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Local LLMs vs Cloud LLMs

Studio Cavan ·

The AI landscape in 2025 offers businesses a genuine choice: run models in the cloud via APIs, or run them on your own hardware. Neither is universally better. The right answer depends on your data, your team, and your workflows.

The Case for Cloud LLMs

Cloud LLMs (OpenAI, Anthropic, Google Gemini) offer:

  • State-of-the-art capabilities. GPT-4o, Claude 3.5, and Gemini Ultra are among the most capable models available. If you need the best reasoning, they lead.
  • Zero infrastructure. No GPUs to buy, no servers to manage, no CUDA drivers. One API key and you’re building.
  • Managed scaling. Handle 10 requests or 10 million — the provider scales for you.
  • Frequent model updates. New capabilities appear automatically without migration work.

Best for: Customer-facing features, high-traffic applications, tasks requiring frontier intelligence, rapid prototyping.

The Case for Local LLMs

Running models locally (via Ollama, vLLM, or bare metal) offers:

  • Complete data privacy. Your prompts, code, and documents never leave your network. Critical for legal, healthcare, and finance.
  • No per-token costs. After hardware investment, inference is free. At scale, this becomes a significant savings.
  • No rate limits. Unlimited parallelism on your own infrastructure.
  • Air-gapped capability. Works in environments with no internet connectivity.
  • Customization. Fine-tune models on your proprietary data.

Best for: Internal tools, sensitive data processing, high-volume batch jobs, regulated industries.

The Numbers

A rough cost comparison for a team running 10M tokens/day:

ApproachMonthly Cost
GPT-4o (cloud)~$150,000+
Claude Haiku (cloud)~$2,500
Local Llama 3 70B (NVIDIA A100 x2)~$2,000 (amortized hardware)

At scale, local wins on cost. At small scale, cloud wins on simplicity.

The Hybrid Strategy

The smartest organizations in 2025 use both:

  1. Local LLMs for internal workflows — code review, document summarization, internal search, developer tooling. Privacy-safe, cost-effective.
  2. Cloud LLMs for customer-facing features — chatbots, content generation, complex reasoning tasks where capability matters most.

This hybrid approach gives you the best of both worlds: privacy and cost control where it matters, frontier intelligence where users need it.

For most teams getting started with local AI:

  • Ollama — easy local model serving
  • Open WebUI — team-facing chat interface
  • LangChain / LlamaIndex — orchestration layer
  • Qdrant or Chroma — vector database for RAG

Studio Cavan specializes in setting up exactly this kind of hybrid infrastructure. Get in touch if your team is ready to move beyond the ChatGPT API.