HaystackFreemium
An open-source framework from deepset for building production LLM applications — RAG, search, and agents — built around composable pipelines.
Agent Development comparison
Haystack vs Microsoft AutoGen
Pricing, pros, cons, and ideal use cases — side by side.
An open-source framework from Microsoft Research for building multi-agent applications, where agents converse to solve tasks together.
At a glance
| Haystack | Microsoft AutoGen | |
|---|---|---|
| Pricing | FreemiumOpen-source framework is free. The deepset AI Platform is a paid, enterprise-oriented managed offering. | FreeOpen-source (MIT), free to use. You pay only for the underlying model API calls. |
| Category | Agent Development | Agent Development |
| Ideal for | Teams building production RAG and searchEngineering orgs that want inspectable pipelinesEnterprises evaluating managed LLM platforms | Engineering teams prototyping multi-agent systemsResearch and innovation groupsTeams already in the Microsoft ecosystem |
Pros & cons
Haystack
Pros
- Explicit, testable pipeline architecture
- Mature retrieval and evaluation tooling
- Managed deepset platform for governed deployments
- Production-focused rather than demo-focused
Cons
- Pipeline model is less flexible than free-form agents
- Smaller community than LangChain or LlamaIndex
- Managed platform pricing requires a sales conversation
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
Which should you choose?
Microsoft AutoGen is the lighter-weight option (Free), while Haystack sits higher on the pricing ladder (Freemium). Haystack is built around teams building production rag and search; Microsoft AutoGen leans more toward engineering teams prototyping multi-agent systems. Shortlist the one whose strengths line up with your biggest constraint.
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