DSPy
5.7#13 in llm application development frameworksAPISelf-hosted
VisitDSPy vs Flowise
What both publish
| Fact | DSPy | Flowise |
|---|---|---|
| Deployment | The documentation describes deploying DSPy programs with FastAPI or MLflow. | The product site describes cloud and on-premises deployment, while the repository provides npm and Docker installation instructions and self-host deployment options. |
| Integrations | DSPy uses LiteLLM to normalize inference providers and says it can connect to nearly any model and provider supported by LiteLLM. | Flowise documentation says it connects to 100+ sources, tools, vector databases, and memories, and lists LangChain, LlamaIndex, and Zapier Zaps integrations. |
| License | The project is listed as MIT licensed. | The sunset notice says Flowise source code remains on GitHub under the Apache 2.0 license and encourages teams to fork it. |
| Support | The site links to a DSPy Discord community and GitHub repository. | The documentation lists community support and vendor support including SLA support and consultations; the sunset notice says official core team presence in Discord and GitHub concluded on August 31, 2026. |
DSPy plans
No prices published.
Flowise plans
- FreeFree
- Starter$35 a month
- Pro$65 a month
Pick DSPy if…
DSPy suits developers building agent, extraction, or multimodal AI systems in Python. It also fits teams wanting automated prompt optimization and connections to multiple model providers.
Pick Flowise if…
Flowise suits developers building AI assistants or agent workflows who want visual design tools and deployment choices. Its scheduled end of life and cessation of feature development are important considerations for teams evaluating long-term use.