Enterprise-Grade Agents on Your Own Kubernetes
Deploy on-premises in minutes with a single Helm chart. Achieve full data control, isolation, and scale on your infrastructure.
True Data Sovereignty
Keep your data private. All conversations, business logic, and PII remain entirely within your private network and Kubernetes cluster. xpander.ai only makes secure, outbound connections.
Drop-in Kubernetes Native
No need to adopt a new runtime or build your own. xpander runs your agents just like you run your own applications, fitting seamlessly into your existing K8s observability, logging, and security pipelines. Scale with your cluster.
Built for Isolated Environments
Regulated Industries
Meet strict compliance mandates. Deploying to your private cloud is the only way to handle highly sensitive customer data, ensuring all AI processing and model execution adheres to industry regulations like HIPAA and GDPR.
Isolated Network Environments
For air-gapped or VPC-only networks. Full control over egress rules and API key management, with the ability to inject custom secrets (e.g., LLM keys) directly from your Kubernetes environment.
Maximizing Resource Utilization
Optimize costs and latency. Run agents alongside existing business applications and shared infrastructure. Dynamically allocate CPU and memory using K8s resource limits to maximize performance.
Frequently Asked Questions
Frequently Asked Questions
Deployment & Infrastructure
What deployment options does xpander.ai support for enterprise environments?
xpander.ai supports multiple deployment options: fully managed cloud, self-hosted on your Kubernetes clusters, or hybrid configurations. For companies with strict data residency requirements, the self-hosted option handles all AI processing within your private network while maintaining integration with xpander's control plane for updates and managing agents at scale. Examples include running autonomous agents for customer support tickets or multi-step workflow automation.
What deployment options does xpander.ai support for enterprise environments?
xpander.ai supports multiple deployment options: fully managed cloud, self-hosted on your Kubernetes clusters, or hybrid configurations. For companies with strict data residency requirements, the self-hosted option handles all AI processing within your private network while maintaining integration with xpander's control plane for updates and managing agents at scale. Examples include running autonomous agents for customer support tickets or multi-step workflow automation.
What deployment options does xpander.ai support for enterprise environments?
xpander.ai supports multiple deployment options: fully managed cloud, self-hosted on your Kubernetes clusters, or hybrid configurations. For companies with strict data residency requirements, the self-hosted option handles all AI processing within your private network while maintaining integration with xpander's control plane for updates and managing agents at scale. Examples include running autonomous agents for customer support tickets or multi-step workflow automation.
How do I integrate xpander.ai with existing Kubernetes clusters?
Integration is straightforward: deploy with a single Helm chart and xpander runs alongside your existing applications. It fits into your current K8s observability, logging, and security pipelines without requiring a new runtime. The platform handles orchestration, scaling, and agent lifecycle management automatically.
How do I integrate xpander.ai with existing Kubernetes clusters?
Integration is straightforward: deploy with a single Helm chart and xpander runs alongside your existing applications. It fits into your current K8s observability, logging, and security pipelines without requiring a new runtime. The platform handles orchestration, scaling, and agent lifecycle management automatically.
How do I integrate xpander.ai with existing Kubernetes clusters?
Integration is straightforward: deploy with a single Helm chart and xpander runs alongside your existing applications. It fits into your current K8s observability, logging, and security pipelines without requiring a new runtime. The platform handles orchestration, scaling, and agent lifecycle management automatically.
What are the prerequisites for self-hosted Kubernetes deployment?
You need Kubernetes 1.20+, Helm 3.12+, an Ingress Controller, a Storage Class for persistent volumes, and TLS certificates. The open architecture integrates with your existing cluster setup, no proprietary runtime required. Installation takes minutes and the platform auto-configures components including Agent Controller, AI Gateway, and Agent Worker.
What are the prerequisites for self-hosted Kubernetes deployment?
You need Kubernetes 1.20+, Helm 3.12+, an Ingress Controller, a Storage Class for persistent volumes, and TLS certificates. The open architecture integrates with your existing cluster setup, no proprietary runtime required. Installation takes minutes and the platform auto-configures components including Agent Controller, AI Gateway, and Agent Worker.
What are the prerequisites for self-hosted Kubernetes deployment?
You need Kubernetes 1.20+, Helm 3.12+, an Ingress Controller, a Storage Class for persistent volumes, and TLS certificates. The open architecture integrates with your existing cluster setup, no proprietary runtime required. Installation takes minutes and the platform auto-configures components including Agent Controller, AI Gateway, and Agent Worker.
Can I run AI agents in air-gapped environments?
Yes. xpander.ai supports fully air-gapped deployment where all components run entirely within your network with no external connectivity required. This is ideal for regulated industries, government, and organizations with strict security policies.
Can I run AI agents in air-gapped environments?
Yes. xpander.ai supports fully air-gapped deployment where all components run entirely within your network with no external connectivity required. This is ideal for regulated industries, government, and organizations with strict security policies.
Can I run AI agents in air-gapped environments?
Yes. xpander.ai supports fully air-gapped deployment where all components run entirely within your network with no external connectivity required. This is ideal for regulated industries, government, and organizations with strict security policies.
Can AI agents access internal databases and APIs within my VPC?
Yes. Container agents run inside your VPC with full access to internal databases, APIs, and services. This is ideal for organizations in regulated industries like insurance or finance where data must stay on-premises. xpander.ai is framework-agnostic and enterprise-ready, complementing your existing technical infrastructure while automating complex workflows that require access to sensitive internal systems.
Can AI agents access internal databases and APIs within my VPC?
Yes. Container agents run inside your VPC with full access to internal databases, APIs, and services. This is ideal for organizations in regulated industries like insurance or finance where data must stay on-premises. xpander.ai is framework-agnostic and enterprise-ready, complementing your existing technical infrastructure while automating complex workflows that require access to sensitive internal systems.
Can AI agents access internal databases and APIs within my VPC?
Yes. Container agents run inside your VPC with full access to internal databases, APIs, and services. This is ideal for organizations in regulated industries like insurance or finance where data must stay on-premises. xpander.ai is framework-agnostic and enterprise-ready, complementing your existing technical infrastructure while automating complex workflows that require access to sensitive internal systems.
How do I productionize AI agents built with different frameworks?
xpander.ai simplifies moving from prototypes to production for agents built with LangChain, CrewAI, AutoGen, or custom code. The agent builder tools handle deployment, scaling, and monitoring so development teams can focus on agent logic. The platform is compatible with agentic frameworks and supports general-purpose or specialized agent use cases. Tips: start with the Helm chart deployment and use xpander's CLI for local development.
How do I productionize AI agents built with different frameworks?
xpander.ai simplifies moving from prototypes to production for agents built with LangChain, CrewAI, AutoGen, or custom code. The agent builder tools handle deployment, scaling, and monitoring so development teams can focus on agent logic. The platform is compatible with agentic frameworks and supports general-purpose or specialized agent use cases. Tips: start with the Helm chart deployment and use xpander's CLI for local development.
How do I productionize AI agents built with different frameworks?
xpander.ai simplifies moving from prototypes to production for agents built with LangChain, CrewAI, AutoGen, or custom code. The agent builder tools handle deployment, scaling, and monitoring so development teams can focus on agent logic. The platform is compatible with agentic frameworks and supports general-purpose or specialized agent use cases. Tips: start with the Helm chart deployment and use xpander's CLI for local development.
Platform Capabilities
How does xpander.ai compare to other AI agent platforms for Kubernetes deployment?
In comparison to managed platforms that require sending data externally, xpander.ai deploys fully inside your own Kubernetes clusters. This gives you complete data sovereignty while still providing enterprise-grade orchestration, observability, and tracing. You get the best of both worlds: managed services capabilities with the control of self-hosted infrastructure. The platform integrates well with your existing Kubernetes security stack and cloud-native ecosystems.
How does xpander.ai compare to other AI agent platforms for Kubernetes deployment?
In comparison to managed platforms that require sending data externally, xpander.ai deploys fully inside your own Kubernetes clusters. This gives you complete data sovereignty while still providing enterprise-grade orchestration, observability, and tracing. You get the best of both worlds: managed services capabilities with the control of self-hosted infrastructure. The platform integrates well with your existing Kubernetes security stack and cloud-native ecosystems.
How does xpander.ai compare to other AI agent platforms for Kubernetes deployment?
In comparison to managed platforms that require sending data externally, xpander.ai deploys fully inside your own Kubernetes clusters. This gives you complete data sovereignty while still providing enterprise-grade orchestration, observability, and tracing. You get the best of both worlds: managed services capabilities with the control of self-hosted infrastructure. The platform integrates well with your existing Kubernetes security stack and cloud-native ecosystems.
Which AI agent frameworks are supported for containerized deployment?
xpander.ai provides compatibility and interoperability with popular frameworks including LangChain, CrewAI, AutoGen, Agno, and custom implementations. Developers can build with their preferred tools and deploy as containerized workloads. The platform abstracts away infrastructure complexity so your teams can focus on agent logic rather than operations. You can also reuse existing agent code and bots across different use cases.
Which AI agent frameworks are supported for containerized deployment?
xpander.ai provides compatibility and interoperability with popular frameworks including LangChain, CrewAI, AutoGen, Agno, and custom implementations. Developers can build with their preferred tools and deploy as containerized workloads. The platform abstracts away infrastructure complexity so your teams can focus on agent logic rather than operations. You can also reuse existing agent code and bots across different use cases.
Which AI agent frameworks are supported for containerized deployment?
xpander.ai provides compatibility and interoperability with popular frameworks including LangChain, CrewAI, AutoGen, Agno, and custom implementations. Developers can build with their preferred tools and deploy as containerized workloads. The platform abstracts away infrastructure complexity so your teams can focus on agent logic rather than operations. You can also reuse existing agent code and bots across different use cases.
How does xpander.ai compare vs alternatives for internal agent deployment?
Unlike alternatives that focus only on serverless or external hosting, xpander.ai supports multi-framework agents running on your internal corporate infrastructure. It provides a complete backend for handling agent execution while supporting best practices for MLOps and AI deployment. Whether you need code review assistants, ticketing automation, or complex business workflows, they run securely within your environment.
How does xpander.ai compare vs alternatives for internal agent deployment?
Unlike alternatives that focus only on serverless or external hosting, xpander.ai supports multi-framework agents running on your internal corporate infrastructure. It provides a complete backend for handling agent execution while supporting best practices for MLOps and AI deployment. Whether you need code review assistants, ticketing automation, or complex business workflows, they run securely within your environment.
How does xpander.ai compare vs alternatives for internal agent deployment?
Unlike alternatives that focus only on serverless or external hosting, xpander.ai supports multi-framework agents running on your internal corporate infrastructure. It provides a complete backend for handling agent execution while supporting best practices for MLOps and AI deployment. Whether you need code review assistants, ticketing automation, or complex business workflows, they run securely within your environment.
Can xpander.ai be used as a Backend-as-a-Service (BaaS) for AI agents?
Yes. xpander.ai provides backend-as-a-service capabilities for productionizing AI agents and models. It handles infrastructure, scaling, and orchestration so your company can focus on agent logic. The K8s-native architecture fits into existing DevOps and CI/CD pipelines without rewriting your deployment tooling. Deploy multi-cloud or on-premises based on your requirements.
Can xpander.ai be used as a Backend-as-a-Service (BaaS) for AI agents?
Yes. xpander.ai provides backend-as-a-service capabilities for productionizing AI agents and models. It handles infrastructure, scaling, and orchestration so your company can focus on agent logic. The K8s-native architecture fits into existing DevOps and CI/CD pipelines without rewriting your deployment tooling. Deploy multi-cloud or on-premises based on your requirements.
Can xpander.ai be used as a Backend-as-a-Service (BaaS) for AI agents?
Yes. xpander.ai provides backend-as-a-service capabilities for productionizing AI agents and models. It handles infrastructure, scaling, and orchestration so your company can focus on agent logic. The K8s-native architecture fits into existing DevOps and CI/CD pipelines without rewriting your deployment tooling. Deploy multi-cloud or on-premises based on your requirements.
Does xpander.ai support no-code agent building for businesses?
xpander.ai offers both no-code and code-first approaches. Non-technical teams can build agents using the visual Workbench, while developers can write custom agent software with full control. Both options benefit from the same enterprise-grade operating environment — containerized deployment, security, and observability. Agents can draft responses, automate workflows, or handle complex multi-step tasks.
Does xpander.ai support no-code agent building for businesses?
xpander.ai offers both no-code and code-first approaches. Non-technical teams can build agents using the visual Workbench, while developers can write custom agent software with full control. Both options benefit from the same enterprise-grade operating environment — containerized deployment, security, and observability. Agents can draft responses, automate workflows, or handle complex multi-step tasks.
Does xpander.ai support no-code agent building for businesses?
xpander.ai offers both no-code and code-first approaches. Non-technical teams can build agents using the visual Workbench, while developers can write custom agent software with full control. Both options benefit from the same enterprise-grade operating environment — containerized deployment, security, and observability. Agents can draft responses, automate workflows, or handle complex multi-step tasks.
Can xpander.ai connect to enterprise systems like Salesforce?
Yes. Containerized agents can connect to Salesforce, internal APIs, databases, MCP servers, and other enterprise systems within your network. Use built-in connectors or integrate custom MCP servers for specialized tools. The agentic architecture supports LLMs from multiple providers while keeping sensitive data on-premises. Build RAG-powered knowledge agents, RFP response bots, or RPA-compatible automations that integrate with your existing tech stack.
Can xpander.ai connect to enterprise systems like Salesforce?
Yes. Containerized agents can connect to Salesforce, internal APIs, databases, MCP servers, and other enterprise systems within your network. Use built-in connectors or integrate custom MCP servers for specialized tools. The agentic architecture supports LLMs from multiple providers while keeping sensitive data on-premises. Build RAG-powered knowledge agents, RFP response bots, or RPA-compatible automations that integrate with your existing tech stack.
Can xpander.ai connect to enterprise systems like Salesforce?
Yes. Containerized agents can connect to Salesforce, internal APIs, databases, MCP servers, and other enterprise systems within your network. Use built-in connectors or integrate custom MCP servers for specialized tools. The agentic architecture supports LLMs from multiple providers while keeping sensitive data on-premises. Build RAG-powered knowledge agents, RFP response bots, or RPA-compatible automations that integrate with your existing tech stack.
Use Cases & Adoption
How does xpander.ai help large enterprises scale AI agent deployment?
xpander.ai provides standardized infrastructure that multi-department teams can share. Instead of each department building custom solutions, you deploy one platform that handles agent runtime, orchestration, monitoring, and governance. This enables scaling from a few agents to hundreds across the organization with consistent security and observability. Top enterprise features include multi-agent workflows, built-in integrations, and full traceability.
How does xpander.ai help large enterprises scale AI agent deployment?
xpander.ai provides standardized infrastructure that multi-department teams can share. Instead of each department building custom solutions, you deploy one platform that handles agent runtime, orchestration, monitoring, and governance. This enables scaling from a few agents to hundreds across the organization with consistent security and observability. Top enterprise features include multi-agent workflows, built-in integrations, and full traceability.
How does xpander.ai help large enterprises scale AI agent deployment?
xpander.ai provides standardized infrastructure that multi-department teams can share. Instead of each department building custom solutions, you deploy one platform that handles agent runtime, orchestration, monitoring, and governance. This enables scaling from a few agents to hundreds across the organization with consistent security and observability. Top enterprise features include multi-agent workflows, built-in integrations, and full traceability.
What use cases does containerized agent deployment automate?
xpander.ai helps automate internal workflows, customer support handling, ticket management, document processing, and more. Build AI assistants that operate on sensitive corporate data without leaving your network. The platform supports everything from simple bots to sophisticated multi-agent systems with human-in-the-loop review capabilities.
What use cases does containerized agent deployment automate?
xpander.ai helps automate internal workflows, customer support handling, ticket management, document processing, and more. Build AI assistants that operate on sensitive corporate data without leaving your network. The platform supports everything from simple bots to sophisticated multi-agent systems with human-in-the-loop review capabilities.
What use cases does containerized agent deployment automate?
xpander.ai helps automate internal workflows, customer support handling, ticket management, document processing, and more. Build AI assistants that operate on sensitive corporate data without leaving your network. The platform supports everything from simple bots to sophisticated multi-agent systems with human-in-the-loop review capabilities.
What helpdesk and support automation features are available?
xpander.ai enables automated helpdesk solutions where AI agents handle ticket triage, response drafting, and escalation. Agents can access company documentation and knowledge bases to provide accurate support. Similar to other enterprise platforms, it supports departments across your organization while keeping sensitive data within your infrastructure.
What helpdesk and support automation features are available?
xpander.ai enables automated helpdesk solutions where AI agents handle ticket triage, response drafting, and escalation. Agents can access company documentation and knowledge bases to provide accurate support. Similar to other enterprise platforms, it supports departments across your organization while keeping sensitive data within your infrastructure.
What helpdesk and support automation features are available?
xpander.ai enables automated helpdesk solutions where AI agents handle ticket triage, response drafting, and escalation. Agents can access company documentation and knowledge bases to provide accurate support. Similar to other enterprise platforms, it supports departments across your organization while keeping sensitive data within your infrastructure.
How does xpander.ai support enterprise AI agent adoption across teams?
xpander.ai accelerates AI agent adoption by providing a standardized platform that technical and non-technical teams can use. Build knowledge retrieval agents, document processing bots, or custom automations without differences in how they're deployed and monitored. The platform supports organizations scaling from pilot projects to production-ready deployments across multiple business units.
How does xpander.ai support enterprise AI agent adoption across teams?
xpander.ai accelerates AI agent adoption by providing a standardized platform that technical and non-technical teams can use. Build knowledge retrieval agents, document processing bots, or custom automations without differences in how they're deployed and monitored. The platform supports organizations scaling from pilot projects to production-ready deployments across multiple business units.
How does xpander.ai support enterprise AI agent adoption across teams?
xpander.ai accelerates AI agent adoption by providing a standardized platform that technical and non-technical teams can use. Build knowledge retrieval agents, document processing bots, or custom automations without differences in how they're deployed and monitored. The platform supports organizations scaling from pilot projects to production-ready deployments across multiple business units.
What types of generative AI agents can I build?
Build generative agents for document generation, RFP response drafting, content creation, code review, and more. Agents can work with internal docs through Retrieval Augmented Generation (RAG), handle employee requests via IT service desk automation, and integrate with enterprise systems through strong connectors. xpander.ai complements your existing AI investments by providing the infrastructure layer.
What types of generative AI agents can I build?
Build generative agents for document generation, RFP response drafting, content creation, code review, and more. Agents can work with internal docs through Retrieval Augmented Generation (RAG), handle employee requests via IT service desk automation, and integrate with enterprise systems through strong connectors. xpander.ai complements your existing AI investments by providing the infrastructure layer.
What types of generative AI agents can I build?
Build generative agents for document generation, RFP response drafting, content creation, code review, and more. Agents can work with internal docs through Retrieval Augmented Generation (RAG), handle employee requests via IT service desk automation, and integrate with enterprise systems through strong connectors. xpander.ai complements your existing AI investments by providing the infrastructure layer.
Can agents automate IT service desk ticket resolution?
Yes. Build agents that handle ticket resolution by accessing internal knowledge bases, docs, and system APIs. Agents can triage requests, draft responses, escalate complex issues, and track resolution status—all while running securely within your infrastructure.
Can agents automate IT service desk ticket resolution?
Yes. Build agents that handle ticket resolution by accessing internal knowledge bases, docs, and system APIs. Agents can triage requests, draft responses, escalate complex issues, and track resolution status—all while running securely within your infrastructure.
Can agents automate IT service desk ticket resolution?
Yes. Build agents that handle ticket resolution by accessing internal knowledge bases, docs, and system APIs. Agents can triage requests, draft responses, escalate complex issues, and track resolution status—all while running securely within your infrastructure.
The AI Agent Platform
for Enterprise Teams
Build with any framework. Deploy on any cloud. Orchestration, security, and observability built in.
The AI Agent Platform
for Enterprise Teams
Everything you need to build, deploy,
and scale your AI agents
The AI Agent Platform for Enterprise Teams
Build with any framework. Deploy on any cloud. Orchestration, security, and observability built in.
