bolei

Exactly what we are looking for. Any performance plugins on top of this would be GOLD.

Indeed, and no you are not off. We heard lots of feedback for building out business cases so we are will be working on that. Thanks a lot for the feedback

data scientists and data engineers who do not want to deal with the server management or deploy multithreading jobs when creating data ingestion APIs.

Langflow is a great orchestration product! Our goal is to enable low latency data processing with and without LLMs through one click deployment into production ready APIs.

that's really great observation. what kind of tasks are you building with StepFunctions? We are integration partner with AWS Lambda, so definitely curious about StepFunctions too!

sorry about that. Please try docs.fleak.ai directly? What Fleak offers is a simple way to capture the data processing logic, with or without LLM, and we make deployment to production with one click. That means as a user, no need to write multithread scripts, configuring clusters or create load balancers since Fleak's ingestion engine will optimize the latency across different processing unit (we call them nodes, whether it's llm or simple SQL). On the LLM side, we work with our partner to coordinate the throughput, so if there is a demand for 5000 bps Llm inferencing, we can certainly scale up to that. But for SQL functions, the users can basically get that level of autoscaling out of box.

you may think of it as a cloud function, we mostly focus on HTTP based data ingestions without a storage layers. e.g. Your app is sending some data, Fleak can be used to create an API to process that data then return back to the app synchronously or save the processed data directly into a storage layer.

What are the best use cases you have built?