GPU utilization
52%
Serving cluster
3 active nodes
3 active nodes
Private, On-Premise AI Infrastructure
Intelligence, Internalized.
Own Your AI.
Own Your Data.
Eliminate API Costs.
We design, deploy, and fine-tune self-hosted open-source AI systems that run entirely inside your infrastructure, replacing recurring OpenAI and Anthropic API bills with a one-time investment you fully own.
No per-token fees
Data never leaves your network
Full infrastructure ownership
$0/token
Ongoing per-token fees once deployed
0%
Of inference data stays inside your network
0 mo
Typical break-even vs. cloud API spend
24/7
Infrastructure you control, not a provider
The problem with API-based AI
Cloud AI APIs get more expensive the more your business succeeds
OpenAI and Anthropic price by the token. As usage grows, more employees, more documents, and more customers increase the bill indefinitely, with no ceiling and no ownership at the end.
01 - COST
Recurring costs that scale against you
Every request, every document processed, and every internal assistant query adds to a monthly invoice that only grows as adoption succeeds, turning your AI strategy's success into your biggest new line item.
02 - LOCK-IN
Vendor lock-in on mission-critical workflows
Once internal tools, copilots, and customer support are built around a provider's API, migrating away means rebuilding integrations, prompts, and workflows from scratch at a time and price the vendor controls.
03 - PRIVACY
Sensitive data leaving your perimeter
Contracts, customer records, financial data, and internal knowledge are transmitted to third-party servers for every single query, expanding your data exposure and audit surface with every integration.
04 - COMPLIANCE
Compliance risk you can't fully control
Regulated industries need to demonstrate exactly where data lives and who can access it. Third-party API processing complicates GDPR, HIPAA, and internal governance requirements that on-premise systems sidestep entirely.
The Intrateal AI approach
Move the model inside your walls, not your data outside them
1
Your data stays putDocuments, chat logs, and records never leave your infrastructure
2
Open-source model, fine-tunedAn LLM trained specifically on your domain and terminology
3
Served from your own GPUsOn-premise or private cloud, under your access control
4
You own the outcomeNo monthly bill, no provider dependency, no data export
Instead of renting intelligence by the token, you own the system that produces it. We handle the infrastructure, the fine-tuning, and the maintenance, and you keep the assistants, the knowledge base, and every query, forever.
- One-time implementation fee, not a recurring subscription
- Built on proven open-source model families, not proprietary black boxes
- Runs entirely within infrastructure you control
- Scales with hardware you own, not usage tiers you're billed for
See it running
This isn't a mockup, it's what your team would actually use
Below is a simulated preview of a deployed system: an internal chat interface on the left, and the backend telemetry your IT team would see for every single request on the right.
Requests handled today
48,210
Cloud API cost avoided today
$1,842
Knowledge base: 12,480 docs indexed · Uptime 41d 6h
IA
Internal Assistant
Running on your infrastructure
Backend trace
LiveModel routed—
Latency—
Tokens in / out—
Retrieved sources—
Cost of this query$0.0000
$0.00
Estimated cost avoided this session vs. an equivalent cloud API
This is a simulated preview environment with sample data, built to show the structure of a real deployment (model routing, retrieval, and per-query cost). It is illustrative and not connected to a live customer system. In a live deployment, responses are generated by your fine-tuned model against your own indexed documents.
Internal AI Agent
Task execution simulation
Preparing workflow
Router → Small model
4 tasks
Read incoming requestParse email, ticket, or internal form into a structured instruction.
done
Classify task typeDecide whether the agent should draft, summarize, search, or escalate.
running
Run retrievalPull the relevant company documents and knowledge sources.
queued
Generate outputDraft the answer, meeting summary, or action list for approval.
queued
What we build
Production-grade AI infrastructure, end to end
Every deployment is engineered to enterprise standards, not a demo, and built to run in production for years.
GPU server provisioning
On-premise racks or private cloud GPU instances sized correctly for your model and throughput requirements.
Linux environment hardening
Purpose-built Linux hosts configured and locked down for stable, secure long-term model serving.
Docker & orchestration
Containerized services orchestrated for reliable deployment, scaling, and rollback of every component.
Model serving (vLLM, Ollama, TGI)
High-throughput inference serving using proven open-source engines tuned to your hardware.
Monitoring & logging
Full observability into latency, throughput, errors, and resource usage across the stack.
Security hardening
Network segmentation, encrypted storage, and hardened endpoints reduce the attack surface of every service.
Backup systems
Automated, versioned backups of models, data, and configuration so nothing is ever a single point of failure.
Authentication & access control
Role-based access so only the right people and systems can query or administer your AI infrastructure.
API gateways
Internal, OpenAI-compatible API endpoints so your existing tools integrate with minimal code changes.
Internal chat interfaces
Branded, access-controlled chat applications employees use daily, hosted entirely on your infrastructure.
CI/CD for model updates
Automated pipelines to test and roll out model and fine-tune updates without downtime.
Vector databases
Retrieval infrastructure that lets your assistants search and cite your own documents accurately.
Document processing pipelines
Automated ingestion, chunking, and indexing of PDFs, spreadsheets, and internal documentation.
Knowledge bases
Structured, searchable internal knowledge that keeps your assistants accurate and up to date.
High availability & failover
Redundant serving nodes and automatic failover to keep AI systems online during hardware issues.
Private model fine-tuning
A model that actually knows your business
Generic models don't know your products, policies, or terminology. We fine-tune open-source LLMs directly on your company's own documents and knowledge without any of that data ever leaving your organization.
STEP 1
Knowledge audit
We map the documents, tickets, and workflows your assistant needs to understand.
STEP 2
Data preparation
Company data is cleaned, structured, and prepared entirely within your own environment.
STEP 3
Fine-tuning
An open-source base model is fine-tuned on your data to build a domain-specific assistant.
STEP 4
RAG integration
Retrieval systems connect the model to your live knowledge base for accurate, current answers.
STEP 5
Evaluation & rollout
The assistant is tested against real use cases, then rolled out to employees or customer support.
Security & data ownership
Your data never becomes someone else's training set
Private infrastructure isn't just cheaper, it fundamentally changes your risk profile.
Data never leaves your network
Every prompt, document, and response is processed and stored inside your own infrastructure.
No third-party API providers
Requests never transit an external AI vendor's servers or logging systems.
No external model training on your data
Your proprietary information is never used to improve a vendor's commercial models.
Full infrastructure ownership
You own the servers, the models, and the fine-tuning, not a subscription to someone else's system.
GDPR-friendly architecture
Data residency and processing location are fully under your control by design.
Reduced cybersecurity exposure
Fewer third-party integrations and data transfers mean a smaller external attack surface.
Cost comparison
The math changes fast once you own the system
Cloud APIs charge per token forever. On-premise AI requires a larger upfront investment, then costs stay flat while your usage grows.
Example company: 200 employees
Volume: ~1M requests / month
Workload: document processing, internal copilots, customer support automation
OpenAI API (cumulative)
Anthropic API (cumulative)
In-house open-source AI (cumulative)
| Solution | Monthly cost | Year 1 cost | 3-year total |
|---|---|---|---|
| OpenAI API | ≈ $18,000 | ≈ $216,000 | ≈ $648,000 |
| Anthropic API | ≈ $21,000 | ≈ $252,000 | ≈ $756,000 |
| In-house open-source AI | ≈ $1,200 (example post-launch maintenance, typical range $50 to $5,000+) | ≈ $59,400 (incl. one-time build) | ≈ $88,200 |
Estimated monthly in-house AI operating cost
These figures help clients understand approximate ongoing cost after deployment. They are indicative operating ranges, not fixed promises. In lighter setups, post-launch maintenance can be as low as $50 per month, while more demanding deployments can run to $5,000+ per month.
Typical for lighter internal usage, smaller model routing, and lower concurrency requirements.
Typical for multi-user department copilots, RAG workflows, and steady daily usage across teams.
Typical for larger GPU fleets, higher availability requirements, and heavier concurrent demand.
Actual monthly cost depends on model mix, number of active users, concurrency, hardware choice, uptime requirements, and model routing strategy. In most business environments, not every task needs the same model size, which is why using smaller models for routine tasks and larger models only where needed can reduce operating cost significantly. Post-launch support can range from very light maintenance at around $50 per month to heavier enterprise support above $5,000 per month.
$0Estimated 3-year savings vs. OpenAI API
0 monthsApproximate break-even point
0%Lower average cost by year 3
Figures are illustrative estimates for a representative 200-employee organization at the stated volume, based on published cloud API pricing at time of writing and typical GPU infrastructure and engineering costs for a comparable on-premise deployment. Actual costs vary by request size, model choice, and infrastructure requirements. We do not claim identical performance between open-source and proprietary frontier models. We design fine-tuned open-source systems to handle the specific business workload well, which for most document processing, internal copilot, and support automation use cases is sufficient without frontier-model pricing.
Ongoing maintenance
Optional support once the system is yours
You own the infrastructure outright. If you'd like us to keep it running at its best, we offer maintenance packages that are entirely optional and never required.
Updates
Model updates
Periodic upgrades to newer open-source model versions as they improve.
Security
Security patching
Ongoing patching of the OS, containers, and serving stack.
Performance
Performance tuning
Continuous optimization of latency and throughput as usage grows.
Monitoring
Infrastructure monitoring
24/7 monitoring with alerting for hardware or service issues.
Hardware
Hardware support
Guidance and support for GPU capacity planning and upgrades.
Fine-tuning
Fine-tuning improvements
Iterative retraining as your business and documents evolve.
Knowledge
Knowledge base updates
Keeping retrieval systems current with your latest documentation.
Training
Employee onboarding
Training sessions to help teams get the most from internal assistants.
Customer benefits
What ownership actually gets you
$
Predictable long-term cost
A flat maintenance fee instead of usage-based billing that scales against you.
◇
Full ownership
The infrastructure, models, and fine-tuning are yours, not a rented subscription.
⛨
Data privacy by default
Sensitive information never has to leave your organization to be useful.
↗
Scales with your hardware
Add GPU capacity on your terms, without renegotiating a vendor contract.
Pricing packages
One-time AI infrastructure packages designed to replace recurring API spend
This section is built to be decision-ready for businesses that need a clear path from variable API cost to controlled in-house infrastructure economics.
Starter AI Replacement Package
€5,000 to €9,000
One-time implementation for small businesses paying AI API bills.
- Basic open-source LLM deployment
- Simple RAG system
- Internal AI chat interface
- Docker-based setup
- Lightweight API replacement layer
Designed to immediately reduce AI API spending.
Business AI Cost Replacement Package
€9,000 to €19,000
Core one-time product for businesses with scaling AI usage.
- Full AI infrastructure deployment
- Advanced RAG pipeline
- Fine-tuned models for business data
- Multi-user internal AI system
- Vector database optimization
- Performance tuning for cost efficiency
- API replacement architecture
This system is designed to be significantly cheaper than OpenAI and Anthropic API usage within 1 to 3 months.
Enterprise AI Infrastructure System
€20,000 to €49,000
One-time implementation for heavy AI usage companies.
- Multi-model AI architecture
- High availability deployment
- Advanced fine-tuning pipelines
- Scalable GPU orchestration
- Enterprise security layer
- System integrations for CRM, ERP, and internal tools
- Full automation workflows
Trust section
Built to protect data ownership, compliance posture, and enterprise security
Data never leaves company infrastructure
All prompts, model processing, and retrieval pipelines run inside your private environment.
No third-party API dependency
Core workflows do not rely on external AI API vendors for inference or retrieval.
Full ownership of AI systems
Your organization owns the infrastructure, deployment logic, and model lifecycle.
GDPR compliant architecture
Data residency and access patterns are designed for strict governance requirements.
Enterprise-grade security
Security hardening, RBAC, monitoring, and audit-ready controls are included in deployment design.
Reduced cybersecurity risk
Lower external data transfer and fewer third-party AI dependencies reduce attack surface.
Leadership and team
FivaroIT team: AI engineers and PhD-level AI researchers
The platform is built and maintained by FivaroIT with deep technical expertise in enterprise AI systems.
FivaroIT Team
Private AI infrastructure specialists
Our team combines highly skilled AI engineers and PhD-level AI researchers focused on delivering production-grade enterprise AI infrastructure and measurable cost outcomes. Learn more at fivaroit.com.
Core expertise
- LLM systems architecture and serving stack design
- Distributed GPU computing for scalable enterprise workloads
- Enterprise AI deployment with security and compliance controls
Conversion options
Book a strategy call or request a quote
Choose your preferred conversion path. Both forms include anti-spam protection and route directly to our team.
Schedule a Call
Request a strategy session with your preferred date, time, and time zone. Our team will contact you to confirm the meeting.
Request a Quote
Secure inquiry form with anti-spam protection. Our team will contact you after review.
Frequently asked questions
Common questions from enterprise buyers
Frontier proprietary models generally lead on the broadest, most general-purpose tasks. However, for well-defined business workloads such as document processing, internal support, and domain-specific copilots, a fine-tuned open-source model can perform very effectively because it's specialized on your exact data rather than trying to do everything. We evaluate your specific use case before recommending this approach.
It depends on scale, model size, and infrastructure requirements, but most mid-sized deployments fall in the low-to-mid six figures, comparable to one to two years of equivalent cloud API spend at meaningful volume, after which the system is fully yours with no further licensing.
No. We deploy either on your existing on-premise hardware or within a private cloud GPU environment that you control, depending on your infrastructure preferences and compliance requirements.
Because processing and storage happen entirely within infrastructure you control, you determine data residency, retention, and access rather than relying on a third-party processor's terms and infrastructure location.
The system is fully yours after deployment. Maintenance is optional, your internal IT team can operate it independently, or you can bring us back in only when needed.
Most deployments take between six and twelve weeks from infrastructure provisioning through fine-tuning and rollout, depending on data readiness and integration complexity.
Ready to stop renting your AI?
Talk to our infrastructure team about what a private, owned AI system would look like for your business and what it would save you.