Why ForestFlow?

We first set out to find a solution to deploy our own models. The model server implementations we found were either proprietary, closed-source solutions or had too many limitations in what we wanted to achieve. The main concerns for creating ForestFlow can be summarized as:

While ForestFlow has already delivered tremendous value for us in production, it’s still in early phases of development as there are plenty of features we have planned and this continues to evolve at a rapid pace. We appreciate and consistently, make use of and, contribute open source projects back to the community. We realize the problems we’re facing aren’t unique to us so we welcome feedback, ideas and contributions from the community to help develop our roadmap and implementation for ForestFlow. Check out ForestFlow on Github for a getting started guide and more information.

Model Deployment

For model deployment, ForestFlow supports models described via MLflow Model format which allows for different flavors i..e, frameworks & storage formats.

ForestFlow also supports a BASIC REST API for model deployment as well that mimics the MLflow Model format but does not require it.


For inference, we’ve adopted a similar approach. ForestFlow provides 2 interfaces for maximum flexibility; a BASIC REST API in addition to standardizing on the GraphPipe API specification.

Relying on standards, for example using GraphPipe’s specification means immediate availability of client libraries in a variety of languages that already support working with ForestFlow; see GraphPipe clients.

Please visit the quickstart guide to get a quick overview of setting up ForestFlow and an example on inference. Also please visit the Inference documentation for a deeper dive.

Currently Supported model formats