Platform vs Framework

Comparing Apples with Apples. The difference between platforms, frameworks and tools.

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.

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)

• 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.