Custom, Enterprise LLM-as-a-Service
A managed private LLM with zero infrastructure overhead.
Cloud, on-prem, or at the edge.
Same model, same governance, same control plane — sized and operated for the environment that fits your security, latency, and cost profile.
- On-prem for full data sovereignty
- Private cloud (AWS · Azure · GCP) for elastic scale
- Edge for offline + low-latency environments
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.
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.
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:
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
RBAC (Role-Based Access Control)
SSO (Single Sign-On) integration
End-to-end encryption
Firewall & IP whitelisting (optional)
Logging & observability tools
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.
Common questions
01Is my model instance truly private, or is it shared like a public API?
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.
02Can the model be fine-tuned or trained on our internal documents?
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.
03How long does it take to go live?
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.
04Where is the model hosted, and who controls the infrastructure?
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.
05What does pricing include, and are there usage-based fees?
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.
06Which open-source LLM models can you deploy for us?
We support a range of open-weight foundation models including Llama 3.3 70B, Mistral Large, Qwen 2.5, and others suited to specific task types or compliance profiles. Model selection is part of the scoping process—we recommend based on your context window needs, throughput requirements, and licensing constraints. You are not locked into a single model and can request a model swap or parallel evaluation at any time.
07What SLAs come with the managed service?
LLM.co defines uptime, incident response, and inference latency commitments in the service agreement. Our operations team monitors GPU health, model serving, and network paths continuously, with defined escalation procedures for degraded performance or outages. Specific SLA tiers—including response time windows and remediation commitments—are scoped during the contracting phase based on your criticality requirements.
08Does LLM-as-a-Service meet EU AI Act and data sovereignty requirements?
Yes. Deployments can be region-locked to EU infrastructure, ensuring data residency compliance under GDPR and emerging EU AI Act obligations. We support data sovereignty requirements by isolating all inference, storage, and logging within your designated geographic boundary. Our governance layer provides the documentation and audit trails regulators and internal legal teams typically require.
09How is LLMaaS priced compared to per-token public API costs?
Our managed private LLM is priced as a fixed monthly or annual subscription rather than per-token, eliminating cost unpredictability as usage scales. For sustained enterprise workloads, dedicated private inference consistently delivers better unit economics than public API pricing at volume. Full pricing details are shared after an initial scoping call, since infrastructure configuration, model size, and support tier affect the final structure.
10Can your private LLM integrate with our existing enterprise systems and workflows?
Yes. The deployment exposes a standard REST or OpenAI-compatible API endpoint, making integration with existing applications, internal tools, Slack, Microsoft Teams, or custom portals straightforward. We also support agentic workflows, RAG pipeline connections, and automation integrations as part of the managed service. Your team retains control over access policies and integration architecture throughout.
Open-Weight Model Selection: Llama, Mistral, and Beyond
One of the core advantages of a managed private LLM is the freedom to choose the right open-weight foundation model for your workload—without vendor lock-in to a single proprietary provider. LLM.co supports deployment of leading open-source models including Llama 3.3 70B, Mistral Large, Qwen 2.5, and others, allowing your team to select based on context window size, reasoning capability, language support, or licensing requirements.
Our engineering team handles model versioning, quantization tuning, and inference optimization using serving frameworks such as vLLM, so you benefit from production-grade throughput without managing the underlying stack. Need to switch models or run A/B evaluations between architectures? We accommodate that within your existing deployment—no re-contracting, no infrastructure rebuild. See our custom model options for fine-tuned and domain-adapted variants.
Managed Operations, Uptime SLAs, and Ongoing Support
Unlike a self-hosted open-source deployment, LLM-as-a-Service from LLM.co comes with defined service-level agreements covering uptime, incident response, and model performance monitoring. Our operations team manages GPU health, inference latency, autoscaling, patch management, and security updates—so your engineering resources stay focused on application logic rather than infrastructure reliability.
Every deployment includes structured support tiers with escalation paths, proactive alerting, and scheduled model health reviews. Compliance-sensitive environments benefit from immutable audit logging and change-management controls that align with SOC 2 Type II, HIPAA, and EU AI Act requirements. Pair this with our governance and audit capabilities to satisfy enterprise procurement and legal review processes without custom-building observability tooling.
Migrating from Public LLM APIs to a Private Deployment
Organizations moving from public APIs often face the same friction points: unpredictable per-token costs, inability to prevent prompt data from leaving the enterprise perimeter, and limited control over model behavior at the system level. Our migration process begins with a prompt and workflow audit—mapping your existing API calls, system prompts, and integration points to equivalent private-deployment patterns.
LLM.co provides an API-compatible inference gateway, meaning most application code requires minimal changes to route requests to your private model instance instead of a public endpoint. Data privacy controls are applied at the network layer from day one, ensuring no inference traffic transits public API infrastructure during or after cutover. For teams managing hybrid environments with both private and cloud-routed workloads, we support policy-driven routing that keeps sensitive data paths fully isolated.
Private AI On Your Terms
Tell us your use case and constraints — on-prem, cloud, or edge — and we'll map a compliant deployment within one business day.
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