Sep 8, 2025
Bringing AI Observability Behind the Firewall: Deploying On-Premise AI
Sam Goldfield
Sales Engineer
Sam Goldfield
Sales Engineer
When Replit’s live infrastructure code serving over 1000 companies went down, they thought it was a human error. Instead, it was the act of an AI agent.
Often, these AI agents are acting on proprietary data and workflows. Sectors like financial services, telecommunications, and healthcare have repositories of relevant, helpful data that could benefit AI. Yet strict compliance controls, mandatory audit trails, and zero tolerance for data breaches make it challenging to unlock this data to enable AI to operate outside of their environments. This constraint means regulated enterprises must solve the challenges of rogue AI while maintaining observability and guardrails for it.
The only way to do that is to put the guardrails and observability into the same environment where your AI is executing. This requires not just AI capabilities, but comprehensive AI observability tools deployed on-premise alongside them.
AI Observability Challenges in Regulated Industries
Most observability and open source eval solutions rely on cloud-hosted deployments. For regulated organizations, this approach is often untenable. The alternative is on-premise deployments hosted and managed by the client. While this keeps all of an organization's sensitive data and observability under its control, it can create significant operational overhead and logistical challenges.
The problem is that many gen AI observability platforms are not built with the same regard for managing data security, access controls, and scale that enterprises are used to. These gaps mean that AI observability solutions may fall short of actually solving the challenges of regulated companies, causing cloud security or technical teams to struggle to adopt their tools. To enable enterprise AI adoption and unlock the value of customer data safely and securely, greater observability customization and regulatory control are necessary.
Why On-Premise Gen AI Observability Matters
Regulated companies have distinct sets of non-negotiable requirements for their operations in different geographies. These include laws prohibiting data from leaving a boundary, auditable structured logs, and service-level agreements. Conversely, gen AI observability means having access to inputs such as prompts and tokens, retrieved documents, tools, and outputs. These will all contain sensitive data that would otherwise be sent to a cloud offering that may not provide the same robust controls that an organization expects. That means organizations seeking to adopt AI will need a solution to meet them where they are. This often means forgoing a public SaaS offering for an on-premise solution.
Galileo’s On-Premise Solution
To adopt an AI evaluation or observability tool, your team needs confidence. Confidence that your data will be secure and that measurements will be accurate. At Galileo, we’ve tailored our solution to fit the customizable needs of regulated industries, whether in the cloud, hybrid, or fully on-prem.
The Galileo platform offers a suite of tools to measure and also continue to iterate on your evaluations. We do this by providing several out-of-the-box metrics that can leverage human feedback to self-improve. These are organized by problem space, so ‘agentic reliability’ metrics and ‘brand safety’ metrics are only a click away. These metrics also extend to our real-time guardrail solution, Protect. By configuring rulesets in Protect, your metrics will actively intercept or block interactions that could cause issues, such as prompt injection.
Most importantly, we don’t stop at out-of-the-box metrics. We understand that different teams need different evaluation methods. Galileo enables teams to customize and fine-tune any of our metrics and to create their own custom metrics, either through simple self-serve flows or with expert advice.
Offering these metrics as guardrails in the same space as the application means that sensitive data for the application doesn’t leave the environment. This is because our metrics can be offered as small language models (SLM’s) that can run entirely on the compute available in the environment, which also yields faster and more cost-effective evaluations because Luna-2 SLM’s are only focused on the metric they aim to measure. Because the environment can be a virtual private cloud (VPC) or on bare metal, you can preserve your preferences for geographic or safety-related compliance requirements when running these models.
Built for enterprise on-prem
Galileo addresses these needs through a deployment model designed for regulated clients that is flexible enough to deploy to most environments. We do this through three parts:
User Facing Layer
Infrastructure Layer
Infrastructure Observability Layer
The user-facing layer consists of our user interface in the web browser and our comprehensive SDKs for Python and Typescript. Developers and subject matter experts can quickly use Galileo with just a few clicks or lines of code. Plus, these interfaces are kept up to date with constant improvements to ensure that these processes evolve as your AI applications do.
Galileo’s infrastructure layer is composed of cluster and workload orchestration through Kubernetes. We often deliver these as Helm charts that deploy containerized microservices. These look something like this:
apiVersion: v2 name: galileo-base type: application version: 1.11.4 appVersion: "1.16.0"
Our deployments abstract away many tedious or difficult tasks associated with managing and maintaining components. These services use a mix of stateless and stateful services so that services only have a history if it is relevant for their operation. By limiting and clearly defining boundaries for these services, we ensure that we limit risks associated with moving highly sensitive data. As one example, gateways for specific, customer-managed LLMs can be accessed while not permitting access to the public LLM endpoints. Access and retention policies are also configured here to ensure that data is granted on a need-to-know basis through things like OIDC-based SSO.
Our infrastructure observability layer means we also have audit logs and visibility into all actions taken inside Galileo. When you have infrastructure that is playing an important role in regulatory activities, you need to make sure it is working as intended. We use services such as Grafana and Sentry to understand what is happening, which is important for auditing internal systems. Sensitive data remains in the cluster, while logs help provide insight into cluster usage and issues.
Secure Architecture Makes a Difference
For regulated organizations, observability in AI is not optional. It is a compliance safeguard and security measure that enables operational excellence. On-premise deployments ensure that sensitive data and telemetry stay within the boundaries required by law and policy. Galileo’s architecture is set to meet these changes with a rare combination of modern engineering and compliance-focused controls.
With clients and partners like John Deere, HP, Comcast, and NTT, Galileo focuses on enterprise technology and deployment flexibility that meets regulatory standards while enabling internal teams. Since each element of Galileo’s architecture can be deployed inside a company’s own Kubernetes cluster, Galileo provides regulated clients with the end-to-end control they require. This even extends to using some of our services, such as object stores, through managed services in cloud providers.
Our clients can control sensitive data flows and access permissions at both infrastructure and user-facing levels. Audit logging helps provide visibility into cluster actions for reporting. Finally, we ensure the process is as smooth as possible through our dedicated platform and developer operations team, which can provide white glove support.
For enterprises that need to minimize risks and meet sensitive compliance and security standards, Galileo is the AI observability and evaluations vendor of choice.
Ready to learn more? Contact our team to start a conversation.
When Replit’s live infrastructure code serving over 1000 companies went down, they thought it was a human error. Instead, it was the act of an AI agent.
Often, these AI agents are acting on proprietary data and workflows. Sectors like financial services, telecommunications, and healthcare have repositories of relevant, helpful data that could benefit AI. Yet strict compliance controls, mandatory audit trails, and zero tolerance for data breaches make it challenging to unlock this data to enable AI to operate outside of their environments. This constraint means regulated enterprises must solve the challenges of rogue AI while maintaining observability and guardrails for it.
The only way to do that is to put the guardrails and observability into the same environment where your AI is executing. This requires not just AI capabilities, but comprehensive AI observability tools deployed on-premise alongside them.
AI Observability Challenges in Regulated Industries
Most observability and open source eval solutions rely on cloud-hosted deployments. For regulated organizations, this approach is often untenable. The alternative is on-premise deployments hosted and managed by the client. While this keeps all of an organization's sensitive data and observability under its control, it can create significant operational overhead and logistical challenges.
The problem is that many gen AI observability platforms are not built with the same regard for managing data security, access controls, and scale that enterprises are used to. These gaps mean that AI observability solutions may fall short of actually solving the challenges of regulated companies, causing cloud security or technical teams to struggle to adopt their tools. To enable enterprise AI adoption and unlock the value of customer data safely and securely, greater observability customization and regulatory control are necessary.
Why On-Premise Gen AI Observability Matters
Regulated companies have distinct sets of non-negotiable requirements for their operations in different geographies. These include laws prohibiting data from leaving a boundary, auditable structured logs, and service-level agreements. Conversely, gen AI observability means having access to inputs such as prompts and tokens, retrieved documents, tools, and outputs. These will all contain sensitive data that would otherwise be sent to a cloud offering that may not provide the same robust controls that an organization expects. That means organizations seeking to adopt AI will need a solution to meet them where they are. This often means forgoing a public SaaS offering for an on-premise solution.
Galileo’s On-Premise Solution
To adopt an AI evaluation or observability tool, your team needs confidence. Confidence that your data will be secure and that measurements will be accurate. At Galileo, we’ve tailored our solution to fit the customizable needs of regulated industries, whether in the cloud, hybrid, or fully on-prem.
The Galileo platform offers a suite of tools to measure and also continue to iterate on your evaluations. We do this by providing several out-of-the-box metrics that can leverage human feedback to self-improve. These are organized by problem space, so ‘agentic reliability’ metrics and ‘brand safety’ metrics are only a click away. These metrics also extend to our real-time guardrail solution, Protect. By configuring rulesets in Protect, your metrics will actively intercept or block interactions that could cause issues, such as prompt injection.
Most importantly, we don’t stop at out-of-the-box metrics. We understand that different teams need different evaluation methods. Galileo enables teams to customize and fine-tune any of our metrics and to create their own custom metrics, either through simple self-serve flows or with expert advice.
Offering these metrics as guardrails in the same space as the application means that sensitive data for the application doesn’t leave the environment. This is because our metrics can be offered as small language models (SLM’s) that can run entirely on the compute available in the environment, which also yields faster and more cost-effective evaluations because Luna-2 SLM’s are only focused on the metric they aim to measure. Because the environment can be a virtual private cloud (VPC) or on bare metal, you can preserve your preferences for geographic or safety-related compliance requirements when running these models.
Built for enterprise on-prem
Galileo addresses these needs through a deployment model designed for regulated clients that is flexible enough to deploy to most environments. We do this through three parts:
User Facing Layer
Infrastructure Layer
Infrastructure Observability Layer
The user-facing layer consists of our user interface in the web browser and our comprehensive SDKs for Python and Typescript. Developers and subject matter experts can quickly use Galileo with just a few clicks or lines of code. Plus, these interfaces are kept up to date with constant improvements to ensure that these processes evolve as your AI applications do.
Galileo’s infrastructure layer is composed of cluster and workload orchestration through Kubernetes. We often deliver these as Helm charts that deploy containerized microservices. These look something like this:
apiVersion: v2 name: galileo-base type: application version: 1.11.4 appVersion: "1.16.0"
Our deployments abstract away many tedious or difficult tasks associated with managing and maintaining components. These services use a mix of stateless and stateful services so that services only have a history if it is relevant for their operation. By limiting and clearly defining boundaries for these services, we ensure that we limit risks associated with moving highly sensitive data. As one example, gateways for specific, customer-managed LLMs can be accessed while not permitting access to the public LLM endpoints. Access and retention policies are also configured here to ensure that data is granted on a need-to-know basis through things like OIDC-based SSO.
Our infrastructure observability layer means we also have audit logs and visibility into all actions taken inside Galileo. When you have infrastructure that is playing an important role in regulatory activities, you need to make sure it is working as intended. We use services such as Grafana and Sentry to understand what is happening, which is important for auditing internal systems. Sensitive data remains in the cluster, while logs help provide insight into cluster usage and issues.
Secure Architecture Makes a Difference
For regulated organizations, observability in AI is not optional. It is a compliance safeguard and security measure that enables operational excellence. On-premise deployments ensure that sensitive data and telemetry stay within the boundaries required by law and policy. Galileo’s architecture is set to meet these changes with a rare combination of modern engineering and compliance-focused controls.
With clients and partners like John Deere, HP, Comcast, and NTT, Galileo focuses on enterprise technology and deployment flexibility that meets regulatory standards while enabling internal teams. Since each element of Galileo’s architecture can be deployed inside a company’s own Kubernetes cluster, Galileo provides regulated clients with the end-to-end control they require. This even extends to using some of our services, such as object stores, through managed services in cloud providers.
Our clients can control sensitive data flows and access permissions at both infrastructure and user-facing levels. Audit logging helps provide visibility into cluster actions for reporting. Finally, we ensure the process is as smooth as possible through our dedicated platform and developer operations team, which can provide white glove support.
For enterprises that need to minimize risks and meet sensitive compliance and security standards, Galileo is the AI observability and evaluations vendor of choice.
Ready to learn more? Contact our team to start a conversation.
When Replit’s live infrastructure code serving over 1000 companies went down, they thought it was a human error. Instead, it was the act of an AI agent.
Often, these AI agents are acting on proprietary data and workflows. Sectors like financial services, telecommunications, and healthcare have repositories of relevant, helpful data that could benefit AI. Yet strict compliance controls, mandatory audit trails, and zero tolerance for data breaches make it challenging to unlock this data to enable AI to operate outside of their environments. This constraint means regulated enterprises must solve the challenges of rogue AI while maintaining observability and guardrails for it.
The only way to do that is to put the guardrails and observability into the same environment where your AI is executing. This requires not just AI capabilities, but comprehensive AI observability tools deployed on-premise alongside them.
AI Observability Challenges in Regulated Industries
Most observability and open source eval solutions rely on cloud-hosted deployments. For regulated organizations, this approach is often untenable. The alternative is on-premise deployments hosted and managed by the client. While this keeps all of an organization's sensitive data and observability under its control, it can create significant operational overhead and logistical challenges.
The problem is that many gen AI observability platforms are not built with the same regard for managing data security, access controls, and scale that enterprises are used to. These gaps mean that AI observability solutions may fall short of actually solving the challenges of regulated companies, causing cloud security or technical teams to struggle to adopt their tools. To enable enterprise AI adoption and unlock the value of customer data safely and securely, greater observability customization and regulatory control are necessary.
Why On-Premise Gen AI Observability Matters
Regulated companies have distinct sets of non-negotiable requirements for their operations in different geographies. These include laws prohibiting data from leaving a boundary, auditable structured logs, and service-level agreements. Conversely, gen AI observability means having access to inputs such as prompts and tokens, retrieved documents, tools, and outputs. These will all contain sensitive data that would otherwise be sent to a cloud offering that may not provide the same robust controls that an organization expects. That means organizations seeking to adopt AI will need a solution to meet them where they are. This often means forgoing a public SaaS offering for an on-premise solution.
Galileo’s On-Premise Solution
To adopt an AI evaluation or observability tool, your team needs confidence. Confidence that your data will be secure and that measurements will be accurate. At Galileo, we’ve tailored our solution to fit the customizable needs of regulated industries, whether in the cloud, hybrid, or fully on-prem.
The Galileo platform offers a suite of tools to measure and also continue to iterate on your evaluations. We do this by providing several out-of-the-box metrics that can leverage human feedback to self-improve. These are organized by problem space, so ‘agentic reliability’ metrics and ‘brand safety’ metrics are only a click away. These metrics also extend to our real-time guardrail solution, Protect. By configuring rulesets in Protect, your metrics will actively intercept or block interactions that could cause issues, such as prompt injection.
Most importantly, we don’t stop at out-of-the-box metrics. We understand that different teams need different evaluation methods. Galileo enables teams to customize and fine-tune any of our metrics and to create their own custom metrics, either through simple self-serve flows or with expert advice.
Offering these metrics as guardrails in the same space as the application means that sensitive data for the application doesn’t leave the environment. This is because our metrics can be offered as small language models (SLM’s) that can run entirely on the compute available in the environment, which also yields faster and more cost-effective evaluations because Luna-2 SLM’s are only focused on the metric they aim to measure. Because the environment can be a virtual private cloud (VPC) or on bare metal, you can preserve your preferences for geographic or safety-related compliance requirements when running these models.
Built for enterprise on-prem
Galileo addresses these needs through a deployment model designed for regulated clients that is flexible enough to deploy to most environments. We do this through three parts:
User Facing Layer
Infrastructure Layer
Infrastructure Observability Layer
The user-facing layer consists of our user interface in the web browser and our comprehensive SDKs for Python and Typescript. Developers and subject matter experts can quickly use Galileo with just a few clicks or lines of code. Plus, these interfaces are kept up to date with constant improvements to ensure that these processes evolve as your AI applications do.
Galileo’s infrastructure layer is composed of cluster and workload orchestration through Kubernetes. We often deliver these as Helm charts that deploy containerized microservices. These look something like this:
apiVersion: v2 name: galileo-base type: application version: 1.11.4 appVersion: "1.16.0"
Our deployments abstract away many tedious or difficult tasks associated with managing and maintaining components. These services use a mix of stateless and stateful services so that services only have a history if it is relevant for their operation. By limiting and clearly defining boundaries for these services, we ensure that we limit risks associated with moving highly sensitive data. As one example, gateways for specific, customer-managed LLMs can be accessed while not permitting access to the public LLM endpoints. Access and retention policies are also configured here to ensure that data is granted on a need-to-know basis through things like OIDC-based SSO.
Our infrastructure observability layer means we also have audit logs and visibility into all actions taken inside Galileo. When you have infrastructure that is playing an important role in regulatory activities, you need to make sure it is working as intended. We use services such as Grafana and Sentry to understand what is happening, which is important for auditing internal systems. Sensitive data remains in the cluster, while logs help provide insight into cluster usage and issues.
Secure Architecture Makes a Difference
For regulated organizations, observability in AI is not optional. It is a compliance safeguard and security measure that enables operational excellence. On-premise deployments ensure that sensitive data and telemetry stay within the boundaries required by law and policy. Galileo’s architecture is set to meet these changes with a rare combination of modern engineering and compliance-focused controls.
With clients and partners like John Deere, HP, Comcast, and NTT, Galileo focuses on enterprise technology and deployment flexibility that meets regulatory standards while enabling internal teams. Since each element of Galileo’s architecture can be deployed inside a company’s own Kubernetes cluster, Galileo provides regulated clients with the end-to-end control they require. This even extends to using some of our services, such as object stores, through managed services in cloud providers.
Our clients can control sensitive data flows and access permissions at both infrastructure and user-facing levels. Audit logging helps provide visibility into cluster actions for reporting. Finally, we ensure the process is as smooth as possible through our dedicated platform and developer operations team, which can provide white glove support.
For enterprises that need to minimize risks and meet sensitive compliance and security standards, Galileo is the AI observability and evaluations vendor of choice.
Ready to learn more? Contact our team to start a conversation.
When Replit’s live infrastructure code serving over 1000 companies went down, they thought it was a human error. Instead, it was the act of an AI agent.
Often, these AI agents are acting on proprietary data and workflows. Sectors like financial services, telecommunications, and healthcare have repositories of relevant, helpful data that could benefit AI. Yet strict compliance controls, mandatory audit trails, and zero tolerance for data breaches make it challenging to unlock this data to enable AI to operate outside of their environments. This constraint means regulated enterprises must solve the challenges of rogue AI while maintaining observability and guardrails for it.
The only way to do that is to put the guardrails and observability into the same environment where your AI is executing. This requires not just AI capabilities, but comprehensive AI observability tools deployed on-premise alongside them.
AI Observability Challenges in Regulated Industries
Most observability and open source eval solutions rely on cloud-hosted deployments. For regulated organizations, this approach is often untenable. The alternative is on-premise deployments hosted and managed by the client. While this keeps all of an organization's sensitive data and observability under its control, it can create significant operational overhead and logistical challenges.
The problem is that many gen AI observability platforms are not built with the same regard for managing data security, access controls, and scale that enterprises are used to. These gaps mean that AI observability solutions may fall short of actually solving the challenges of regulated companies, causing cloud security or technical teams to struggle to adopt their tools. To enable enterprise AI adoption and unlock the value of customer data safely and securely, greater observability customization and regulatory control are necessary.
Why On-Premise Gen AI Observability Matters
Regulated companies have distinct sets of non-negotiable requirements for their operations in different geographies. These include laws prohibiting data from leaving a boundary, auditable structured logs, and service-level agreements. Conversely, gen AI observability means having access to inputs such as prompts and tokens, retrieved documents, tools, and outputs. These will all contain sensitive data that would otherwise be sent to a cloud offering that may not provide the same robust controls that an organization expects. That means organizations seeking to adopt AI will need a solution to meet them where they are. This often means forgoing a public SaaS offering for an on-premise solution.
Galileo’s On-Premise Solution
To adopt an AI evaluation or observability tool, your team needs confidence. Confidence that your data will be secure and that measurements will be accurate. At Galileo, we’ve tailored our solution to fit the customizable needs of regulated industries, whether in the cloud, hybrid, or fully on-prem.
The Galileo platform offers a suite of tools to measure and also continue to iterate on your evaluations. We do this by providing several out-of-the-box metrics that can leverage human feedback to self-improve. These are organized by problem space, so ‘agentic reliability’ metrics and ‘brand safety’ metrics are only a click away. These metrics also extend to our real-time guardrail solution, Protect. By configuring rulesets in Protect, your metrics will actively intercept or block interactions that could cause issues, such as prompt injection.
Most importantly, we don’t stop at out-of-the-box metrics. We understand that different teams need different evaluation methods. Galileo enables teams to customize and fine-tune any of our metrics and to create their own custom metrics, either through simple self-serve flows or with expert advice.
Offering these metrics as guardrails in the same space as the application means that sensitive data for the application doesn’t leave the environment. This is because our metrics can be offered as small language models (SLM’s) that can run entirely on the compute available in the environment, which also yields faster and more cost-effective evaluations because Luna-2 SLM’s are only focused on the metric they aim to measure. Because the environment can be a virtual private cloud (VPC) or on bare metal, you can preserve your preferences for geographic or safety-related compliance requirements when running these models.
Built for enterprise on-prem
Galileo addresses these needs through a deployment model designed for regulated clients that is flexible enough to deploy to most environments. We do this through three parts:
User Facing Layer
Infrastructure Layer
Infrastructure Observability Layer
The user-facing layer consists of our user interface in the web browser and our comprehensive SDKs for Python and Typescript. Developers and subject matter experts can quickly use Galileo with just a few clicks or lines of code. Plus, these interfaces are kept up to date with constant improvements to ensure that these processes evolve as your AI applications do.
Galileo’s infrastructure layer is composed of cluster and workload orchestration through Kubernetes. We often deliver these as Helm charts that deploy containerized microservices. These look something like this:
apiVersion: v2 name: galileo-base type: application version: 1.11.4 appVersion: "1.16.0"
Our deployments abstract away many tedious or difficult tasks associated with managing and maintaining components. These services use a mix of stateless and stateful services so that services only have a history if it is relevant for their operation. By limiting and clearly defining boundaries for these services, we ensure that we limit risks associated with moving highly sensitive data. As one example, gateways for specific, customer-managed LLMs can be accessed while not permitting access to the public LLM endpoints. Access and retention policies are also configured here to ensure that data is granted on a need-to-know basis through things like OIDC-based SSO.
Our infrastructure observability layer means we also have audit logs and visibility into all actions taken inside Galileo. When you have infrastructure that is playing an important role in regulatory activities, you need to make sure it is working as intended. We use services such as Grafana and Sentry to understand what is happening, which is important for auditing internal systems. Sensitive data remains in the cluster, while logs help provide insight into cluster usage and issues.
Secure Architecture Makes a Difference
For regulated organizations, observability in AI is not optional. It is a compliance safeguard and security measure that enables operational excellence. On-premise deployments ensure that sensitive data and telemetry stay within the boundaries required by law and policy. Galileo’s architecture is set to meet these changes with a rare combination of modern engineering and compliance-focused controls.
With clients and partners like John Deere, HP, Comcast, and NTT, Galileo focuses on enterprise technology and deployment flexibility that meets regulatory standards while enabling internal teams. Since each element of Galileo’s architecture can be deployed inside a company’s own Kubernetes cluster, Galileo provides regulated clients with the end-to-end control they require. This even extends to using some of our services, such as object stores, through managed services in cloud providers.
Our clients can control sensitive data flows and access permissions at both infrastructure and user-facing levels. Audit logging helps provide visibility into cluster actions for reporting. Finally, we ensure the process is as smooth as possible through our dedicated platform and developer operations team, which can provide white glove support.
For enterprises that need to minimize risks and meet sensitive compliance and security standards, Galileo is the AI observability and evaluations vendor of choice.
Ready to learn more? Contact our team to start a conversation.
If you find this helpful and interesting,
Sam Goldfield