What AI models does the agent use?

The agent has access to Claude, DeepSeek, Gemini, and other top models. It automatically routes to the best model for each task — you never have to think about API keys or token limits.

Yes. Each agent runs in an isolated environment. We don't train on your data, and you can export or delete everything at any time. The underlying engine (GoGogot) is open-source, so you can audit exactly how it works.

What can the agent actually do?

Browse the web, process files (CSV, PDF, images), run scheduled tasks, remember context across sessions, and communicate via Telegram or Slack. Think of it as a capable junior employee, not a chatbot.

How does memory work?

The agent maintains persistent memory across sessions. It remembers your preferences, past conversations, and task context. You can also explicitly tell it to remember or forget things.

Can I self-host the agent?

Absolutely. GoGogot is 100% open-source. Run it on your own hardware for free. cowork.ink is the managed version — we handle servers, model costs, updates, and uptime so you don't have to.

Is there a free trial?

We offer a 7-day money-back guarantee. Spin up an agent, give it real tasks, and if it doesn't save you time, we'll refund you — no questions asked.

What does it mean to self-host AI agents?

Self-hosting AI agents means running the agent software on infrastructure you control — your own servers, a private cloud VPC, or on-premise hardware — rather than using a vendor's cloud platform. Your data, agent configurations, and conversation history stay entirely within your infrastructure.

Do I need Kubernetes to self-host AI agents?

Kubernetes is the recommended production deployment method for scale and reliability, but it's not required. cowork.ink Business also supports Docker Compose for smaller deployments (1–20 agents). Kubernetes becomes valuable when you need horizontal scaling, automatic failover, and enterprise-grade reliability.

How many AI agents can I run on self-hosted infrastructure?

cowork.ink Business supports 200 agents per Kubernetes node. A 3-node cluster (modest hardware) handles 600 concurrent agents. Vertical scaling (adding nodes) is linear — adding a node adds 200 agent capacity.

What hardware do I need to self-host AI agents?

For API-based models (OpenAI, Anthropic): any 3 VMs with 4 vCPUs and 8GB RAM each runs comfortably. For self-hosted open-source models (Llama 3.3 70B): 2× NVIDIA A100 80GB or equivalent. For smaller models (Mistral 7B, Llama 8B): a single gaming GPU (RTX 4090, A10G) handles moderate loads.

Self-Hosted AI Agents for Business: Kubernetes Deployment in Under 5 Minutes

Deploy self-hosted AI agents for your business on Kubernetes in under 5 minutes. Step-by-step guide with Helm, RBAC, and open-source model setup. Full data isolation guaranteed.

Frequently Asked Questions

What does it mean to self-host AI agents?: Self-hosting AI agents means running the agent software on infrastructure you control — your own servers, a private cloud VPC, or on-premise hardware — rather than using a vendor's cloud platform. Your data, agent configurations, and conversation history stay entirely within your infrastructure.
Do I need Kubernetes to self-host AI agents?: Kubernetes is the recommended production deployment method for scale and reliability, but it's not required. cowork.ink Business also supports Docker Compose for smaller deployments (1–20 agents). Kubernetes becomes valuable when you need horizontal scaling, automatic failover, and enterprise-grade reliability.
How many AI agents can I run on self-hosted infrastructure?: cowork.ink Business supports 200 agents per Kubernetes node. A 3-node cluster (modest hardware) handles 600 concurrent agents. Vertical scaling (adding nodes) is linear — adding a node adds 200 agent capacity.
What hardware do I need to self-host AI agents?: For API-based models (OpenAI, Anthropic): any 3 VMs with 4 vCPUs and 8GB RAM each runs comfortably. For self-hosted open-source models (Llama 3.3 70B): 2× NVIDIA A100 80GB or equivalent. For smaller models (Mistral 7B, Llama 8B): a single gaming GPU (RTX 4090, A10G) handles moderate loads.