Many businesses are eager to bring AI into their operations. But most AI tools come with a hidden limitation: you do not control where they run, how they store data, or how deeply they integrate with your workflows. This limitation might not seem critical at first, but it quickly becomes a challenge as organizations scale, handle sensitive data, or require compliance with internal governance policies.

In this blog, we compare Dot with some of the most widely used cloud-only AI tools including ChatGPT, Gemini, Claude, Microsoft Copilot, Perplexity AI, and Sana AI. We explore what they offer, where they fall short, and why full control, compliance, and customization make a real difference when AI becomes a core part of your business stack.

This post focuses on what most comparisons miss: how much control your business actually has over the AI tools you rely on every day.

The Cloud-Only AI Tools Landscape

Let’s start by understanding what “cloud-only” means and why it matters.

Cloud-only AI tools are platforms that:

• Operate exclusively on the vendor’s infrastructure
• Do not offer on-premise or hybrid deployment models
• Depend on the provider’s predefined storage, security, and data handling policies
• Offer limited options for infrastructure-level customization

These tools are easy to access, quick to implement, and ideal for initial testing or non-sensitive use cases. However, for companies in regulated sectors or those handling confidential data, cloud-only solutions may introduce security and operational limitations.

Here’s a more detailed look at some of the most common cloud-only AI tools:

ChatGPT (OpenAI)

• Fully hosted on OpenAI infrastructure
• Great for personal productivity and developer experimentation
• Offers limited data control or integration depth
  → Read our full comparison: Dot vs. ChatGPT

Gemini (Google)

• Embedded within Google Workspace apps
• Powerful model for summarization, search, and writing tasks
• Bound to Google Cloud, with little room for hosting flexibility
  → Read our full comparison: Dot vs. Gemini

Claude (Anthropic)

• Known for long context and thoughtful responses
• Hosted fully on Anthropic’s platform
• Lacks deployment flexibility and direct data management
  → Read our full comparison: Claude vs. Dot

Microsoft Copilot

• Integrates deeply with Microsoft 365 apps
• A strong choice for teams already using Office tools
• No options for on-premise deployment or workflow customization

Perplexity AI

• Designed for rapid search and factual responses
• Consumer-friendly and web-based
• Not designed for enterprise data integration or compliance

Sana AI

• Focused on internal knowledge delivery and corporate learning
• Entirely hosted on Sana’s infrastructure
• Lacks private deployment or deep customization capabilities

What Dot Does Differently

Dot was designed from the beginning to support long-term, enterprise-grade AI use. It is not just another chatbot; it is a flexible, adaptable AI framework built for companies with real operational needs.

With Dot, teams can:

• Choose their hosting method including cloud, hybrid, and fully on-premise options
• Create complex workflows using orchestration of multiple AI agents
• Use and switch between multiple models (Claude, Mistral, Gemini, Cohere, and more)
• Build workflows using no-code tools while also enabling developer-level extensions
• Manage their own data storage, access controls, and compliance structure

This setup allows organizations to use AI not just as a tool but as part of their infrastructure. Unlike most cloud-only tools, Dot adapts to your operations instead of forcing your operations to adapt to the tool.

Why Hosting Options Matter

In industries like finance, healthcare, government, and legal services, data location and infrastructure control are non-negotiable. Where and how your AI operates can determine whether you meet industry regulations, protect intellectual property, or maintain customer trust.

Dot offers:

• Cloud hosting for organizations needing speed and convenience
• Hybrid deployment to separate sensitive and non-sensitive workloads
• On-premise deployment for full data sovereignty and internal infrastructure use

In contrast, cloud-only tools centralize everything in the vendor's infrastructure which may conflict with internal IT policies or regional compliance laws. The ability to choose your hosting method is often the line between experimentation and real implementation.

Compliance You Can Define

AI tools process sensitive data. That means your organization is responsible for how it is handled, secured, and stored.

Dot helps you meet your own standards by offering:

• GDPR compliance and full alignment with regional data laws
• Alignment with common governance needs in areas like healthcare, consumer privacy, and information security
• Customizable encryption, logging, retention, and access control options

Cloud-only tools may comply with general standards, but often restrict custom configurations. For organizations in regulated sectors or those building proprietary AI operations, that lack of flexibility can become a long-term risk.

Customization Beyond the Surface

AI should feel like part of your team, not a disconnected app with a pretty interface.

Dot enables:

• Visual, no-code workflow creation so business teams can build independently
• Multi-agent orchestration, allowing agents to take on different roles across workflows
• Developer access for custom logic, internal tool integration, or advanced model usage
• Use of internal data, documents, APIs, and decision layers

Cloud-only tools may allow prompt customization, but they rarely offer the ability to build autonomous, multi-step workflows that reflect your internal processes.

What About Open-Source Models?

The open-source model space is growing fast. Tools like LLaMA, Mistral, DeepSeek and Falcon provide competitive capabilities, especially for companies looking to avoid vendor lock-in.

But here’s the catch: most cloud-only tools do not support open models.

Dot does.

You can run Mistral and other open models inside Dot, fully integrated with your workflows, agents, and infrastructure preferences. This means you get the flexibility of open-source with the structure and compliance of an enterprise-grade platform.

So, what exactly is an open-source model?

Open-source AI models are publicly released by developers or research labs with permission to inspect, modify, and build upon the model architecture and weights. Unlike proprietary models (like GPT-4 or Claude) that are locked inside private infrastructure, open-source models allow you to:

• Host the model locally or in your own cloud
• Fine-tune or extend it for your own use cases
• Audit the code and training data (where available)
• Integrate into private systems without external dependencies

This makes them a powerful foundation for organizations that want to control how their AI evolves. However, open-source models require infrastructure, engineering resources, and orchestration support, all of which Dot provides.

This hybrid approach ensures that AI development is not restricted by model availability or deployment structure. You get the freedom of open tools, backed by the reliability of a business-ready platform.

Conclusion: AI Tools Are Everywhere. Control Isn’t.

Cloud-only AI tools have pushed generative AI into the mainstream. They are fast, accessible, and ideal for simple tasks or personal productivity. But they are not enough when AI becomes a critical part of how your company works.

Dot was built for those moments when AI stops being a test and becomes a requirement. It gives you full control over infrastructure, compliance, and workflow design.

Whether you’re building internal copilots, AI agents for finance, automated support flows, or custom AI integrations across teams, Dot helps you do it securely, flexibly, and at scale.

Get in touch with us to discuss how Dot can support your enterprise AI needs with on-premise or hybrid deployment.

Frequently Asked Questions

What is the difference between Dot and cloud-only AI tools?
Cloud-only AI tools run entirely on vendor infrastructure, while Dot offers full control with cloud, hybrid, or on-premise deployment.

Why does hosting flexibility matter for AI tools?
It impacts compliance, data privacy, and scalability — especially for regulated industries or companies with internal infrastructure.

Can Dot run open-source models like Mistral or DeepSeek?
Yes, Dot supports open-source models and allows them to be used in secure, customized enterprise environments.

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