RapidFire AI
Experiment engine for tuning and comparing LLM and agent configurations
About RapidFire AI
RapidFire AI runs many variants of a model, retrieval or agent setup in parallel on shared hardware, stopping weak ones early and cloning strong ones. It supports fine-tuning and RAG experimentation and has an open-source core.
Common use cases
- Tune RAG pipelines
- Fine-tune LLMs efficiently
- Compare agent designs
- Control experiment spend
Products and services
RapidFire platform
Parallel experimentation across agent, RAG and fine-tuning configurations
Key features
- Parallel experiments
- Early stopping
- Cost and latency tradeoffs
- Open-source core
- Audit log
- Model provider support
Integrations
- Hugging Face
- PyTorch
- MLflow
- Ray
- OpenAI
- Anthropic
RapidFire AI alternatives
FAQs
Is it open source?
The company describes an open-source project on GitHub.
What does it speed up?
Comparing many configurations of models, retrieval and prompts at once.
Which frameworks does it work with?
Hugging Face, PyTorch, MLflow and Ray among others.
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