Large Language Models (LLMs) are no longer limited to researchers - they have become practical tools for business. With their help, companies automate customer support, write code, generate content, and analyze data faster than ever before. Yet most decision-makers face a clear question: should they rely on cloud services (OpenAI, Anthropic, Google) or build their own local solutions?
Both options have their strengths and weaknesses. Let’s break down where local models bring more value, and where the cloud remains the better choice.
Advantages:
- Full security: data never leaves your infrastructure.
- Flexibility: models can be fine-tuned for specific business processes.
- Cost efficiency at scale: with large query volumes, local models may be cheaper than cloud subscriptions.
- Minimal latency: no dependency on internet connectivity.
Disadvantages:
- High entry cost: requires GPUs, servers, and an engineering team.
- Responsibility for maintenance: updates and stability must be managed internally.
- Lagging behind leaders: cloud providers update models faster.
Local models are the right choice for companies where data control and customization are more important than ease of setup.
Local LLMs: When Control Matters More Than Convenience
Advantages:
- Access to leading models (GPT, Claude, Gemini).
- Scalability without infrastructure costs.
- Quick setup: integration via API in just a few days.
- Minimal maintenance effort.
Disadvantages:
- Costs scale with usage.
- Risks associated with sending sensitive data to external providers.
- Dependence on vendor stability and policies.
Cloud services are the choice for businesses that value fast deployment and state-of-the-art models right out of the box.
Cloud LLMs: Speed and Access to the Best Models
- When working with confidential data (banks, healthcare, government).
- When you have a consistently high workload - subscriptions may become too expensive.
- When you need a custom AI adapted to unique business needs.
When to Choose Local Models
- When building a pilot or MVP.
- When access to the latest models is critical.
- When you lack infrastructure and internal support resources.
When the Cloud is a Better Option
In recent years, several powerful open-source solutions have emerged that can be deployed locally and adapted to business needs. The most promising include:
- LLaMA 3 (Meta), Gemma 3 (Google), and GPT-oss - mature, actively maintained models well-suited for customization and fine-tuning.
- DeepSeek and Qwen2 - new contenders showing impressive benchmark results and rapidly gaining community adoption.
- Mistral - compact, fast, open-license models optimized for on-premise deployment.
These solutions enable businesses to build flexible local AI systems with full data control and the ability to tailor functionality to specific workflows.
The Best Local LLMs in 2025
Choosing between local and cloud LLMs is a strategic decision that will shape the future of your product. We can be your partner to guide you through every stage - from analysis to full-scale implementation. Get in touch with us, and we’ll help select the right path for your business.
Local LLMs give businesses control and security, while cloud-based models offer flexibility and quick access to cutting-edge technology. The right choice depends on your goals: are you building a long-term AI strategy or simply testing ideas in a pilot format?
In practice, many companies opt for a hybrid approach: some processes are handled by local models, while others rely on cloud APIs. This way, they balance data control with the benefits of advanced technologies.
25/09/2025
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