An open-source framework from Microsoft Research for building multi-agent applications, where agents converse to solve tasks together.
AI / LLM comparison
Microsoft AutoGen vs Perplexity
Pricing, pros, cons, and ideal use cases — side by side.
PerplexityFreemium
AI-powered answer engine that searches the live web and cites its sources — the ChatGPT alternative for research.
At a glance
| Microsoft AutoGen | Perplexity | |
|---|---|---|
| Pricing | FreeOpen-source (MIT), free to use. You pay only for the underlying model API calls. | FreemiumFree tier available. Pro $20/month. Enterprise plans available. |
| Category | AI / LLM | AI / LLM |
| Ideal for | Engineering teams prototyping multi-agent systemsResearch and innovation groupsTeams already in the Microsoft ecosystem | Any SMBLaw FirmsAgenciesConsultantsRecruiters |
Pros & cons
Microsoft AutoGen
Pros
- Mature multi-agent conversation patterns
- Backed by Microsoft Research
- Flexible high-level and low-level APIs
- Strong fit for experimentation
Cons
- Multi-agent conversations are hard to evaluate and debug
- Token costs can escalate without guardrails
- APIs have changed significantly between versions
Perplexity
Pros
- Cites every source
- Access to multiple frontier models
- Excellent for research
- Follow-up threads keep context
Cons
- Sometimes cites low-quality sources
- Less conversational than ChatGPT
- Not built for long-form creation
Which should you choose?
Microsoft AutoGen is the lighter-weight option (Free), while Perplexity sits higher on the pricing ladder (Freemium). Microsoft AutoGen is built around engineering teams prototyping multi-agent systems; Perplexity leans more toward any smb. Shortlist the one whose strengths line up with your biggest constraint.