Hyground
Also known as: Hyground AI, Hyground GmbH
Data sovereign, self hosted AI SRE agent that runs bring your own model on the customer's own Kubernetes infrastructure, investigating incidents and cutting MTTR with human governed actions.
Hyground is a data sovereign AI SRE agent for enterprise IT operations, built in Germany and self hosted by default: it runs bring your own chart and bring your own model on the customer's own infrastructure, on premises, in private cloud or bring your own cloud, air gapped if required, with no data sent back to the vendor. Kubernetes native by design, it runs directly on Kubernetes as its execution and observation runtime and leverages the client's existing observability stack, connecting to systems through the open Model Context Protocol and cross referencing logs, metrics, configuration, documentation, code and historical context to turn fragmented signals into clear root cause analysis and recommended remediation. During incidents the agent reasons through iterative investigation loops, renders charts and blast radius maps inline, and helps teams resolve incidents up to eighty five percent faster, while responsibility and decision making stay fully with the engineering team through read only investigation paths, human governed actions and identity bound execution. Every task becomes a reusable Hyground skill that teams can run, reuse and schedule, codifying senior engineer playbooks in plain text as deterministic, repeatable workflows, and the platform captures operational patterns and institutional knowledge so they are accessible across the organization. Architected for GDPR with data residency controls and audit logging, Hyground is in production at enterprises including Deutsche Bahn Reisendeninformation and ifm, and is expanding beyond incident resolution into FinOps and SecOps.
Vendor details
Canonical URL
https://hyground.ai/
Category
SRE / DevOps agent
Funding status
3 million euro pre seed (March 2026), from Partech, Adesso Ventures, Angel Invest and Plug and Play. Hamburg, Germany; spun out of MaibornWolff.
Company status
private
Use cases & customers
Primary use cases
Target customers
Deployment options
Integrations
Connects to any system with an API through the open Model Context Protocol and leverages the customer's existing observability stack, cross referencing logs, metrics, configuration, documentation and code across Kubernetes, AWS, Azure and GCP. Integration work stays portable with no external control plane and no vendor lock in.
Sources & related URLs
Capability coverage
11.5 / 14 capabilities · 82%
| Integrations & Tool CallingConnects any system with an API through the open Model Context Protocol and leverages the customer's observability stack, cross referencing logs, metrics, config, docs and code across Kubernetes, AWS, Azure and GCP (hyground.ai product overview, Partech). | Full |
|---|---|
| Workflow OrchestrationAutonomous agents reason and adapt through iterative investigation loops, and every task becomes a Hyground skill that teams run, reuse and schedule as deterministic repeatable workflows (hyground.ai home, product overview). | Full |
| Knowledge Grounding & RAGCorrelates logs, metrics, configurations, documentation, code and historical context and captures operational patterns and institutional knowledge from senior engineers, making them accessible across the organization (Crunchbase, Partech). | Full |
| Human Oversight & GuardrailsResponsibility and decision making stay fully with the engineering team through read only investigation paths, human governed actions and identity bound execution (hyground.ai whitepapers, Crunchbase). | Full |
| Security, Identity & GovernanceFull data sovereignty with zero external exposure, air gapped capable, architected for GDPR with data residency controls and audit logging, plus identity bound actions and governed autonomy from the platform layer up (hyground.ai home, whitepapers, product overview). | Full |
| Observability & AuditabilityUnifies observability data and adds audit logging, rendering charts inline (Apache ECharts) and drawing service topology and blast radius maps, with auditability designed in from the platform layer (hyground.ai product overview, whitepapers). | Full |
| Memory & State PersistenceCaptures operational patterns, institutional knowledge and historical context and persists reusable, schedulable skills, though a dedicated long term agent memory store was not detailed (Partech, hyground.ai product overview). | Partial |
| Deployment & Data ResidencySelf hosted by default and not SaaS: runs bring your own chart on the customer's own infrastructure, on premises, in private cloud or bring your own cloud, air gapped if required, with data residency controls (hyground.ai home, Crunchbase). | Full |
| Prebuilt Agents / Templates / PacksA Hyground skills library codifies senior engineer playbooks in plain text as reusable, schedulable workflows that any team member can run (hyground.ai home, product overview). | Full |
| Triggers & Channel CoverageSkills can be scheduled and incidents trigger autonomous investigation, but coverage is centered on the operations domain and a chat interface rather than broad channels (hyground.ai home, product overview). | Partial |
| Model Flexibility & RoutingBring your own model: Hyground runs on the customer's own LLMs, air gapped, with the model a swappable part rather than a dependency that cannot be revoked (hyground.ai blog, home). | Full |
| APIs / SDKs / MCP ExtensibilityConnect any system with an API via the open Model Context Protocol, with bring your own chart deployment, portable integrations, no external control plane and a self serve sandbox (hyground.ai product overview, home). | Full |
| Testing, Debugging & OptimizationBlast radius mapping assesses impact before action, deterministic repeatable workflows aid consistency, and a sandbox lets teams try the agent, but a formal agent testing framework was not documented (hyground.ai product overview, home). | Partial |
| Browser / Computer-useNo browser or computer use capability; Hyground acts on infrastructure through MCP and observability connectors (hyground.ai). | Unable to verify |
Pricing
Annual license priced by the size of your infrastructure, with no usage fees, per seat charges or costs; the specific rate is not published.
annual license scaled by infrastructure size, with no usage or per seat fees
Cost watchouts
The model is designed to avoid costs; self hosted deployment runs on customer infrastructure and premium support for setup and operations is part of the engagement, and the license scales as infrastructure grows.
Variable cost rationale
Explicitly no usage fees and no per seat charges; a flat annual license scaled only by infrastructure size makes cost predictable with minimal variable exposure.
Sales call required
Yes — required for paid access
Free / trial
Self serve sandbox plus a scheduled demo
Lowest paid plan
Annual infrastructure sized license; rate via sales
Key ambiguities
The pricing model is clearly public (annual license by infrastructure size, no usage or per seat fees), but the specific license rate is not published.
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