Zedly AI
Enterprise AI
Every week brings a new headline about confidential data escaping through a public AI tool. Legal teams scramble, CISOs lose sleep, and employees hesitate to paste anything sensitive into ChatGPT or Claude. By mid-2024 most enterprises decided the convenience of public LLMs no longer outweighed the risk.
That's when "Private GPT" stopped being a science experiment and became a checkpoint on every enterprise roadmap.
Forget the marketing noise. A true Private GPT runs entirely under your control on-prem, in your private cloud, or inside an air-gapped rack. Prompts never leave your network, models never phone home, and compliance teams finally get the audit trail they need.
A mid-sized company can burn $18k-$30k per month on ChatGPT Enterprise or Claude Teams API calls. That same annual budget covers two H100 GPUs, and those servers keep serving responses forever.
Factor in higher cyber insurance premiums, privacy impact assessments every quarter, and the lawsuits triggered when client data leaks into a public model. Those 'cheap' API credits quickly become the most expensive line item in AI.
Public tools promise encryption in transit and at rest. Private GPT adds physical control, single tenancy, and the ability to audit every byte. Defense contractors run air-gapped clusters. Banks process SWIFT messages without touching the public internet. Law firms upload million-page discovery sets knowing nothing leaves the building.
Early private deployments were sluggish. That era is over. Modern 70B models quantized to 4-bit already match GPT-4-turbo on many enterprise workloads while running comfortably on a single high-end server. Add RAG and responses stay grounded in your own knowledge base. Production teams now report 3.7 second answers fast enough that employees forget they're not hitting the public cloud.
HIPAA, GDPR, SOC 2, FedRAMP, state privacy acts all share a theme: prove you control the data. Private GPT turns the compliance checkbox into a competitive advantage. Procurement teams now ask, 'Do you rely on public LLMs?' The wrong answer kills deals.
Most companies pilot Private GPT with a single department. Within weeks it spreads company-wide because the economics, security model, and employee satisfaction numbers speak for themselves.
Hardware prices dropped, models improved, and the risk of public tools spiked. 2025 is the year staying on public, shared LLMs simply stopped making business sense.
Your data is your most valuable asset. Keeping it private no longer requires compromise.
Private-by-design document analysis with strict retention controls.