Private Deployment
We deploy LLMs in a private cloud or on the client’s infrastructure to maintain control over the environment, access, and confidential data.
LLM Deployment Services
Start your LLM deployment in just 5 days – with scalable infrastructure and controlled costs.
80
Certified Engineers
12+
Years of Experience
15
Countries with Clients
Custom LLM Deployment Services
ZentixSoft deploys all components of an LLM solution – from models, RAG pipelines, vector databases, and AI agents to backend services, cloud infrastructure, and DevOps processes. One team is responsible for ensuring that all components work as a single production system, and you do not need to coordinate multiple specialized contractors for the model, infrastructure, and integrations. We prepare each LLM model for real workloads and monitor their operation after launch. Monitoring response quality, latency, errors, and costs helps identify problems in time and maintain predictable system operation. For us, deployment covers not only model deployment but also ongoing monitoring, cost control, and system stability.
Start from 5 days
We bring in a ready-made team without lengthy hiring to start preparing and deploying the solution faster.
Ready for load
We design the architecture for simultaneous requests and peak loads and test it using load testing.
Costs under control
We track request volume, token usage, and infrastructure resources to identify overspending and reduce the cost of operating the system.
When Businesses Need
LLM Deployment Services
Key indicators that your product needs to transition to a stable production infrastructure.
Challenge
The company has an LLM model, RAG prototype, or individual AI components that need to be prepared for launch in the product.
Risk
The launch is delayed, investments do not deliver results, and the solution remains at the demonstration stage.
Solution
We design the complete path to production – from model and infrastructure selection to integration, testing, and launch.
Challenge
The LLM works during testing but processes the actual number of requests slowly or unstably.
Risk
Users experience delays and failures, and the AI feature cannot be scaled.
Solution
We build and test the architecture for simultaneous requests and peak loads and, if necessary, configure automatic scaling.
Challenge
API, token, GPU, and cloud resource costs are difficult to predict.
Risk
After launch, the cost of operating grows faster than the value it delivers.
Solution
We set up cost tracking and apply optimization strategies for model selection, token usage, and infrastructure resources.
Challenge
The LLM needs to be connected to the product, corporate data, APIs, and business processes.
Risk
The system does not receive the necessary context, and AI agents cannot correctly perform actions through external APIs and tools.
Solution
We integrate the model with the backend, APIs, and data sources and, if necessary, add RAG, a vector database, and AI agents.
Challenge
The LLM must process confidential or regulated data.
Risk
An inappropriate deployment method creates a risk of information leakage and violation of internal or regulatory requirements.
Solution
We select the appropriate model – a managed cloud service, dedicated cloud infrastructure, or an on-premises environment – and implement encryption, access control, secret management, and action logging.
Challenge
After launch, the team cannot see what is happening with response quality, speed, errors, and costs.
Risk
Problems are detected only after user complaints, and updates can unpredictably degrade LLM performance.
Solution
We implement monitoring, testing, versioning, and controlled deployment of changes.
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Our LLM Model
Deployment Services
We deploy LLM solutions in production and take care of everything needed for their stable operation: infrastructure, integrations, optimization, and monitoring.
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LLM Deployment Assessment & Planning
We assess the readiness of the LLM solution for production and define LLM deployment strategies based on load, data, and budget:
analyze the model, architecture, and existing AI components;
define performance and availability requirements;
choose cloud, private cloud, or self-hosted deployment;
create a launch, scaling, and recovery plan.
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Cloud LLM Deployment
03
Private & Self-Hosted LLM Deployment
04
RAG & AI Agent Productionization
05
LLM Inference Optimization
06
LLMOps & Production Monitoring
Want to Bring Your LLM Product to Production Quickly?
Tell us about your task – we’ll assess the current readiness of your LLM solution and propose a deployment approach taking into account infrastructure, load, and budget.
Security and Compliance
in LLM Deployment
We protect confidential data, control access, and take applicable compliance requirements into account to reduce risks when launching AI in production.
Data Encryption
We encrypt data in transit and at rest to protect it from unauthorized access.
Access Control
We separate access rights to models, data, APIs, and environments so that users can work only with authorized resources.
Data Isolation
We isolate data across clients, teams, environments, and knowledge bases according to the solution architecture to prevent data mixing or accidental disclosure.
Audit Logging
We log access, changes, and system operations without exposing confidential data to monitor system activity and investigate incidents.
Compliance Alignment
We take into account applicable GDPR and HIPAA requirements, as well as SOC 2 and ISO/IEC 27001 controls, to simplify internal reviews and audit preparation.
Deep Expertise in LLM Deployment
We combine the necessary models, data, integrations, and infrastructure into an LLM solution ready to operate in production.
Multi-Model Systems
We combine multiple LLMs in one system and route requests to the appropriate model based on quality, speed, and cost.
Production RAG Systems
We deploy RAG with corporate data and vector databases so that the LLM generates relevant responses based on up-to-date internal information.
AI Agent Infrastructure
We create infrastructure for AI agents and provide them with controlled access to APIs and business systems.
High-Load LLM Applications
We prepare LLM solutions to handle a large number of simultaneous requests while maintaining stability and speed as the audience grows.
Private LLM Environments
We deploy models in a private cloud or on the client’s infrastructure so that the company maintains control over confidential data.
Enterprise LLM Integrations
We connect the LLM to the backend, databases, CRM, ERP, and internal systems so that it becomes a full-fledged part of workflows
Advantages
Why Companies Choose ZentixSoft
We combine engineering with a responsible approach to LLM deployment to reduce risks and maintain stable and controlled operation of the solution in production.
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Production Ownership
We take responsibility for the launch and stable operation of the LLM solution in production, so you do not have to resolve issues between the model, backend, and infrastructure yourself.
Provider-Neutral Approach
We select the LLM and environment based on your quality, data, and budget requirements, so you are less dependent on the capabilities and pricing of a single provider.
Scale Without Hiring
We expand the team by five or more specialists within 1–2 weeks, so you can accelerate deployment without lengthy hiring and additional workload for HR.
Delivery Visibility
We work through transparent sprints, agreed results, and quality control, so you can see progress, costs, and risks at every stage.
Long-Term Continuity
On average, our partnerships last 30 months, so you can develop your LLM solution with a team that already knows its architecture and your business context.
Path to a Production-Ready LLM
A transparent six-step process – from readiness assessment to launch and monitoring in production.
01
Requirements & Readiness Assessment
We define business scenarios, data, load, speed, and budget requirements and assess the readiness of existing LLM components for launch.
02
Architecture & Model Selection
We select the model and LLM deployment methods and design the architecture with performance, security, and future costs in mind.
03
Infrastructure Setup
We configure development, staging, and production environments, computing resources, access, CI/CD, and backup scenarios.
04
Integration & Deployment
We connect the LLM to the backend, databases, APIs, and other components and deploy the entire system in the production environment.
05
Testing & Optimization
We test response quality, security, latency, and stability under load and optimize token usage and infrastructure resources.
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Launch & Monitoring
We launch the LLM solution, configure monitoring of quality, errors, and costs, and monitor system operation after release.
Our team
The Team Behind Your LLM Solution
We combine technical expertise in AI, backend, and cloud infrastructure to ensure that every component of the solution is designed and launched in a coordinated manner.
CEOAgness
CTOAndrew
HRDYevhenii
Project ManagerOlha
Full Stack DeveloperDmytro
Full Stack DeveloperMarko
Team LeadYehor
Content LeadMaksim
Project ManagerYuliia
Project ManagerOlga
BDMPavlo
Full Stack DeveloperYurii
Full Stack DeveloperVitalii
Team Lead | DevOpsOleg
Backend DeveloperMaks
Team Lead | DevOpsOleg
QA EngineerElina
Full Stack DeveloperOleksandr
RecruiterOksana
Project ManagerYeva
Full Stack DeveloperAnastasiia
Head of DeliveryKristina
Tender ManagerYurii
Business AnalystOlena
UI/UX DesignerVladyslav
Technology Stack
for LLM Deployment
A proven stack for secure, scalable, and high-load LLM solutions – from models and RAG to cloud infrastructure and monitoring.
LLM Models & APIs
OpenAI
Anthropic Claude
Google Gemini
Llama
Model Serving
vLLM
Hugging Face TGI
RAG & Orchestration
LangChain
LlamaIndex
Backend & Data
Python
FastAPI
Node.js
PostgreSQL
Redis
Cloud & Infrastructure
AWS
Docker
Kubernetes
CI/CD
GitHub Actions
GitLab CI/CD
Monitoring & Observability
OpenTelemetry
Prometheus
Grafana
Sentry
Companies Growing with Us
Client Impressions
After Working with Us
A partnership that delivers results – in the words of those who have already gone through the journey with us.
They adapted quickly to changing priorities.
The team quickly understood our goals and translated them into practical solutions. Their ability to adapt and move fast made the collaboration smooth and highly productive.
Joseph F.
Ozeaon | Portugal
Joseph F.
Ozeaon | Portugal
Worked in sprints
Zentix delivered their development work on time, which was an excellent start for the client. The team worked in sprints, updated the client weekly, and delivered tasks on schedule.
Andrew R.
RaDevs | Estonia
Andrew R.
RaDevs | Estonia
Responsiveness and dedication
Responsiveness and dedication to delivering high-quality services were outstanding.
Maria А.
ARGUNOVA | Ukraine
Maria А.
ARGUNOVA | Ukraine
The team delivered exactly what we needed
The team delivered exactly what we needed — high-quality solutions and smooth collaboration from start to finish.
Alex L.
Wavory | Cyprus
Alex L.
Wavory | Cyprus
Their e-commerce expertise made a real difference
Zentix helped us build and launch our e-commerce platform with great attention to detail. The team was responsive, professional, and easy to work with throughout the entire process.
Amir B.
Servicom | Sweden
Amir B.
Servicom | Sweden
They delivered great results and provided useful support throughout the project."
They delivered great results and provided useful support throughout the project.
Andrey H.
Bestclevers | UK
Andrey H.
Bestclevers | UK
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Not Sure Where to Start with LLM Deployment?
We’ll help you plan the deployment with architecture, load, and future scaling in mind.
