AI Framework => Production simplified.
The OSS control plane for your AI implementations.
Start Building →Control Plane in Action
Experience how KitchenAI simplifies AI framework orchestration

Try out the KitchenAI Playground and see the control plane in action
Framework Orchestration
Manage multiple AI frameworks through a single, standardized API endpoint. From Langchain to RAG, deploy with confidence.
Bento Box Management
Create, test, and deploy self-contained AI implementations. Version control and instant rollbacks included.
Real-Time Monitoring
Watch your AI workflows execute in real-time. Debug and optimize with comprehensive observability.
Empowering Your Teams
Simplify AI Integration. Amplify Expertise.
Application Developers
Simple API integration using familiar OpenAI SDK
client.chat.completions.create(
model="@my-bento/query",
messages=[{"role": "user",
"content": query}]
)
AI Teams
Build sophisticated AI workflows in Bento Boxes
index = VectorStoreIndex.from_vector_store(vector_store)
query_engine = index.as_query_engine(
chat_mode="best",
filters=filters,
llm=llm,
verbose=True
)
# Execute query
response = await query_engine.aquery(data.query)
...
KitchenAI bridges the gap between application developers and AI teams, allowing each to focus on their strengths while delivering powerful AI capabilities.
FOR DATA SCIENTISTS & ENGINEERS
From AI Framework
to Production API
Focus on building AI implementations. We'll handle the infrastructure.
Bento Boxes
Self-contained AI implementations using Langchain, LlamaIndex, and other AI frameworks. Version, test, and deploy with confidence.
Control Plane
A unified interface that standardizes how your apps interact with AI frameworks. One API endpoint, multiple implementations.
Framework Agnostic
Build with any AI framework. Swap implementations without changing your application code.
Pro tip: Use the familiar OpenAI SDK to interact with your bento boxes. Point the client to your KitchenAI control plane and access your AI implementations like any other model.
Framework Integration
Simple APIs to manage your AI implementations across different frameworks
@kitchen.query.handler("query")
async def query_handler(data: WhiskQuerySchema) -> WhiskQueryBaseResponseSchema:
"""Query handler with RAG"""
# Create index and query engine
index = VectorStoreIndex.from_vector_store(vector_store)
query_engine = index.as_query_engine(
chat_mode="best",
filters=filters,
llm=llm,
verbose=True
)
# Execute query
response = await query_engine.aquery(data.query)
return WhiskQueryBaseResponseSchema.from_llama_response(
data,
response,
token_counts=TokenCountSchema(**token_counts),
metadata={"token_counts": token_counts, **data.metadata}
)
LlamaIndex
Like AWS Lambda for AI frameworks - define the kitchenai entrypoint, build with any framework
Entrypoint Signature
@kitchen.query.handler("query") async def query_handler(data: WhiskQuerySchema) -> WhiskQueryBaseResponseSchema:
Level Up Your LLMOps With KitchenAI Managed Cloud
First 100 teams onboarded get 500 free AI credits
What Makes Us Unique
Redefining how organizations integrate and manage AI workflows
Modular Architecture
Package AI workflows as independent "bento boxes" that can be updated, replaced, or scaled without affecting the entire system.
High-Performance Messaging
Built on NATS for lightning-fast, reliable communication between AI modules and dynamic service discovery.
Framework Agnostic
Work with any AI framework or model. No vendor lock-in, maximum flexibility for your AI stack.
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