PRIVATE LLM DEVELOPMENT
AI that stays on your infrastructure.
Self-hosted Llama, Mistral, Qwen, and DeepSeek deployments engineered for enterprise data privacy, compliance, and per-token cost predictability — with the inference, fine-tuning, and observability stack to make them work in production.
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Enterprise-grade delivery. Human-verified outcomes.
Built for teams that can't afford to guess.
Self-hosted Llama, Mistral, Qwen, and Deep Seek deployments engineered for enterprise data privacy, compliance, and per-token cost predictability — with the inference, fine-tuning, and observability stack to make them work in production.
Why teams move off hosted APIs.
There are exactly four reasons enterprises self-host. If even one applies strongly, it's worth a conversation.
Data sovereignty
Your prompts and outputs never leave your network. Anything covered by a DPA, BAA, or policy stays in your VPC.
Cost predictability
Above ~5–10 M tokens/day, self-hosted costs less per token — and the cost is flat, not variable per query.
Compliance & audit
Real audit logs, real retention controls, real access reviews — not a vendor's certification page.
Latency & availability
Co-located inference removes the hosted round-trip and the dependency on a provider's uptime.
Hosted API vs. private LLM — honest comparison.
| Time to first token | Minutes | Days–weeks |
| Frontier capability | Best-in-class | Strong open models (not always frontier) |
| Per-token cost at low volume | Very low | High (fixed GPU cost) |
| Per-token cost at high volume | Linear, expensive | Flat → effectively free |
| Data sovereignty | Provider's DPA | Yours, period |
| Fine-tuning | Limited | Full (Lo RA, QLo RA, SFT, DPO) |
| Operational burden | Near-zero | GPU ops, model lifecycle, eval |
| Best for | Prototyping, frontier reasoning | Regulated data, high volume, latency-sensitive |
Open models we deploy.
We re-benchmark on every meaningful release. These are the families running in production today.
Llama (Meta)
The safe default — broad capability, huge ecosystem, long context. 8 B / 70 B / 405 B.
Mistral / Mixtral
The cost/throughput pick. Mo E architecture, strong function-calling, permissive licenses.
Qwen (Alibaba)
Multilingual + tool-use leader. Strong code benchmarks at every size class.
Deep Seek
Exceptional reasoning-per-dollar. Outstanding cost-per-quality, strong code performance.
Three reference architectures we deploy.
Privacy-first single-tenant
Dedicated GPUs, often air-gapped. For regulated healthcare, defense, financial services. Highest sovereignty.
Cost-optimized multi-tenant
Shared GPU pool with model routing and aggressive batching. For AI-native Saa S, optimized for unit economics.
Hybrid private + hosted
Private LLM for high-volume routine work, hosted frontier for complex reasoning. Best first-year ROI.
How we engage on private LLM projects.
LLM/TGI deployment
Related reading
Go deeper on private, self-hosted LLMs and where they fit.
Run the numbers.
A 30-minute call: token volume, sensitivity, latency requirements. We'll tell you honestly whether private LLM makes sense at your scale — and if not, what does.
Hosted API — Private LLM
Ready to build?
Let's build your next intelligent platform.
Share your goals — we'll recommend a model, timeline, and team that fits Aanandi Technosoft.
Frequently asked questions
Llama vs. Mistral vs. Qwen vs. DeepSeek?+
Depends on workload. Llama is the safe default. Mistral wins on throughput economics. Qwen wins on multilingual and tool use. DeepSeek wins on reasoning-per-dollar. We benchmark on your data first.
What hardware do I need?+
From a single L40S for a 7B model up to a cluster of 8–16 H100s for a 70B model at production scale. Discovery sizes the hardware to your workload.
How does cost compare to OpenAI?+
Break-even is usually 5–10M tokens/day. Above 50M tokens/day, private is typically 70–90% cheaper per token, with zero variance.
Can we fine-tune?+
Yes — LoRA, QLoRA, full SFT, DPO. Often LoRA is sufficient and far cheaper. Included in the Production Deployment when in-scope.
Can it be deployed air-gapped?+
Yes. Models, dependencies, and weights pre-staged. No outbound internet from the inference cluster.
Do you handle SOC2 / HIPAA paperwork?+
We architect to the framework and produce the evidence your auditor needs. Your security team and auditor own the certification itself.