We deploy and manage private AI infrastructure on your own servers — self-hosted LLMs, OpenClaw agents, local vector databases, and custom AI pipelines that keep sensitive data within your perimeter. Zero vendor lock-in, full data sovereignty.
ZTABS Self-Hosted AI & Private LLM Deployment: We deploy and manage private AI infrastructure on your own servers — self-hosted LLMs, OpenClaw agents, local vector dat 300+ clients, 500+ projects. Houston, TX.
ZTABS provides self-hosted AI & private LLM deployment — We deploy and manage private AI infrastructure on your own servers — self-hosted LLMs, OpenClaw agents, local vector databases, and custom AI pipelines that keep sensitive data within your perimeter. Zero vendor lock-in, full data sovereignty. Our capabilities include private LLM deployment, openclaw setup & management, gpu infrastructure provisioning, and more.
Deployed 20+ self-hosted LLM stacks (Llama 3, Mistral, Qwen) on customer infrastructure — every deployment ships with a documented GPU/quant tradeoff matrix, vLLM/TGI tuning data, and SOC 2-friendly air-gapped configs.
Not every organization can send sensitive data to OpenAI or Anthropic. Healthcare providers, law firms, financial institutions, and defense contractors need AI that runs entirely within their own infrastructure — with zero external API calls and complete data sovereignty. At ZTABS, we specialize in deploying self-hosted AI systems using open-source models from Meta (Llama), Mistral, Google (Gemma), and others.
We set up and manage infrastructure including OpenClaw for self-hosted AI agent orchestration, Ollama for local model serving, vLLM for high-throughput inference, and vector databases like Qdrant and Weaviate running on your own hardware or private cloud. The economics are compelling for high-volume use cases: organizations processing 10M+ tokens per month can achieve 70–90% cost reduction compared to API-based approaches, while gaining unlimited throughput, zero rate limits, and complete privacy. We handle the entire stack: GPU provisioning (NVIDIA A100/H100, AMD MI300), model selection and quantization for your hardware, inference optimization (batching, caching, speculative decoding), and monitoring.
Post-deployment, we provide model updates, performance tuning, and scaling as your usage grows.
Core capabilities we deliver as part of our self-hosted AI & private LLM deployment.
Deploy Llama, Mistral, Gemma, and other open-source models on your infrastructure with optimized inference.
Full OpenClaw deployment with persistent memory, security hardening, skill development, and multi-channel integrations.
NVIDIA A100/H100 and AMD MI300 provisioning, configuration, and optimization for AI workloads.
Self-hosted Qdrant, Weaviate, or pgvector for RAG systems that never leave your network.
Model quantization (GPTQ, AWQ, GGUF) and inference optimization to maximize performance on your hardware.
24/7 monitoring, model updates, performance tuning, and scaling support for your private AI infrastructure.
Our team picks the right tools for each project — not trends.
Leverage the power of Python to streamline operations, reduce costs, and drive innovation. Our Python solutions enable businesses to enhance productivity and deliver results faster than ever.
Docker empowers businesses to streamline their development and deployment processes, enhancing agility and reducing time-to-market. By leveraging container technology, organizations can achieve significant cost savings and improved operational efficiency.
AWS empowers organizations to innovate faster, reduce costs, and enhance operational efficiency. Leverage the power of the cloud to streamline processes and drive growth in an ever-evolving digital landscape.
Node.js empowers businesses to build scalable applications with unparalleled speed and efficiency. By leveraging its non-blocking architecture, organizations can deliver seamless user experiences and accelerate time-to-market, driving innovation and growth.
PostgreSQL empowers businesses with an advanced, open-source database solution that enhances data integrity, scalability, and performance. Experience a significant reduction in operational costs while driving innovation and agility in your organization.
Every self-hosted AI & private LLM deployment project follows a proven delivery process with clear milestones.
Evaluate your hardware, network, and compliance requirements to design the optimal self-hosted AI architecture.
Choose the right open-source models and quantization levels for your use case, accuracy needs, and hardware capacity.
Deploy models, vector databases, and orchestration layers on your infrastructure with security hardening.
Connect self-hosted AI to your applications, test throughput, latency, and accuracy against your benchmarks.
Implement network isolation, access controls, encryption, audit logging, and compliance documentation.
Model updates, performance optimization, scaling, and 24/7 monitoring of your private AI infrastructure.
What sets us apart for self-hosted AI & private LLM deployment.
We've shipped HyperPrompt, Chatsy, Morphed, and 20+ more. We understand AI infrastructure from the product side, not just the ops side.
We handle model deployment, application integration, frontend, and backend — not just infrastructure provisioning.
Deep experience with Llama, Mistral, Gemma, and the open-source ML ecosystem — choosing the right model for your constraints.
HIPAA, SOC 2, and data residency requirements built into every deployment — not bolted on afterward.
We optimize GPU utilization, batching, caching, and quantization to minimize your per-token cost while maintaining quality.
Monthly model updates, performance tuning, and scaling support — your private AI stays current without your team managing it.
Projects typically start from $10,000 for MVPs and range to $250,000+ for enterprise platforms. Every engagement begins with a free consultation to scope your requirements and provide a detailed estimate.
Across our portfolio, we track delivery patterns to improve outcomes. Our internal data from 2023-2026 shows:
| Alternative | Best For | Cost Signal | Biggest Gotcha |
|---|---|---|---|
| Managed APIs (OpenAI, Anthropic, Google Vertex, AWS Bedrock) | Teams without data residency requirements, needing fastest time-to-market with top-tier models. | $3–$15 per million tokens blended at mid-tier (indicative). | Data egress to vendor — blocks you from regulated/confidential use cases. Rate limits and quota caps can throttle production bursts. No model version control. |
| Cloud-provider private endpoints (AWS Bedrock Provisioned, Azure OpenAI) | Teams wanting top-tier models with a data processing agreement inside their cloud tenant. | $8–$30 per model-unit-hour; $6K–$25K/month minimum commitments (indicative). | Not truly self-hosted — vendor still sees inference traffic, even if contract says 'no training on your data.' Compliance audits often treat this as third-party, not internal. |
| Boutique self-hosted AI specialist (ZTABS tier) | Regulated-industry companies (healthcare, finance, legal) needing private LLM inside their VPC or on-prem. | $140–$220/hour; $50K–$400K per engagement (indicative). | We insist on a 1–2 week hardware sizing + model evaluation sprint — teams that skip it over-buy GPUs by 2–4× OR under-size and hit throughput walls. Sizing math is not optional. |
| Enterprise AI platforms (Databricks, SambaNova, Together AI) | Large enterprises wanting turn-key hosted open-source models with support contracts. | $200K–$2M/year (indicative). | 'Turn-key' can mean opinionated — fine-tuning, custom stacks, and novel architectures often require escape hatches. |
| In-house ML platform team | FAANG-scale orgs with 10+ models in production and dedicated MLOps staff. | $3M–$15M/year platform team (indicative). | Requires hiring an MLOps lead ($300K+) plus SREs; only makes economic sense past ~$10M/year in inference spend. |
Self-hosted Llama 3.1 70B vs. GPT-4o API. Self-hosted on 2×A100 at $5K/month, capacity ~1.5M tokens/hour = ~1B tokens/month at 90% utilization. GPT-4o at $5/M input + $15/M output (blended ~$10/M): 1B tokens = $10K/month. Self-hosted saves $5K/month AT CAPACITY. Break-even: ~500M tokens/month (utilization drops below 50% and fixed GPU costs become wasteful). Fine-tuning costs. Fine-tuning Llama 70B with LoRA on 4×A100: ~$1K–$3K one-time. OpenAI fine-tuning GPT-4o-mini: $3/M training tokens × 10M tokens = $30 + $0.30/M inference overhead (vs. $0.15 for base) = ~2× inference cost. Self-hosted fine-tune wins on ongoing cost above 200M inference tokens/month; managed API fine-tune wins for small-scale + time-to-market. HIPAA/regulated ROI. A self-hosted AI for a healthcare SaaS serving 100 clinics at $10K/year: $1M/year revenue. Going managed with a BAA would still see data egress — lost 30 clinics worth $300K/year to compliance concerns. Self-hosted build at $200K = payback in year 1 if it unlocks the regulated market.
Llama 70B served via vLLM handled 5 concurrent users; 6th request triggered CUDA OOM, whole server restarted, all 6 users timed out. Fix: set gpu_memory_utilization=0.85 (leaves headroom), enable PagedAttention, configure max batch size based on real prompts (not synthetic benchmarks), and add a request queue with backpressure.
A team loaded an INT4-quantized 70B model to fit on one A100; benchmarks stayed at 85% but domain-specific tasks (legal summarization) dropped from 88% to 73%. Fix: always eval on YOUR task set, not public benchmarks. Test FP16 vs. INT8 vs. INT4 on 100 representative prompts; only quantize if quality delta is <3%.
A user pasted a 40K-token PDF; vLLM allocated memory for full context, hit OOM mid-inference, killed the worker. Fix: set max context length (--max-model-len 32768 in vLLM), reject requests exceeding limit at the gateway with a clear 413 error, and route long-context use cases to a separate GPU pool or a long-context-specialized model.
Team rolled back from Llama 3.1 to 3.0 for a quality regression fix; prompt cache keys included model version, so cache hit rate dropped from 40% to 0% — costs spiked. Fix: version cache keys AND plan cache warming (replay top 10K prompts on rollback) OR keep both models warm during rollout/rollback windows.
Rate limit was 100K tokens/min per user; streaming responses counted tokens at request-accept time, not generation time. Abuser sent 50 concurrent streaming requests, each generating 2K tokens, total 100K in 10 seconds. Fix: rate-limit at the server BASED on actual tokens generated (streamed), not input tokens alone. Use Redis sliding-window counters updated per generated chunk.
Verified reviews from real client engagements — sourced from our public testimonial archive and Clutch profile.
My experience is throughout positive. Communication, service, the short response times and the flawless execution of a challenging topic was absolutely great. ZTABS is definitely my first choice again.
Christian Neff
Bank Software Advisory · Bank Software Advisory
Fintech
Fantastic Agency! I couldn't fault them even if I tried. They always go above and beyond to meet your expectations and always produces quality work. Thank you ZTABS.
Stephanie Kal
CEO · Beauty Finder Australia
Marketplace
It has been great working with ZTABS. They bounce off the ideas along the way. Amazing Experience.
Joel Rowe
CEO · Drill Quoter
Marketplace
We don't just contract — we ship and operate our own software. 17 products in production.
Find answers to common questions about our self-hosted AI & private LLM deployment.
Three reasons: data privacy (sensitive data never leaves your servers), cost (70-90% savings at high volume), and control (no rate limits, no vendor lock-in, custom model fine-tuning). Organizations in healthcare, legal, finance, and defense often can't send data to external APIs due to regulatory requirements.
We build production-grade AI systems — from machine learning models and LLM integrations to autonomous agents and intelligent automation. 17 production SaaS products shipped, 300+ clients served.
We build modern web applications using Next.js, React, and Node.js — from marketing sites and dashboards to full-stack SaaS platforms. Every project ships with responsive design, SEO optimization, and performance scores above 90 on Core Web Vitals.
We build native iOS, Android, and cross-platform mobile apps using Swift, Kotlin, React Native, and Flutter. From consumer apps with social features to enterprise tools with offline sync — we deliver polished, high-performance applications from concept to App Store and Play Store.
End-to-end SaaS development from MVP to scale — multi-tenancy, Stripe billing, role-based access, and cloud-native architecture. We have built and shipped 17 SaaS products of our own, serving 50,000+ users. Next.js, Node.js, PostgreSQL, AWS and Vercel.
Get a free consultation and project estimate for your self-hosted AI & private LLM deployment project. No commitment required.
