About Modelyo
Modelyo sells a cryptographically isolated cloud environment in which customer data, applications, AI workloads and keys remain under customer control, even on infrastructure the customer does not own. Modelyo provides a mesh of confidential managed services that inherit the same security and trust architecture from the start. Modelyo is a confidential layer that runs on the cloud customers already use.[1][3][4][5]
Modelyo creates a hardware-rooted trust layer that keeps workloads isolated from infrastructure operators and Modelyo itself. Neither the cloud provider nor Modelyo can see inside the infrastructure that runs a customer's workloads. Modelyo isolates infrastructure and workloads from the cloud provider and verifies them cryptographically before they are trusted. Modelyo continuously verifies infrastructure and workloads against trusted policies, and changes in integrity or trust state are detected and handled automatically. Modelyo checks each workload every 60 seconds and moves it automatically if anything looks off. Intel SGX, TDX and NVIDIA H100 are used so that Modelyo operators cannot access customer data.[1][3][4][5]
Modelyo ties access to sensitive data and services to verified environments, so neither the cloud provider nor Modelyo becomes part of the customer's trust model. Modelyo keeps data protected at rest, in transit, and while it is being processed. Modelyo produces cryptographic proof and audit evidence of exactly what ran and on what data. Enterprise customers of Modelyo get a named technical contact and 24/7 support for production issues.[3][5][6]
Kubernetes, databases, storage, networking, and managed services operate together within the same confidential trust architecture at Modelyo. Kubernetes, databases, AI, storage, and development tools all inherit the same confidential protection from the moment they are deployed. Nodes, control plane, secrets, keys, storage, and workloads operate inside the same confidential architecture. Teams deploy pods, services, and workloads on Modelyo using the Kubernetes tools and workflows they already use. Modelyo delivers familiar databases, messaging, search, APIs, and developer services as confidential managed services. Customers can launch PostgreSQL, MySQL, MongoDB, and Redis on Modelyo with compute, storage, networking, and keys protected as one environment. Modelyo also lists Kafka, RabbitMQ, Elasticsearch, API gateways, container registries, and observability tools among the services deployed inside the confidential workspace.[3][4]
Modelyo supports data environments with object storage, Apache Spark, Hadoop, Kafka, and supporting services integrated inside the same confidential architecture. On Modelyo, customers hold the keys to S3-compatible object storage, shared file stores over NFS, persistent Kubernetes volumes, and encrypted backups.[3]
Modelyo runs inference, agents, and AI workloads inside hardware-protected environments while keeping sensitive data, models, and weights under customer control. Modelyo runs inference, RAG, vector databases, data processing, and AI applications while keeping sensitive data protected during processing. For agents, Modelyo keeps prompts, memory, and reasoning traces sealed even during active use. Modelyo checks each agent tool call and retrieval against keys only the customer holds. Modelyo checks each RAG lookup against the customer's keys, the same as every model call. Modelyo traces every answer to the model and dataset that produced it.[3][4][6]
Modelyo deploys across hyperscalers, neoclouds, sovereign clouds, and on-prem environments across Intel, AMD, Arm, and NVIDIA architectures. Modelyo is built to support confidential AI infrastructure, from H100 and H200 through Blackwell and Vera Rubin, including Arm CCA-based environments. Modelyo provisions the confidential hardware from the cloud provider's own fleet. Customers deploy Modelyo with the Kubernetes tools and workflows their teams already use, and connect their identity provider and existing tools. Modelyo deployments start with a technical deep dive and a guided first workload. Because Modelyo runs across hyperscalers, neoclouds and on-prem environments, the same confidential environment can be deployed in more than one place for resilience.[1][3][4][5]
Modelyo describes customer stories of regulated enterprises, government, and defence teams running sensitive workloads on Modelyo. In one story, a regional bank needed to run core transaction processing on public cloud infrastructure, but internal compliance and its regulator required on-prem-level data control. At that bank, Kubernetes, the transaction database, and the analytics pipeline all moved inside a single hardware-enforced perimeter, with encryption keys held exclusively by the bank's own HSM. In another story, a government department needed the compute of a public cloud for a classified analytics programme. That programme was deployed inside a Modelyo confidential environment spanning two cloud regions. In a third story, a healthcare provider wanted to run AI models over patient records, but the models lived with a third-party vendor. In that story, inference runs on confidential H100 compute inside a Modelyo environment.[1][2][3][5][6][7][10]
Modelyo on the record