TheStage is an AI inference optimization platform that helps developers deploy machine learning models faster by combining quantization, pruning, acceleration, and serving into a single workflow. Its ANNA tool lets users tune model size, latency, and quality with a slider, while GPU rental and on-device SDKs support both cloud and edge deployments. The platform targets AI teams building applications like AI tutors, note-takers, and gaming NPCs.
Funding
Funding not disclosed
Founders
Product
Problem
Deploying AI models to production is complex and time-consuming, requiring developers to navigate separate tools for quantization, acceleration, compilation, and serving. This fragmented workflow slows iteration, increases engineering overhead, and makes it difficult to balance model size, latency, and quality for different hardware targets.
Solution
TheStage provides an end-to-end inference optimization platform that unifies quantization, pruning, automated acceleration, compilation, and serving into a single stack. Its ANNA (Automated NNs Accelerator) tool lets developers control model size, latency, and quality through a simple slider, powered by state-of-the-art optimizations. The platform supports importing custom models or selecting pre-optimized open-source models, then deploying them to cloud GPUs rented from leading providers or to on-device environments via an SDK. Developers can attach GPU instances to projects, create Docker containers, and run code from their laptops with real-time log streaming, creating a seamless feedback loop. TheStage also offers Triton-based serving for cloud deployments and an on-device SDK for local, low-latency use cases like AI tutoring, note-taking, and gaming NPCs.
Target Audience
Primary customers are AI engineers and product teams building inference-heavy applications such as AI tutors, real-time transcription tools, robotics, gaming, and generative AI services, who need to optimize and deploy models across cloud and edge devices.
Features
- ANNA automated accelerator with a slider-based interface for tuning model size, latency, and quality
- Configurable quantization and pruning API with predefined configs for NVIDIA GPUs and Apple silicon
- Unified compilation API that converts compressed checkpoints into optimized, deployable formats
- Triton-based serving for cloud deployments and an on-device SDK for local inference
- GPU rental marketplace with pay-as-you-go billing across leading providers, plus support for self-hosted instances
- CLI tool that connects a laptop to GPU containers with sub-second starts, automatic commits, and real-time log streaming
- Pre-optimized open-source model library for quick project initialization
- SOC 2 Type I compliance, role-based access, audit logs, and encrypted data handling for production security