Ondine¶
SDK for batch processing tabular datasets with LLMs.
For data engineers and ML practitioners who need to process millions of tabular rows with LLMs, Ondine delivers 100x fewer API calls via multi-row batching and 40-50% cost reduction via prefix caching -- with cost estimation, budget limits, checkpointing, and 100+ provider support built in.
Quick Start¶
from ondine import QuickPipeline
result = QuickPipeline.create(
data="products.csv",
prompt="Classify this product into a category: {name} - {description}",
model="gpt-4o-mini"
)
Key Features¶
- Quick API -- 3-line hello world with smart defaults
- Builder API -- Full control over pipeline configuration
- 100+ LLM providers via LiteLLM (OpenAI, Anthropic, Groq, Azure, local MLX)
- Multi-row batching -- Process 100 rows per API call
- Prefix caching -- 40-50% cost reduction on repeated prompts
- Cost estimation -- Know the cost before you run
- Budget limits -- Hard caps on spending
- Checkpointing -- Resume failed pipelines from where they stopped
- Structured output -- Pydantic models, JSON, regex parsing
- Observability -- OpenTelemetry, Langfuse, structured logging