# Platform vs Framework

### **Introduction**

One of the biggest challenges to AI is filtering through all the noise to find the signal. There are so many companies doing different things in AI - and most likely you will end up using many different tools,  apps, platforms and other variations of AI.  When it comes to building AI Agents - it's helpful to know the difference between different "AI Agentic" solutions available to you.

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### **AI Frameworks (DIY / custom build)**

* What it is: You assemble everything yourself (or with open-source frameworks): models, vector stores, tools, orchestration, security.
* Strengths: Maximum control, deep customization, no vendor lock-in.
* Trade-offs: Hard-to-hire AI talent, slower time-to-value, constant upkeep to track model/tool changes; you own security, compliance, and MLOps.
* Best for: Teams with strong engineering capacity and unique requirements that off-the-shelf tools can’t meet.
* Examples: Autogen, Langchain

### **AI Platforms (e.g.,** [**raia**](https://raiaai.com/)**)**

* What it is: A secure, compliant, zero-/low-code foundation for building, training, testing, monitoring, and operating agents—while still allowing custom extensions.
* Strengths: Faster deployment, centralized governance, built-in observability and HITL, lets your team focus on use-cases instead of platform plumbing.
* Trade-offs: Some guardrails and opinionated workflows; you align with a vendor’s roadmap and pricing.
* Best for: Orgs that want enterprise-grade scale and compliance without staffing a full AI platform team.
* Examples: raia, Botpress, Agent.ai

### **Add-On / Single-Point Solutions**

* What it is: Narrow AI features bundled into a specific SaaS app or aimed at one role (e.g., help desk bot, sales email assistant).
* Strengths: Quick wins, minimal setup, fits neatly inside the host tool.
* Trade-offs: Limited flexibility and cross-department reach; creates silos and won’t support an enterprise-wide agentic workforce long term.
* Best for: Targeted, department-level needs or proving value before a broader rollout.
* Examples: Salesforce, Sierra.

**How to choose:** Balance speed vs. flexibility, departmental wins vs. enterprise scale, and upfront build costs vs. ongoing operational ownership aligned to your long-term AI vision.

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