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.

Is it cheaper to run AI agents locally or in the cloud?

It depends on volume. Cloud APIs are cheaper below ~2 million tokens/day because you pay zero upfront. Above that threshold, local hardware pays itself off in 3–5 months and can be 8–18x cheaper per million tokens at high utilization. Use our [AI agent cost guide](/blog/ai-agent-cost/) to run the numbers for your workload.

Which is more private — local AI or cloud AI?

Local AI is inherently more private because data never leaves your machine or network. Cloud providers have strong security controls but your data crosses the internet and resides on shared infrastructure. For HIPAA, GDPR, or classified workloads, local deployment is the standard approach — though it requires your own security hardening.

Can local AI agents match cloud AI in quality and capability?

For many tasks, yes. Open-weight models improved ~30% year-over-year from 2024–2025, and Llama 3 70B now rivals GPT-4 on structured coding and reasoning tasks. For frontier capabilities — long-context reasoning, multimodal tasks, the very latest models — cloud still leads. Most teams run a hybrid: local for high-volume or sensitive tasks, cloud for complex reasoning.

What hardware do I need to run an AI agent locally?

Minimum for a 7B model: 8–16 GB RAM, 4-core CPU. For a 13B model: 32 GB RAM and a 16 GB VRAM GPU. For 70B models you need an RTX 4090 (24 GB VRAM) or equivalent. 4-bit quantization lets you run 32B models in 16 GB RAM with less than 2% quality loss.

What is a hybrid AI agent deployment?

Hybrid deployments route tasks between local and cloud models based on sensitivity, complexity, or cost. For example, you run sensitive customer data through a local Ollama instance but send complex multi-step reasoning tasks to a cloud API. 55% of enterprises already use this approach according to a 2025 a16z survey. See our [multi-agent systems guide](/blog/multi-agent-systems/) for orchestration patterns.

AI Agent Local vs Cloud: Cost, Speed & Privacy (2026)

LOCAL vs CLOUD AI agents — REAL cost numbers, latency benchmarks, and privacy trade-offs. Find the right deployment for your team. Compare now!

Frequently Asked Questions

Is it cheaper to run AI agents locally or in the cloud?: It depends on volume. Cloud APIs are cheaper below ~2 million tokens/day because you pay zero upfront. Above that threshold, local hardware pays itself off in 3–5 months and can be 8–18x cheaper per million tokens at high utilization. Use our [AI agent cost guide](/blog/ai-agent-cost/) to run the numbers for your workload.
Which is more private — local AI or cloud AI?: Local AI is inherently more private because data never leaves your machine or network. Cloud providers have strong security controls but your data crosses the internet and resides on shared infrastructure. For HIPAA, GDPR, or classified workloads, local deployment is the standard approach — though it requires your own security hardening.
Can local AI agents match cloud AI in quality and capability?: For many tasks, yes. Open-weight models improved ~30% year-over-year from 2024–2025, and Llama 3 70B now rivals GPT-4 on structured coding and reasoning tasks. For frontier capabilities — long-context reasoning, multimodal tasks, the very latest models — cloud still leads. Most teams run a hybrid: local for high-volume or sensitive tasks, cloud for complex reasoning.
What hardware do I need to run an AI agent locally?: Minimum for a 7B model: 8–16 GB RAM, 4-core CPU. For a 13B model: 32 GB RAM and a 16 GB VRAM GPU. For 70B models you need an RTX 4090 (24 GB VRAM) or equivalent. 4-bit quantization lets you run 32B models in 16 GB RAM with less than 2% quality loss.
What is a hybrid AI agent deployment?: Hybrid deployments route tasks between local and cloud models based on sensitivity, complexity, or cost. For example, you run sensitive customer data through a local Ollama instance but send complex multi-step reasoning tasks to a cloud API. 55% of enterprises already use this approach according to a 2025 a16z survey. See our [multi-agent systems guide](/blog/multi-agent-systems/) for orchestration patterns.