Jarvislabs.ai Launches Managed Endpoints to Cut Weeks from Enterprise AI Deployment
Jarvislabs.ai a platform owned by E2E Networks Limited focused on AI infrastructure, has announced Managed Endpoints, a service that enables enterprises to deploy open-weight AI models without configuring and optimizing the underlying inference infrastructure themselves.
Across enterprise deployments over the past several months, Jarvislabs.ai found that getting a newly released AI model ready for production could take 15 to 20 working days, even with its AI research team working alongside customers. Teams had to evaluate GPUs, serving software and model-specific settings before an endpoint could reliably handle a production workload.
Managed Endpoints performs that work once at the platform level. Customers choose a model from the catalog and receive an API endpoint with the model weights, GPU configuration and serving engine already set up, tested and tuned by Jarvislabs.ai.
The current catalog includes DeepSeek V4 Flash, Gemma 4 31B, GLM-5.2, GLM-5.3 and GLM-5.3 Flash, covering reasoning, multimodal and tool-calling workloads. Jarvislabs.ai plans to add newly released models as its research and engineering teams evaluate and optimize them.
“We repeatedly saw enterprises spend close to three weeks getting a newly released model ready for production,” said Vishnu Subramanian, Founder, Jarvislabs.ai. “Managed Endpoints lets us do that engineering once for each model, so customers can start with infrastructure that has already been tested and optimized.”
The validation behind each endpoint can be substantial. While preparing GLM-5.2, Jarvislabs.ai spent more than 1,000 GPU-hours and $5,000 in compute characterizing the model on an eight-H200 node. The deployment scored 80.9% on Terminal-Bench 2.1, closely matching the 81.0% score reported by model developer Z.ai.
Managed Endpoints is designed for organizations running AI coding assistants, autonomous agents, enterprise copilots and internal AI applications. It is particularly suited to sustained workloads where predictable performance, dedicated capacity and control over data location matters.
Jarvislabs.ai plans to expand the service with additional language and multimodal models, including speech-to-text, text-to-speech and text-to-video workloads.