Run a self-hosted, on premise LLM inside your own infrastructure, with full control over your data, your models, and your costs. KriraAI designs, deploys, and manages private large language models on your servers, in your private cloud, or in fully air-gapped environments. Your prompts, documents, and outputs never pass through a third-party API.
A private LLM is a large language model that runs on infrastructure your organization controls, such as on-premise servers, a dedicated private cloud, or an isolated network. Unlike public AI services, a private LLM processes every prompt and document inside your security perimeter, so no data is shared with an external model provider. This makes private AI the preferred choice for enterprises that handle regulated, confidential, or proprietary information.
Public AI APIs are fast to adopt, but they move sensitive data outside your control and make costs unpredictable. A self-hosted LLM removes both problems while giving your teams the same generative AI capabilities. Here is what changes when the model runs on your terms.
Every prompt, file, and response stays within infrastructure you own or control. There is no external logging, no third-party retention policy, and no risk of your data training someone else's model. Your security team keeps full visibility over where information lives and who can access it.
Regulated industries often cannot send patient records, financial data, or classified material to a shared AI service. A HIPAA compliant LLM or GDPR compliant AI deployment keeps data residency, access control, and audit trails aligned with your obligations. Compliance reviews become simpler because the entire data path is internal and documented.
Public APIs bill per token, so costs grow with every new user and every new use case. A private LLM shifts spending to infrastructure that you size once and use as heavily as you need. For high-volume workloads, this model often becomes more economical over time.
Inference runs close to your applications, free from a provider's outages and rate limits. You choose the model, the serving stack, and the hardware, and you can change any of them later. Your AI roadmap stays in your hands rather than on a vendor's pricing page.
KriraAI delivers end-to-end LLM deployment services, from infrastructure planning to long-term operations. Every engagement is built around your security requirements, data, and priority use cases. Choose a single service or combine them into a complete private AI platform.
Factor
On-Premise
Private Cloud
Hybrid
Data control
Highest, full physical control
High, isolated VPC
High for sensitive workloads
Upfront investment
Higher, hardware purchase
Lower, pay for capacity
Moderate
Scalability
Limited by installed hardware
Elastic
Flexible by workload
Compliance fit
Strictest regulations, air-gapped needs
Most regulated workloads
Mixed data sensitivity
Time to launch
Longer
Faster
Moderate
Best for
Defense, healthcare, banking
Fast-moving enterprises
Large multi-division organizations
There is no single right answer, because the best model depends on your data classification, existing infrastructure, and growth plans. On premise AI makes sense when control outweighs every other factor, while private cloud suits teams that need to move quickly. Our assessment maps each use case to the right environment before any hardware is purchased.
KriraAI is model-agnostic, so we recommend the model that fits your task, hardware, and licensing needs. We benchmark candidate models on your own documents and workflows, not only on public leaderboards. Our standard stack covers the following layers.
A private LLM is only as secure as the controls around it, so security is part of the architecture from day one. We align every deployment with frameworks such as HIPAA, GDPR, SOC 2, and ISO 27001 based on your requirements. Each KriraAI deployment includes these safeguards.
Network isolation with no public inference endpoint
Role-based access control integrated with your identity provider
Encryption of data at rest and in transit
Complete audit logs of prompts, responses, and administrative actions
Guardrails against prompt injection, data leakage, and policy violations
Organizations that handle sensitive data gain the most from on premise AI and private LLM deployment. KriraAI has delivered AI systems across more than 22 industries, and the following sectors show the strongest demand. Each deployment is shaped by the regulations and workflows of that industry.
Running a private LLM in production requires expertise across AI engineering, infrastructure, and security. KriraAI brings all three under one team, so you never coordinate multiple vendors. Here is what sets our approach apart.
Model-agnostic advice
We pick the model that fits your use case, not a reseller deal.
Production focus
We build for real concurrency, uptime, and audit requirements.
Full lifecycle ownership
From GPU sizing to LLMOps, one team is accountable for results.
Knowledge transfer
Your engineers get documentation and training to run the system.
Your AI should work for you without putting your data at risk. KriraAI helps enterprises deploy private LLMs that are secure, compliant, and built for production from the first day. Book a free deployment assessment to map your use cases, your infrastructure, and the fastest path to a running private LLM.
Our enterprise LLM deployment process reduces risk at every stage and moves a working system into production quickly. Each phase ends with a clear deliverable, so your stakeholders always know what is complete and what comes next. A typical engagement follows six steps.
ChatGPT is a public AI service where prompts are processed on the provider's servers under its data policies. A private LLM runs on infrastructure your organization controls, so prompts and documents never leave your environment. You also decide which model is used, how it is updated, and who can access it.
On-premise LLM deployment means installing and running a large language model on servers inside your own data center. The model, the inference stack, and all data stay within your physical and network perimeter. It offers the highest level of control and is common in healthcare, finance, government, and defense.
Yes, a private LLM can run fully offline after deployment, with no internet connection or external API calls. Models and updates are transferred through controlled offline processes. This setup serves classified, defense, and critical infrastructure workloads where any external connectivity is unacceptable.
A self-hosted LLM supports HIPAA and GDPR compliance because data stays within infrastructure you control. Compliance also depends on the controls around the model, including access management, encryption, audit logging, and retention policies. KriraAI designs these controls into every deployment and documents them for your compliance team.
Hardware depends on model size, the number of concurrent users, and your latency targets. Smaller models can run on a single data center GPU, while larger models need multi-GPU servers with high memory bandwidth. We size compute, storage, and networking during the infrastructure assessment, so you buy only what your workloads need.
No single model is best for every enterprise LLM deployment. The Llama, Mistral, Qwen, Gemma, and DeepSeek families each perform differently depending on task, language, and hardware budget. We benchmark shortlisted models on your own data and select the one with the best accuracy for your cost and latency targets.
Yes, we use retrieval-augmented generation (RAG) to connect your private LLM to document repositories, databases, and knowledge bases. The model retrieves relevant content at query time and answers with source citations, while your documents stay inside your environment. Access permissions from your existing systems can be enforced in the retrieval layer.
Choose on-premise if you need maximum control, have strict data residency rules, or already operate a data center. Choose a private cloud LLM if you want a faster launch, elastic scaling, and lower upfront investment. Many enterprises adopt a hybrid model, keeping the most sensitive workloads on-premise and running the rest in private cloud.
Timelines depend on the deployment model, hardware availability, and integration scope. A private cloud deployment with a single use case moves faster than an air-gapped on-premise build with multiple integrations. We provide a phase-by-phase timeline after the discovery and infrastructure assessment.
Yes, KriraAI offers private LLM hosting and managed operations covering monitoring, security patching, scaling, and model upgrades. You can choose full management or a shared model where your team handles daily operations with our engineers on call. In both cases, your data stays inside your controlled environment.
