Morphais is a managed AI‑inference platform that lets teams upload TensorFlow, PyTorch, ONNX or custom Docker models and instantly expose them as high‑performance HTTP endpoints. The service uses an OpenResty‑based gateway for sub‑millisecond routing, automatic horizontal scaling with GPU/CPU selection, and built‑in authentication, TLS encryption, and audit logging. Real‑time dashboards and SDKs for Python, JavaScript and Go provide monitoring and easy client integration, enabling enterprises to embed scalable AI without managing infrastructure.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises and developers often struggle to expose machine‑learning models as reliable, low‑latency web services. Deploying inference workloads requires specialized infrastructure, custom scaling logic, and secure API gateways, which adds operational overhead and delays time‑to‑market. These challenges limit the adoption of AI capabilities in customer‑facing applications.
Solution
Morphais delivers a managed AI‑inference platform that abstracts the underlying infrastructure and presents models as high‑performance HTTP endpoints. By building on OpenResty, the service combines Nginx’s event‑driven architecture with Lua scripting to route requests, perform request‑level authentication, and execute inference with minimal latency. Users upload trained models in common formats (e.g., TensorFlow SavedModel, ONNX) and the platform automatically containers the model, provisions compute resources, and scales horizontally based on traffic patterns. All inference calls are encrypted in transit, logged for auditability, and can be integrated with existing CI/CD pipelines via REST or GraphQL APIs. The platform also provides real‑time usage dashboards and alerts, enabling teams to monitor performance and cost without managing servers directly.
Target Audience
Primary customers are software engineering teams, data‑science groups, and enterprise product units that need to embed AI inference into web, mobile, or SaaS applications at scale.
Features
- Model ingestion pipeline supporting TensorFlow, PyTorch, ONNX, and custom Docker images
- OpenResty‑based API gateway delivering sub‑millisecond request routing and Lua‑level request validation
- Automatic horizontal scaling with container orchestration and GPU/CPU auto‑selection
- Built‑in API key and OAuth 2.0 authentication with rate‑limit policies per endpoint
- Real‑time monitoring dashboard showing latency, error rates, and inference throughput
- SDKs for Python, JavaScript, and Go to simplify client integration
- End‑to‑end TLS encryption and audit‑ready logging compliant with SOC 2 and GDPR standards