Self-Hosted Large Language Models, Deployed Under Your Control
Deploy self-hosted large language models entirely within infrastructure you control, on-prem, private cloud, or hybrid, so sensitive data never leaves your boundary and every inference call is auditable. Built for CTOs and compliance teams who need AI they can actually govern.
GDPR
HIPAA
ISO/IEC 27001
Certifications and Accreditations
Trusted by industry leaders
The Real Cost of Staying API-Only
Enterprises running high-volume LLM workloads through a third-party API routinely watch monthly costs move from a rounding error to a board-level line item within a year of scaling past pilot. That’s the cost problem.
The bigger one is control. Every prompt leaves your infrastructure boundary, which triggers a compliance review at any hospital, bank, or contractor handling regulated data. Model behavior also shifts on the vendor’s release schedule, not yours, a bad place to run production AI from. A self-hosted large language model keeps inference inside your own environment, so prompts, outputs, and fine-tuning data never cross a third-party boundary, and model versions only change when you decide to change them.
Ready to Own Your AI Infrastructure?
Talk to an engineer about what a self-hosted deployment would actually take for your stack.
Discuss Your DeploymentWhat Self-Hosted Large Language Model Deployment Actually Delivers
A Data Boundary That Actually Holds
Prompts, outputs, and embeddings stay inside infrastructure you control, whether that’s on-prem hardware, a private cloud VPC, or a hybrid setup. Nothing gets logged on a vendor’s servers by default.
Fine-Tuning on Data You'd Never Send to an API
Domain-specific fine-tuning runs on proprietary datasets, patient records, transaction histories, internal documentation, without that data ever leaving your environment or training someone else’s model.
Infrastructure Costs You Can Actually Forecast
Fixed GPU and infrastructure spend replaces per-token billing that scales with usage. At sustained high volume, the economics flip in your favor within months, not years.
An Audit Trail That Satisfies a Regulator
Every inference call, prompt, and model version is logged and traceable, the kind of evidence a SOC 2 auditor or a HIPAA compliance officer actually wants to see, not a vendor’s summary dashboard.
The Serving Layer Most Vendors Won't Name
Most self-hosted large language model pitches stop at "we use Kubernetes." We name the actual serving layer, because the choice between vLLM, SGLang, and TensorRT-LLM changes your throughput, your GPU bill, and your system latency under real concurrent load.
- vLLM or SGLang for flexible serving
- TensorRT-LLM when NVIDIA hardware is fixed
- GPU sizing across H100 and Blackwell
- Quantization tuned to your latency budget
- On-prem, private cloud, or hybrid hosting
Signals You're Ready for Self-Hosted Deployment
Data Can't Leave the Building
Patient records, financial transactions, or privileged legal documents carry contractual or regulatory restrictions that make routing them through a third-party API a non-starter, not just a preference.
Token Costs Outgrew the Pilot
What started as a few hundred dollars a month in API calls now shows up as a board-level line item once the feature reached every user in production.
Integration Runs Deeper Than a Chat Widget
The model needs to sit inside existing pipelines, internal tools, and data stores, not bolt on as a separate service your team has to work around.
Fine-Tuning Needs Are Domain-Specific
Generic model behavior doesn’t match your industry’s terminology or edge cases, and a vendor’s API contract won’t let you retrain on your own proprietary data.
An Auditor Is Going to Ask
Compliance, legal, or a customer’s security team wants a full trace of every inference call, in a format most API providers won’t hand over.
Vendor Roadmap Risk Is Unacceptable
A pricing change, a deprecated model, or a policy shift on someone else’s calendar shouldn’t be able to break a system your business depends on. That’s vendor lock-in, and it compounds.
How We Deploy a Self-Hosted Large Language Model
Assessment and Model Selection
We map your compliance requirements, data volumes, and latency needs, then benchmark current open-weight models, GLM-5.2, Qwen3.6, DeepSeek, and others, against your actual workload rather than a generic leaderboard. The output is a shortlist of two or three models with a clear recommendation and the tradeoffs behind each one.
Infrastructure and Serving Layer Design
We size the GPU footprint, H100, H200, or Blackwell-class hardware depending on budget and throughput needs, and choose the serving framework: vLLM or SGLang for flexibility, TensorRT-LLM when you're standardized on NVIDIA and raw throughput matters more than portability. Deployment target is on-prem, private cloud, or hybrid, based on your existing footprint.
Fine-Tuning and Domain Adaptation
We fine-tune the selected model on your proprietary data using parameter-efficient methods rather than full retraining, which keeps cost and turnaround reasonable. Quantization gets applied wherever it improves latency without meaningfully degrading output quality, validated against your own test set, not a generic benchmark.
Secure Integration and Governance
We connect the model to your existing systems, internal knowledge bases, retrieval-augmented generation pipelines, and SSO, then set up role-based access, audit logging, and prompt and output monitoring aligned to whichever standard applies, HIPAA, SOC 2, or the EU AI Act's GPAI obligations, which have been in force since August 2025.
Production Rollout
We move from pilot to production in phases, starting with a limited user group so we can catch latency, cost, and output-quality issues before they reach everyone. GPU autoscaling and load balancing are configured so a usage spike degrades gracefully instead of failing outright.
Monitoring and Model Re-Evaluation
Post-launch, we handle ongoing monitoring of performance, cost, and drift, and we schedule a model re-evaluation checkpoint rather than treating the deployment as finished once it ships. When a newer open-weight model outperforms yours on your own benchmarks, we help you swap it in.
How Compliance Requirements Vary Across Industries
A hospital system and a bank both want data control, but the pressure points aren’t identical.
In healthcare, HIPAA and rising state-level health data laws mean patient data touching a third-party API creates real breach-notification exposure, not just theoretical risk.
In fintech, SOC 2 obligations and internal risk teams often flag any transaction data leaving controlled infrastructure before a security review even starts.
Across both, the EU AI Act’s GPAI obligations, active since August 2025, add a second compliance layer on top of existing industry rules for any AI system touching EU users or data.
What Self-Hosted Large Language Models Deployment Actually Costs
Ranges below reflect typical engagements. Your number depends on model size, GPU footprint, and how deep the cloud infrastructure cost optimization) work needs to go, not a one-size number.
| Deployment Scope | Model Scale | Typical Timeline | Estimated Investment | Complexity |
|---|---|---|---|---|
|
Pilot / Single Use Case |
7B–14B parameter model, single GPU node |
3–5 weeks |
$15,000–$40,000 |
Low |
|
Departmental Deployment |
30B–70B parameter model, multi-GPU serving |
6–10 weeks |
$40,000–$120,000 |
Medium |
|
Enterprise Multi-Model Platform |
Multiple models, HA serving, full governance layer |
10–20 weeks |
$120,000–$300,000+ |
High |
Built to Pass the Review That Actually Matters
Compliance isn't a checkbox at the end of a deployment. We build the audit trail, access controls, and data handling into the architecture from the first infrastructure decision, backed by security and compliance work, not bolted on before a review.
Review Compliance FitHow a Self-Hosted LLM Fits Into Your Existing Stack
A self-hosted large language model is only useful if it plugs into what your team already runs. That means SSO instead of a separate login, your existing data pipelines instead of a new ETL job, and your CI/CD instead of a manual deployment step nobody remembers how to repeat. We build the integration layer around your stack, so it doesn't add integration debt on top of what you already carry.
SSO and Identity
Connects to your existing identity provider, Okta, Azure AD, or an internal SSO layer, so access control for the model follows the same policies as everything else your team already runs.
RAG and Data Pipelines
Wires into your existing document stores and data pipelines so retrieval pulls from current data instead of a stale export, built on the same retrieval-augmented generation foundation we use across other engagements.
Existing Agent and Chatbot Frameworks
If you’ve already got chatbot or agent tooling in place, the self-hosted model becomes a drop-in reasoning layer instead of a rebuild, preserving prompt templates and conversation logic that still work.
CI/CD for Model Updates
Model and framework updates ship through the same pipeline as your application code, versioned and rollback-ready, so a model swap doesn’t mean an off-hours manual deployment.
How Much Does It Cost to Deploy a Self-Hosted LLM?
Costs typically range from $15,000 for a single-model pilot to $300,000+ for a multi-model enterprise platform, depending on GPU footprint, fine-tuning scope, and integration depth. Tell us about your use case.
Technologies and Platforms We Use
Why Choose Citrusbug for Self-Hosted Large Language Model Deployment?
Model Refresh, Not Just Launch
Not a one-time install. We build in a re-evaluation checkpoint because the open-weight model that’s best today won’t be the best in six months, and your infrastructure should be able to swap it in without a rebuild.
Senior-Led Delivery
Engineers senior enough to make the serving-framework and GPU sizing calls sit on your project from the first architecture conversation, not just at the proposal stage.
Discovery Before Code
We document requirements, data flows, and compliance constraints before any infrastructure gets provisioned, so the architecture fits your actual environment instead of a generic template.
Cost-Optimized Infrastructure
GPU sizing and cloud configuration are tuned to your real usage pattern instead of oversized by default, so you’re not paying for capacity you’ll never touch.
Full Source Ownership
You get complete source code and infrastructure-as-code at delivery, under NDA by default, with nothing held back or licensed separately.
L1 to L3 Support
Post-launch support scales from basic monitoring to deep architecture troubleshooting, so you’re not stuck choosing between a support contract and no support at all.
FAQs on Self-Hosted Large Language Models
Do you train foundation models from scratch, or deploy existing ones?
We deploy and fine-tune proven open-weight models like GLM, Qwen, and DeepSeek. Training a foundation model from scratch is rarely justified for enterprise use cases; adapting an existing model is faster and cheaper.
Which open-source models do you recommend, and how does that change?
Model choice depends on your workload. We benchmark current options like GLM-5.2, Qwen3.6, and DeepSeek against your use case, and revisit that choice as better models ship.
Can you migrate us off an API-based setup without downtime?
Yes. We run the self-hosted deployment in parallel, validate output parity, then cut over traffic gradually so your existing integrations never go dark.
What happens when a newer, better open model is released after launch?
We include a model re-evaluation checkpoint in support plans. If a new release beats your current model on your own benchmarks, we help you swap it in.
Do you support fully air-gapped, offline deployments?
Yes. For defense, healthcare, and government clients we deploy models with zero external network calls, including offline model weights and local logging.
How do you handle GPU capacity if our usage spikes?
We design for autoscaling within your infrastructure limits and set alerts before you hit capacity, so a spike degrades gracefully instead of failing outright.
What's included in support after deployment?
Performance monitoring, security patching, model and framework updates, and an optional re-evaluation cycle, available as L1, L2, or L3 SLA tiers.
How does self-hosted pricing compare to ongoing API costs?
Self-hosted shifts you from per-token fees to fixed infrastructure costs. At sustained high-volume usage, most enterprises reach cost parity within 12 to 30 months.