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Your best engineers should build AI that

differentiates your business. Not plumbing.

LangChain for orchestration. CrewAI for agents. Mem0 for memory. LiteLLM for model routing. Six months of integration. And you still don't have enterprise governance, a knowledge graph, or a product roadmap. There's a better way.

Building from open-source frameworks is like assembling a house from separate contractors.

The DIY Framework Stack

LangChain (126K+ GitHub stars) for orchestration. CrewAI for multi-agent coordination. Mem0 for memory. LiteLLM for model routing. LlamaIndex for data indexing. Plus custom glue code for authentication, logging, permissions, and monitoring. Each framework is excellent for its purpose — but together they create 5+ dependencies with different maintainers, different update cycles, and different breaking changes. You're responsible for versioning, compatibility, and maintenance forever.

What Rebase Provides

Single platform: Context Engine (knowledge graph), Agent Studio (no-code + pro-code + MCP support), Memory (persistent organizational intelligence), AI Gateway (any model, BYOC), Auth & Logs (enterprise governance), Sandboxes (safe execution). 50+ native integrations. Production-ready in weeks. Your team deploys in your cloud, on your terms. One vendor, one roadmap, one API — and you focus on building intelligence, not maintaining infrastructure.

Head-to-Head Comparison

Dimension

Build In-House (DIY)

Rebase

Time to Production

6-12 months to build (including integration)

Weeks to deploy

Engineering Cost Year 1

$800K-$1.5M (senior engineers + tooling + opportunity cost)

A fraction of DIY cost. Engineers focus on differentiating AI, not plumbing.

System Connectivity

Custom connectors for each system (CRM, ERP, data warehouse, ticketing, etc.)

50+ native integrations. Real-time sync.

Knowledge Graph

Not included. Custom build required.

Context Engine with cross-domain correlation, ownership mapping, and dependency tracking

Agent Framework

Multiple point solutions (CrewAI, LangChain, custom code)

Agent Studio with no-code builder, pro-code SDK, MCP workspace, and templates

Memory Management

Mem0 or custom implementation

Persistent organizational intelligence with conversation history and context

Model Access

LiteLLM or custom routing. BYOC but unmanaged.

AI Gateway with unified access to any LLM. BYOK. No markup.

Enterprise Governance

Custom build. Auth, audit trails, permissions, compliance are all on you.

Built-in authentication, audit logs, role-based access, compliance readiness

Safe Execution

Sandboxes require custom infrastructure work

Sandboxes with resource limits and permission boundaries

Product Roadmap

Internal platforms stagnate when the team gets pulled to the next priority

Evolving platform with features shipped every sprint

Dependency Risk

5+ dependencies (LangChain, CrewAI, Mem0, LiteLLM, LlamaIndex). Each with its own breaking changes.

Single platform. One vendor, one API, one roadmap.

Total Cost Year 1

$800K-$1.5M engineering + continuous maintenance

A fraction of DIY cost

Head-to-Head Comparison

Dimension

Build In-House (DIY)

Rebase

Time to Production

6-12 months to build (including integration)

Weeks to deploy

Engineering Cost Year 1

$800K-$1.5M (senior engineers + tooling + opportunity cost)

A fraction of DIY cost. Engineers focus on differentiating AI, not plumbing.

System Connectivity

Custom connectors for each system (CRM, ERP, data warehouse, ticketing, etc.)

50+ native integrations. Real-time sync.

Knowledge Graph

Not included. Custom build required.

Context Engine with cross-domain correlation, ownership mapping, and dependency tracking

Agent Framework

Multiple point solutions (CrewAI, LangChain, custom code)

Agent Studio with no-code builder, pro-code SDK, MCP workspace, and templates

Memory Management

Mem0 or custom implementation

Persistent organizational intelligence with conversation history and context

Model Access

LiteLLM or custom routing. BYOC but unmanaged.

AI Gateway with unified access to any LLM. BYOK. No markup.

Enterprise Governance

Custom build. Auth, audit trails, permissions, compliance are all on you.

Built-in authentication, audit logs, role-based access, compliance readiness

Safe Execution

Sandboxes require custom infrastructure work

Sandboxes with resource limits and permission boundaries

Product Roadmap

Internal platforms stagnate when the team gets pulled to the next priority

Evolving platform with features shipped every sprint

Dependency Risk

5+ dependencies (LangChain, CrewAI, Mem0, LiteLLM, LlamaIndex). Each with its own breaking changes.

Single platform. One vendor, one API, one roadmap.

Total Cost Year 1

$800K-$1.5M engineering + continuous maintenance

A fraction of DIY cost

Why Enterprises Choose Rebase

Speed Compounds in AI

In the AI era, 12 months of building means you're 12 months behind. Every quarter you delay is a quarter your competitors are shipping AI capabilities. Rebase gets you to production in weeks, not months. Your team ships AI 10x faster.

Your Best People Deserve Better

Let your senior engineers work on the problems that actually matter. Not plumbing. Not versioning dependencies. Not debugging integrations. Rebase handles infrastructure. Your team builds intelligence.

Governance Isn't a GitHub Star

LangChain has 126K GitHub stars. None of them are for enterprise audit trails, SOC 2 compliance, or role-based access control. These aren't features you can bolt on at the end. They require architecture from day one. Rebase was built for enterprises.

Platforms Compound. Point Solutions Don't

Internal platforms stagnate when the team gets pulled to the next priority. Products evolve. Rebase ships features every sprint. Six months in, you've compounded intelligence in your knowledge graph, memory system, and agent capabilities. DIY platforms are frozen the day you stop building them.

Infrastructure is an Asset

Frameworks give you components. A platform gives you a knowledge graph that understands your business, memory that captures institutional intelligence, and governance that scales. That asset grows with every agent, every integration, every data source. DIY infrastructure becomes technical debt.

The consulting alternative sounds appealing. Until you see the invoice.

The consulting route: hire McKinsey, Accenture, or a boutique AI firm to build custom AI infrastructure. $500K-$2M+ for a bespoke platform. 6-12 months to build. No ongoing product roadmap — it becomes legacy the day the consultants leave.

The irony? Accenture is now a Frontier Alliance partner for OpenAI. When consultants land at a new client, they spend weeks on discovery. Rebase can map the environment instantly — turning months of scoping into days. The consulting model was built for a world where technology moved slowly. AI doesn't.

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?

DIY

6-12 months to prototype. $800K-$1.5M Year 1. No product roadmap.

Consulting

$500K-$2M+. 6-12 months. Legacy on delivery day.

Rebase

Production-ready in weeks. Features every sprint. Your engineers build what matters

When Each Makes Sense

Build In-House When

Core AI IS your product (you're building an AI company)
Unique requirements no platform can meet
Unlimited engineering budget with no timeline pressure
Willing to maintain indefinitely with your team

Choose Rebase When

AI enables your business but isn't the product itself
Need enterprise AI infrastructure in weeks, not months
Want engineers on differentiating work, not plumbing
Need governance, audit trails, and compliance out of the box
Going through AI transformation or post-M&A consolidation

When Each Makes Sense

Build In-House When

Core AI IS your product (you're building an AI company)
Unique requirements no platform can meet
Unlimited engineering budget with no timeline pressure
Willing to maintain indefinitely with your team

Choose Rebase When

AI enables your business but isn't the product itself
Need enterprise AI infrastructure in weeks, not months
Want engineers on differentiating work, not plumbing
Need governance, audit trails, and compliance out of the box
Going through AI transformation or post-M&A consolidation

Why Teams Choose Rebase

DEPLOY IN WEEKS, NOT MONTHS

Production-ready AI infrastructure from day one. No 6-month build. Your team deploys in your cloud, on your terms.

MEASURABLE IMPACT

Up to 80% reduction in manual data sync. 60% faster engineering delivery. Replace 3-5 point solutions with one platform.

ENTERPRISE READY

SOC 2 Type II. 50+ native integrations. Zero data retention. BYOC/on-prem/air-gapped. HIPAA and GDPR ready.

FAQS

REBASE vs BUILDING IN-HOUSE (DIY)

We already have a prototype built with LangChain. Should we switch?

Prototypes are fine. But prototypes aren't products. LangChain is excellent for exploring ideas in weeks. The problem emerges at months 3-6 when you need to add authentication, logging, memory management, model routing, error handling, and data connectivity. That's when your team discovers they've been building plumbing, not intelligence. If you're past prototype and building for production, Rebase saves significant engineering months.

Isn't open-source cheaper than paying for Rebase?

Can't we just use LangChain for everything?

What if we need capabilities Rebase doesn't have?

Our VP Engineering says the team can build this. How do I respond?

We're worried about vendor lock-in with Rebase.

How does Rebase handle framework updates and breaking changes?

Stop Building Plumbing. Start Building Intelligence.

See why engineering teams choose Rebase over duct-taping open-source frameworks — and ship AI capabilities 10x faster.

Stop Building Plumbing. Start Building Intelligence.

See why engineering teams choose Rebase over duct-taping open-source frameworks — and ship AI capabilities 10x faster.

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Ready to become

an AI-first company

Ready to become

an AI-first company

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