When a bank evaluates a testing tool, the first question is not about features. It is about data. Where does our application data go when tests run? Who can access the screenshots? Where are the test results stored? Can we prove to our regulator that no customer data left our infrastructure?
These are not unreasonable questions. They are legal requirements. And for years, they have effectively locked regulated industries out of the AI testing revolution. Cloud-based AI testing tools require sending application data — URLs, screenshots, DOM snapshots, API responses — to external servers for analysis. For organizations subject to GDPR, HIPAA, PCI DSS, or sector-specific regulations, that data transfer is often a non-starter.
The result is a two-tier market. Technology companies and startups adopt AI-powered testing and gain significant velocity advantages. Banks, insurance companies, healthcare providers, and government agencies continue with traditional test automation frameworks, spending disproportionate engineering time on test maintenance while their less-regulated competitors move faster.
On-premise AI testing deployment eliminates this divide.
Why Cloud-Only Does Not Work for Regulated Industries
The objections to cloud-based testing tools in regulated environments are specific and well-founded.
Data Sovereignty
Regulations like GDPR require that personal data of EU citizens remains within the EU unless specific adequacy decisions or safeguards are in place. When an AI testing tool captures screenshots of a staging environment that contains realistic test data — which often mirrors production data — those screenshots become personal data. Sending them to a cloud service hosted in another jurisdiction creates a compliance obligation that many organizations would rather avoid entirely.
Audit Requirements
Regulated industries must demonstrate control over their tooling. Auditors want to know what software is running, what version it is, what data it accesses, and who has access to it. Cloud services complicate this because the customer does not control the deployment. An on-premise deployment running on the organization's own Kubernetes cluster is auditable, version-controlled, and subject to the same change management processes as the rest of the infrastructure.
Network Isolation
Many enterprise applications under test are not accessible from the public internet. They run on internal networks, behind VPNs, or in air-gapped environments. A cloud-based testing tool simply cannot reach them. An on-premise deployment sits inside the network perimeter and can access internal applications directly.
Intellectual Property Protection
The application under test is itself intellectual property. Screenshots of unreleased features, API response schemas, and test scripts that describe business logic all contain sensitive information. Some organizations are unwilling to send this data to any third party, regardless of the third party's security posture.
The EU AI Act Dimension
The EU AI Act, which came into effect in stages starting in 2024, adds another layer of compliance consideration. Organizations using AI systems must understand what the AI does, how decisions are made, and what data is processed. For AI testing tools, this means being able to explain how the AI generates tests, how it decides to heal a broken test versus reporting a failure, and how it handles the application data it encounters.
An on-premise deployment gives the organization full visibility into the AI system's behavior. Logs, model interactions, and decision traces are all stored internally and available for review. This level of transparency is significantly harder to achieve with a cloud-hosted service.
Qate's On-Premise Architecture
Qate's enterprise deployment is packaged as a Helm chart for Kubernetes. This is a deliberate architectural choice: Kubernetes is the de facto standard for enterprise container orchestration, and Helm charts provide a standardized, repeatable deployment mechanism that integrates with existing enterprise infrastructure workflows.
What Gets Deployed
A Qate on-premise installation consists of several components:
- Test orchestration service: Manages execution, scheduling, and result aggregation across available agents.
- AI agent workers: Scalable pods executing tests using Playwright for web and platform-specific agents for desktop and API testing.
- AI reasoning service: Handles conversational test creation, self-healing, and failure analysis. Communicates with AI models either self-hosted or through a configured endpoint.
- Database layer: Stores test definitions, history, results, and configuration in PostgreSQL.
- Web dashboard: User interface for creating tests and reviewing results, served from within the cluster.
- Artifact storage: Screenshots, videos, and reports stored in the organization's own object storage (S3-compatible).
How It Scales
Kubernetes-native scaling means the deployment adapts to workload automatically. During a large regression run, the orchestrator spins up additional agent worker pods for parallel execution. When the run completes, pods scale down. Resource limits are controlled through standard Kubernetes configuration.
For large enterprises, the architecture supports namespace isolation. Each team or project gets its own logical partition with independent scaling and access controls.
Deployment Process
The deployment follows standard enterprise Kubernetes patterns:
- Prerequisites: A Kubernetes cluster (1.24 or later), Helm 3, PostgreSQL, and S3-compatible object storage.
- Configuration: A values file specifying domain, database connection, storage endpoints, AI model configuration, resource limits, and authentication integration (SAML, OIDC, or LDAP).
- Installation: A single
helm installcommand deploys the entire stack. Upgrades usehelm upgrade. - Verification: Built-in health checks and a self-test suite validate the deployment.
The process typically takes less than a day. For step-by-step instructions, see our enterprise deployment guide. The deployment supports custom domains and integrates with existing TLS certificate management through standard Kubernetes ingress patterns.
Security Considerations
Beyond data sovereignty, the on-premise deployment addresses key enterprise security requirements:
- Network policies: Kubernetes network policies restrict pod communication and prevent lateral movement.
- RBAC integration: Access control integrates with the organization's identity provider through SSO.
- Encryption at rest: All stored data is encrypted using the organization's key management system.
- Audit logging: Every action is logged to a dedicated audit stream compatible with enterprise SIEM systems.
Enterprise Support
On-premise deployments include dedicated support from Qate's engineering team. This includes deployment assistance, architecture review, integration consulting, and priority issue resolution. For organizations in regulated industries, the support team has experience navigating compliance requirements for enterprise testing environments across banking, healthcare, insurance, and government sectors.
When On-Premise Makes Sense
Not every organization needs on-premise deployment. Qate's cloud offering is faster to start with, requires no infrastructure management, and is appropriate for organizations without strict data residency requirements. On-premise makes sense when:
- Regulatory requirements mandate data sovereignty.
- The application under test is on an internal network.
- Audit and compliance requirements demand infrastructure control.
- The organization's security policy prohibits sending application data to third parties.
- Scale requirements justify dedicated infrastructure.
For organizations that fall into these categories, on-premise AI testing is not a luxury. It is the only way to access the productivity benefits that AI testing provides.
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