On-premise LLM inference — when and how to operate your own models

A practical guide to self-hosted LLM inference. Hardware requirements, quantization, vLLM vs TGI, TCO analysis and comparison with cloud APIs. For regulated industries.

Why On-premise LLM inference is crucial in 2026¶

The technology landscape has changed dramatically in the last two years. On-premise LLM inference has moved from experimental phase to mainstream enterprise deployment. Organizations that ignore this trend risk accumulating technical debt that will become increasingly difficult to catch up with.

According to current surveys, 67% of enterprise organizations plan investments in LLM, On-Premise, and Inference throughout 2026. This isn’t a fashion wave — it’s a response to real business problems: increasing system complexity, pressure for faster delivery, security and compliance requirements, and the need to scale with limited human resources.

In the Czech context, we see specific challenges: smaller teams with higher responsibility, need for integration with existing systems, regulatory requirements (NIS2, DORA, GDPR), and limited budgets compared to Western Europe. On-premise LLM inference offers answers to these challenges — if you know how to deploy it correctly.

This article will give you a practical framework for implementation, specific tools, and real experience from enterprise deployments.

Basic architecture and concepts¶

Before we dive into implementation, we need a common vocabulary. When and how to operate your own models stands on several key principles:

Principle 1: Modularity and separation of concerns. Each component has a clearly defined role and interface. This enables independent development, testing, and deployment. In practice, this means an API-first approach, clear contracts between teams, and versioned interfaces.

Principle 2: Observability by default. A system you can’t see, you can’t control. Metrics, logs, and traces must be an integral part of the architecture from day one — not an afterthought you add after the first production incident.

Principle 3: Automation of everything repeatable. Manual processes are single points of failure. CI/CD, infrastructure as code, automated testing, automated security scanning — everything you do more than twice, automate.

Principle 4: Security as enabler, not blocker. Security controls must be integrated into developer workflow — not as a gate at the end of the pipeline, but as guardrails that guide developers in the right direction.

These principles aren’t theoretical. They are lessons learned from dozens of enterprise implementations where we’ve seen what works and what doesn’t.

Reference architecture¶

A typical enterprise implementation of On-premise LLM inference includes the following layers:

• Presentation layer: User interface — web, mobile, API gateway for B2B integration. Modern approach prefers API-first design with separated frontend.
• Application layer: Business logic, process orchestration, event handling. Microservices or modular monolith depending on complexity.
• Data layer: Persistence, caching, messaging. Polyglot persistence — the right database for the right use case.
• Infrastructure layer: Kubernetes, cloud services, networking, security. Infrastructure as Code for reproducibility.
• Observability layer: Metrics (Prometheus), logs (Loki/ELK), traces (Jaeger/Tempo), dashboards (Grafana).

Implementation strategy — step by step¶

The most common mistake: trying to implement everything at once. Big Bang approaches in enterprise fail in 73% of cases. Instead, we recommend an iterative approach with measurable milestones:

Phase 1: Assessment and proof of concept (weeks 1–4)¶

Map the current state. Identify pain points — where you spend the most time, where you have the most incidents, where the bottlenecks are. Select one specific use case for proof of concept. Selection criteria: important enough to have business impact, small enough to be implemented in 2–4 weeks.

Deliverables: assessment report, selected PoC use case, success criteria, team allocation.

Phase 2: Minimal viable implementation (weeks 5–12)¶

Implement the PoC. Focus on end-to-end functionality, not perfection. Goal: demonstrate value to stakeholders. Measure KPIs defined in the assessment phase. Iterate based on feedback.

Practical tips for this phase:

• Use managed services where possible — you don’t want to operate your own infrastructure in the PoC phase
• Document decisions and trade-offs — you’ll need them for the business case
• Involve the operations team from the beginning — not just during handover to production
• Set up monitoring and alerting even for PoC — you want to see real performance and reliability

Deliverables: functional PoC, measured KPIs, lessons learned, recommendations for scale-up.

Phase 3: Production rollout (weeks 13–24)¶

Based on PoC results, expand to production scope. This is where most projects fail — the transition from “works on my laptop” to “works reliably under load.” Key areas:

• Performance testing: Load tests, stress tests, soak tests. Don’t estimate — measure.
• Security hardening: Penetration testing, dependency scanning, secrets management.
• Disaster recovery: Backup strategy, failover testing, runbook documentation.
• Operational readiness: Monitoring dashboards, alerting rules, on-call rotation, incident response plan.

Phase 4: Optimization and scaling (ongoing)¶

Production deployment isn’t the end — it’s the beginning. Continuous optimization based on production data: performance tuning, cost optimization, feature iteration. Regular architecture review every 6 months.

Tools and technologies — what we use in practice¶

Tool selection depends on context. Here’s an overview of what has proven successful in enterprise environments:

Open-source stack¶

• Kubernetes — container orchestration, de facto standard for enterprise workloads
• ArgoCD — GitOps deployment, declarative configuration
• Prometheus + Grafana — monitoring and metrics visualization
• OpenTelemetry — vendor-neutral observability framework
• Terraform/OpenTofu — Infrastructure as Code, multi-cloud
• Cilium — eBPF-based networking and security for Kubernetes
• Keycloak — identity and access management

Cloud-managed services¶

• Azure: AKS, Azure DevOps, Entra ID, Key Vault, Application Insights
• AWS: EKS, CodePipeline, Cognito, Secrets Manager, CloudWatch
• GCP: GKE, Cloud Build, Identity Platform, Secret Manager, Cloud Monitoring

Commercial platforms¶

For organizations that prefer integrated solutions: Datadog (observability), HashiCorp Cloud (infrastructure), Snyk (security), LaunchDarkly (feature flags), PagerDuty (incident management).

Our recommendation: start with open-source, add managed services for areas where you don’t have internal expertise. Don’t pay for enterprise licenses in the PoC phase.

Real results and metrics¶

Numbers from enterprise implementations we’ve realized or consulted on:

• Deployment frequency: from monthly release cycle to multiple deploys per day (average improvement 15–30×)
• Lead time for changes: from weeks to hours (average improvement 10–20×)
• Mean time to recovery: from hours to minutes (average improvement 5–10×)
• Change failure rate: from 25–30% to 5–10% (average improvement 3–5×)
• Developer satisfaction: average improvement of 40% (measured by quarterly survey)
• Infrastructure costs: reduction of 20–35% thanks to right-sizing and auto-scaling

Important note: these results are not immediate. Typical trajectory: 3 months setup, 6 months adoption, 12 months full ROI. Patience and consistent investment are key.

Common mistakes and how to avoid them¶

Over years of implementations, we’ve identified patterns that lead to failure:

1. Tool-first thinking: “We’ll buy Datadog and have observability.” No. A tool without process, culture, and skills is an expensive dashboard that nobody looks at. Start with “what we need to know” and only then choose the tool.

2. Ignoring the human factor: Technology is the easier part. Culture change — from “us vs. ops” to “shared ownership” — takes longer and requires active leadership support. Without an executive sponsor, it won’t work.

3. Premature optimization: Don’t optimize what you haven’t measured yet. Don’t scale what you haven’t validated yet. Don’t automate what you haven’t understood yet. Sequence matters.

4. Copy-paste architecture: “Netflix does it this way, so we’ll do it too.” Netflix has 2,000 microservices and 10,000 engineers. You have 20 services and 50 developers. Architecture must match your context, not a Silicon Valley blog.

5. Missing feedback loop: You implement but don’t measure. You have no data for decision-making. You have no retrospectives. You repeat the same mistakes. Measurement and iteration are more important than perfect implementation on the first try.

Czech specifics and regulatory context¶

Enterprise implementations in Czech Republic have specifics that foreign guides don’t cover:

NIS2 and DORA: From 2025, critical and important entities must meet strict cybersecurity requirements. This includes supply chain security, incident reporting, business continuity, and risk management. Your architecture must reflect these requirements from the beginning.

GDPR and data residency: Personal data of Czech citizens have specific requirements for processing and storage. Cloud-first strategy must consider where data physically sits. Prefer EU regions of cloud providers.

Limited talent pool: Czech Republic has excellent engineers, but fewer than we need. Automation and developer experience aren’t luxuries — they’re necessities for efficient use of the people you have.

Integration with legacy: Czech enterprise has a specific legacy stack — Oracle-heavy databases, SAP, custom-built systems from the 90s and 2000s. Modernization must be incremental and respect existing investments.

Conclusion and next steps¶

On-premise LLM inference isn’t a one-time project — it’s a continuous journey that requires clear vision, iterative approach, and measurable results. Start small, measure impact, scale what works.

Key takeaways:

• Start with assessment and proof of concept, not Big Bang migration
• Measure DORA metrics from day one — what you don’t measure, you don’t improve
• Invest in people as much as in tools — culture > technology
• Respect Czech context: regulations, talent pool, existing investments

Ready to start? Contact us for a non-binding assessment of your environment. We’ll tell you honestly where you are, where you can get to, and what it will cost.