineering team says they can build everything on Bedrock. Should we still consider Rebase?

We're an AWS shop. Doesn't Bedrock make more sense for us?

Can Rebase work alongside our existing Bedrock setup?

What about Google Vertex AI or Azure AI Studio instead of Bedrock?

How much engineering effort does Bedrock actually require vs Rebase?

Bedrock's AgentCore has Memory, Gateway, Runtime, Identity. How is Rebase different?

Ready to build on AI-native infrastructure?

Ready to build on AI-native infrastructure?

Ready to build on AI-native infrastructure?

Ready to become

an AI-first company

Ready to become

an AI-first company

Ready to become

an AI-first company

Ready to build on
AI-native infrastructure?

Ready to build on
AI-native infrastructure?

Ready to build on
AI-native infrastructure?