Presage embeds a temporal graph‑based neural model into a production cloud stack to continuously learn the causal dynamics of workloads, resources, and dependencies. The platform delivers real‑time forecasts and what‑if simulations of cost, performance, and failure risk, exposing results through APIs, SDKs, and dashboards for SRE, DevOps, and infrastructure teams. Continuous online learning and drift detection keep predictions accurate as environments evolve.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises operating large-scale cloud environments face constantly changing workloads, resource constraints, and hidden interdependencies that traditional monitoring and rule‑based tools cannot predict. Without a model of how actions propagate through the system, teams react to alerts rather than anticipate issues, leading to inefficiencies, higher costs, and reduced reliability.
Solution
Presage embeds a learned world model directly into a customer’s production cloud stack, continuously ingesting telemetry to capture the temporal dynamics and causal structure of workloads, resources, dependencies, and constraints. The model provides forward‑looking forecasts and what‑if simulations, enabling operators to evaluate the downstream impact of configuration changes before they are applied. By surfacing predicted cost, performance, and reliability outcomes, the platform shifts decision‑making from reactive to proactive. Integration points expose predictive insights through standard APIs and dashboards, allowing seamless incorporation into existing orchestration, CI/CD, and observability pipelines. Continuous online learning ensures the model adapts to evolving workloads and infrastructure topologies, maintaining accuracy over time.
Target Audience
Primary customers are cloud operations teams—Site Reliability Engineers, DevOps, and infrastructure architects—at large enterprises, hyperscale cloud providers, and organizations managing complex multi‑cloud or hybrid environments.
Features
- Real‑time data ingestion pipeline that streams metrics, logs, and traces from cloud services into the modeling layer.
- Temporal graph‑based neural network that learns latent state, dynamics, and causal relationships across workloads, resources, and dependencies.
- Forecasting engine delivering short‑ and long‑term predictions of resource utilization, latency, and failure risk with confidence intervals.
- What‑if simulation interface allowing users to model configuration or scaling changes and view projected system impacts.
- RESTful API and SDKs for integration with Kubernetes, Terraform, cloud provider APIs, and existing observability tools.
- Continuous online learning with drift detection to keep the model aligned with evolving workloads and infrastructure changes.
- Enterprise‑grade security: end‑to‑end encryption, data isolation per client, and role‑based access controls.
- Interactive dashboard visualizing causal graphs, predictive alerts, and cost‑performance trade‑offs.