Cake for AIOps
Automate monitoring, diagnostics, and remediation using LLM-powered AIOps pipelines built on open-source infrastructure. Reduce costs and resolve incidents faster with a composable, cloud-agnostic stack.







Modernize operations with intelligent automation and modular observability
Legacy operations stacks can barely keep up with modern infrastructure, let alone modern data. Logs, metrics, and alerts pour in faster than teams can triage, and manual responses slow everything down. AIOps bridges that gap, bringing intelligence and automation to incident detection, diagnosis, and resolution.
Cake provides a full AIOps stack built on open-source components and designed for real-world infrastructure. Use LLMs to interpret logs, correlate events, and trigger actions. Connect to observability tools like Prometheus and Grafana, orchestrate workflows with Kubeflow Pipelines, and monitor system health using open models like Evidently or NannyML.
With Cake, you can integrate the latest AIOps innovations into your workflows without being locked into an opaque vendor product. And because everything is modular and cloud agnostic, you reduce costs, improve flexibility, and maintain control over critical operational logic.
Key benefits
- Automate root-cause analysis: Use LLMs to summarize logs, correlate alerts, and reduce time-to-resolution.
- Reduce costs and complexity: Replace brittle custom scripts and siloed dashboards with integrated, reusable pipelines.
- Integrate open-source observability: Connect to tools like Prometheus, Grafana, and LLM-based detectors out of the box.
- Stay modular and cloud agnostic: Deploy anywhere and evolve your AIOps stack without lock-in.
- Ensure compliance and traceability: Capture logs, actions, and incident lineage for review and audits.
Common use cases
Teams use Cake’s AIOps infrastructure to streamline operations and reduce alert fatigue:
Intelligent alert routing
Use LLMs to cluster, summarize, and prioritize alerts based on severity and context.
Automated diagnostics
Parse logs and telemetry in real time to identify root causes and suggest remediations.
LLM-powered runbooks
Trigger automated actions (e.g., scaling, restarting, or reconfiguring) based on AI-generated insights.
Components
- Training frameworks & models: Hugging Face (LLMs, embeddings), PyTorch, TensorFlow
- Orchestration: Kubeflow Pipelines
- Monitoring & observability: Prometheus, Grafana, Evidently, NannyML
- Log ingestion & preprocessing: AirByte, DBT
- Model serving: KServe, NVIDIA Triton
- Data storage: AWS S3, Snowflake
- Automation: LangChain, Pipecat
"Our partnership with Cake has been a clear strategic choice – we're achieving the impact of two to three technical hires with the equivalent investment of half an FTE."

Scott Stafford
Chief Enterprise Architect at Ping
"With Cake we are conservatively saving at least half a million dollars purely on headcount."
CEO
InsureTech Company