Simplismart

San Francisco, California, USA · 11-50 employees · simplismart.ai

Fastest inference for generative AI workloads. Simplified orchestration via a declarative language similar to terraform. Deploy any open-source model and take advantage of Simplismart’s optimised serving. With a growing quantum of workloads, one size does not fit all; use our building blocks to personalise an inference engine for your needs. ***API vs In-house*** Renting AI via third-party APIs has apparent downsides: data security, rate limits, unreliable performance, and inflated cost. Every company has different inferencing needs: *One size does not fit all.* Businesses need control to manage their cost <> performance tradeoffs. Hence, the movement towards open-source usage: businesses prefer small niche models trained on relevant datasets over large generalist models that do not justify ROI. **Need for MLOps platform*** Deploying large models comes with its hurdles: access to compute, model optimisation, scaling infrastructure, CI/CD pipelines, and cost efficiency, all requiring highly skilled machine learning engineers. We need a tool to support this advent towards generative AI, as we had tools to transition to cloud and mobile. MLOps platforms simplify orchestration workflows for in-house deployment cycles. Two off-the-shelf solutions readily available: 1. Orchestration platforms with model serving layer: *do not offer optimised performance for all models, limiting user’s ability to squeeze performance* 2. GenAI Cloud Platforms: *GPU brokers offering no control over co