Custom, Enterprise LLM as-a-Service
LLM.co offers LLM-as-a-Service: a fully managed, private large language model deployment tailored to your organization’s data, workflows, and compliance requirements—without requiring your team to manage infrastructure, GPUs, or DevOps.
Unlike public LLM APIs, our solution gives you your own dedicated model instance, isolated in your private cloud or virtual private environment, configured exclusively for your business.
Utilize Your Large Language Model (LLM) of Choice, Depending on Your Enterprise Requirements
What is LLM as-a-Service (LLMaaS)?
LLM-as-a-Service is the next generation of enterprise AI delivery—combining the ease of SaaS with the control, customization, and security of private deployments.
We host and maintain your LLM instance, fine-tuned for your industry, workflows, and documents. You get a secure, scalable model deployment that integrates seamlessly with your systems—without relying on OpenAI, Google, or other public APIs.
Secure by Default
Unlike shared APIs, your model instance is completely separate from other clients. Your data, prompts, and outputs remain 100% private. Unlike shared APIs, your model instance is completely separate from other clients. Your data, prompts, and outputs remain 100% private.
Zero Infrastructure + Scale
No GPUs to manage, no servers to scale. We handle everything from deployment to monitoring—while you focus on results. Need multiple models, multilingual support, or region-specific deployments? We scale with you—no lock-in, no per-token pricing traps.
Fully Customizable
From prompt templates and data connectors to domain-specific language and compliance protocols, your model is tailored—not generic. Each organization has specific and unique needs. We tailor your LLM deployment to meet your unique requirements.
How Our LLMaaS Works
From concept to deployment and beyond, our LLM-as-a-Service offering is designed to deliver high-performing, private AI with zero infrastructure burden on your team. Here’s how we make it happen:
Consultation & Use Case Scoping
We begin with a discovery session to understand your business goals, technical environment, and compliance requirements. Whether you're streamlining legal workflows, enabling internal knowledge search, or automating customer support, we identify high-impact use cases that are best suited for LLM integration.
We also evaluate the nature and volume of your data, determine which data sources need to be ingested, and align on security and access protocols. If needed, we collaborate with your compliance and legal teams to ensure all design decisions meet GDPR, HIPAA, SOC 2, or industry-specific standards.
Deliverables: Solution architecture, use case matrix, compliance checklist, data access plan
Environment Setup
Once your goals are defined, we deploy your LLM in a dedicated private environment—either in your own VPC (e.g., AWS, Azure, GCP) or a LLM.co-managed instance with strict tenant isolation. The environment is provisioned with:
RBAC (Role-Based Access Control)SSO (Single Sign-On) integration
End-to-end encryption
Firewall & IP whitelisting (optional)
Logging & observability tools
Your deployment is fully containerized using Docker or Kubernetes, ensuring easy upgrades, rollback support, and multi-region resilience if required.
Deliverables: Configured hosting environment, credentials, and compliance-ready access controls
Data Ingestion & Fine-Tuning
This is where your model becomes uniquely yours. We ingest your unstructured data—contracts, SOPs, PDFs, chat logs, knowledge base content, internal wikis, emails—and process it through semantic chunking and vector embedding pipelines for optimized retrieval.
Depending on your goals, we can also perform instruction fine-tuning or prompt engineering to adapt the model to your brand voice, internal terminology, regulatory language, or domain-specific tasks.You get a private model instance that understands your context, not just generic internet knowledge.
Deliverables: Embedded vector database, optional fine-tuning outputs, retrieval pipelines, prompt library
Enterprise LLM Features + On-Going Support & Maintenance
LLM-as-a-Service from LLM.co offers a fully managed, private, and scalable large language model deployment tailored to your enterprise needs. Features include a dedicated, single-tenant model instance hosted in your private cloud or VPC; fine-tuning on your proprietary data for domain-specific accuracy; integrated retrieval-augmented generation (RAG) pipelines; and full support for semantic search, document summarization, contract analysis, and multi-turn reasoning.
The platform supports custom UI delivery (web, Slack, Teams, API), role-based access controls, SSO integration, and enterprise-grade encryption and logging. Clients can leverage multi-agent orchestration, custom prompt templates, multilingual support, usage analytics, and optional hybrid cloud routing for large context or logic-heavy tasks. With LLM.co, you get the power of generative AI with the control, compliance, and performance your business demands—delivered as a secure, fully managed service.
Email/Call/Meeting Summarization
LLM.co enables secure, AI-powered summarization and semantic search across emails, calls, and meeting transcripts—delivering actionable insights without exposing sensitive communications to public AI tools. Deployed on-prem or in your VPC, our platform helps teams extract key takeaways, action items, and context across conversations, all with full traceability and compliance.
Security-first AI Agents
LLM.co delivers private, secure AI agents designed to operate entirely within your infrastructure—on-premise or in a VPC—without exposing sensitive data to public APIs. Each agent is domain-tuned, role-restricted, and fully auditable, enabling safe automation of high-trust tasks in finance, healthcare, law, government, and enterprise IT.
Internal Search
LLM.co delivers private, AI-powered internal search across your documents, emails, knowledge bases, and databases—fully deployed on-premise or in your virtual private cloud. With natural language queries, semantic search, and retrieval-augmented answers grounded in your own data, your team can instantly access critical knowledge without compromising security, compliance, or access control.
Multi-document Q&A
LLM.co enables private, AI-powered question answering across thousands of internal documents—delivering grounded, cited responses from your own data sources. Whether you're working with contracts, research, policies, or technical docs, our system gives you accurate, secure answers in seconds, with zero exposure to third-party AI services.
Custom Chatbots
LLM.co enables fully private, domain-specific AI chatbots trained on your internal documents, support data, and brand voice—deployed securely on-premise or in your VPC. Whether for internal teams or customer-facing portals, our chatbots deliver accurate, on-brand responses using retrieval-augmented generation, role-based access, and full control over tone, behavior, and data exposure.
Offline AI Agents
LLM.co’s Offline AI Agents bring the power of secure, domain-tuned language models to fully air-gapped environments—no internet, no cloud, and no data leakage. Designed for defense, healthcare, finance, and other highly regulated sectors, these agents run autonomously on local hardware, enabling intelligent document analysis and task automation entirely within your infrastructure.
Knowledge Base Assistants
LLM.co’s Knowledge Base Assistants turn your internal documentation—wikis, SOPs, PDFs, and more—into secure, AI-powered tools your team can query in real time. Deployed privately and trained on your own data, these assistants provide accurate, contextual answers with full source traceability, helping teams work faster without sacrificing compliance or control.
Contract Review
LLM.co delivers private, AI-powered contract review tools that help legal, procurement, and deal teams analyze, summarize, and compare contracts at scale—entirely within your infrastructure. With clause-level extraction, risk flagging, and retrieval-augmented summaries, our platform accelerates legal workflows without compromising data security, compliance, or precision.
How LLM-as-a-Service Differs from Public LLM APIs
To help you understand the unique value of LLM-as-a-Service from LLM.co, the table below compares our offering with traditional public LLM APIs (like OpenAI, Anthropic, and others). While public APIs are fast and convenient, they often involve shared infrastructure, limited customization, and data exposure risks. In contrast, our solution delivers a dedicated, private model instance that’s trained on your data, hosted in your own environment, and tailored to your specific use cases.
From data retention and compliance to interface flexibility and cost predictability, this side-by-side breakdown illustrates why LLM.co is the better choice for enterprises that need real control, privacy, and performance.
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FAQs
Frequently asked questions for large language models as-a-service
Your model instance is fully private and single-tenant—it is not shared across clients like OpenAI or Anthropic APIs. Each deployment is isolated in your own cloud (AWS, Azure, GCP) or VPC, with no data leakage or cross-tenant access.
Yes. As part of onboarding, we ingest your internal content—contracts, PDFs, knowledge bases, chat transcripts—and either fine-tune the model or use RAG pipelines to make it context-aware. Your model speaks your business language, not just generic internet knowledge.
Most LLM-as-a-Service deployments go live in 2–4 weeks, depending on complexity. Simple pilots can be up and running in under 10 business days, while larger enterprise rollouts with custom workflows or integrations may take longer.
You have options. We can deploy your model in your cloud environment (fully under your control) or in a LLM.co-managed private instance with strict tenant isolation. Either way, you retain ownership of your data, prompts, and usage policies.
LLMaaS is offered at a fixed monthly or annual rate, with no token-based pricing surprises. Your plan includes hosting, maintenance, support, security, and ongoing optimization. Optional services like fine-tuning or multi-agent orchestration can be scoped as add-ons.