LLMOps operations
Operations
from $2,500 /month
Operations of an existing LLM stack: monitoring, upgrades, retraining, incidents, reporting. SLA on latency and quality.
Sovereign AI and LLMOps on owned compute
Local LLM inference on owned hardware, RAG pipelines, AIOps self-healing playbooks. No data exfiltration to third-party APIs, no vendor lock-in, no budget surprises.
The problem
Why cloud AI does not fit production scale
- — Customer data is routed to third parties — breach of GDPR / 152-FZ / NDA scopes.
- — Cost at scale is unpredictable: 100,000 requests per day equals a production infrastructure budget.
- — Vendor lock-in: switching the model or the provider breaks integrations and forces prompt rewrites.
- — Regulatory risk: access restrictions, sanctions, provider-side outages.
The solution
What we do
- — On-premise inference of sovereign LLMs on vLLM, TensorRT-LLM, Aphrodite Engine.
- — RAG pipelines with vector databases Qdrant, ChromaDB, PGVector — search over the corporate knowledge base.
- — Hybrid model orchestration: lightweight routing → heavyweight expert domain.
- — AIOps self-healing playbooks: automated incident triage, runbook retrieval, MTTR < 5 minutes.
Capabilities
Three areas of application
01
Local LLM inference
Deploying Nemotron 3 Super, GPT-OSS 120B, Qwen3 Coder 480B, Gemma 3, Llama 3 on owned GPUs. Inference via vLLM, TensorRT-LLM, Aphrodite Engine, Ollama. Load balancing, quotas, request auditing.
02
RAG / Vector search
Corporate knowledge base with semantic search on Qdrant, ChromaDB, PGVector. Embedding pipelines, chunking, hybrid search (dense + sparse), result re-ranking. Integration with existing sources: documents, tickets, runbooks.
03
AIOps / Self-healing
LLM-driven alert and log analysis, mapping to historical incidents, automated application of known runbook solutions. 2,000+ playbooks in production, MTTR < 5 minutes for typical incidents. Augments engineers — does not replace them.
Stack
Technology stack
LLM models
Nemotron 3 SuperGPT-OSS 120BQwen3 Coder 480BGemma 3Llama 3DeepSeek
Inference engines
vLLMAphrodite EngineTensorRT-LLMOllamallama.cpp
Vector DB / RAG
QdrantChromaDBPGVectorPostgreSQL 17+
Orchestration & AIOps
Python (Asyncio)GoBashAnsibleeBPFOpenTelemetryPrometheusGrafana
Approach
Anti-hype: when it fits and when it doesn’t
When it fits
- ✓ > 5,000 requests per day — on-premise GPUs break even.
- ✓ Sensitive data (GDPR, 152-FZ, healthcare, finance).
- ✓ Clear use-case: classification, extraction, corporate search, operations support.
- ✓ Willingness to invest in data quality and chunking strategy.
- ✓ A team capable of defining KPIs without AGI mythology.
When it does NOT fit
- — Low load (< 1,000 requests/day) — cloud APIs are cheaper.
- — No clear use-case — “AI for AI’s sake” is a wasted budget.
- — Expectations of AGI instead of an engineering tool — disappointment is guaranteed.
- — No internal data discipline — garbage in, garbage out.
- — Hype-driven launches for an investor demo — not our profile.
Engagement
How we deliver
01
Discovery
2 weeks. Use-case, data volume, expected load, KPIs, data constraints.
02
PoC
3–4 weeks. Prototype on a lightweight stack, quality and latency measurement, validation set from real data.
03
Production
2–3 months. Deployment on target hardware, quality monitoring, A/B, feedback loops.
04
LLMOps
Continuous. Model upgrades, embedding retraining, quality audits, playbook expansion.
Pricing
Pricing
Full deployment
Build & Operate
from $18,000 one-time
Hardware selection and procurement (GPUs), stack deployment, data-source integration, team enablement. Then operations on subscription.
Final pricing after discovery. NDA signed before discussing the use-case and data in detail.
FAQ
Frequently asked questions
Why on-premise LLMs when cloud APIs exist?
Data control (GDPR, 152-FZ, NDA scopes), predictable cost at scale, no vendor lock-in, no third-party request leakage. On-premise GPUs typically break even at 5,000–10,000 requests per day.
Which models do you run in production?
Nemotron 3 Super, GPT-OSS 120B, Qwen3 Coder 480B, Gemma 3, Llama 3. Model selection is driven by the domain task, not by marketing. Lightweight Gemma for classification and routing, heavyweight models for expert domains.
What is AIOps self-healing?
Python/Bash playbooks that analyse incidents via LLM, retrieve analogous historical incidents from the knowledge base (RAG over Qdrant/PGVector), and apply known runbook solutions without engineer intervention. Target MTTR below 5 minutes for typical incidents. The engineer stays in the loop for complex cases.
When AI/LLMOps does NOT fit?
Low load (< 1,000 requests/day — cloud is cheaper), no clear use-case, expecting AGI instead of an engineering tool, no internal data discipline. Hype-driven launches for an investor demo are not our profile.
What about data residency and compliance?
Data is processed on the customer’s on-premise compute or in a dedicated isolated environment. No exit to third-party clouds. Request logging, access audit, NDA-scope isolation.
What hardware is needed?
Depends on the model. Gemma 3 (12B) — 1 GPU class L40S/A100. GPT-OSS 120B or Qwen3 Coder 480B — a 4–8 GPU cluster with NVLink. Exact specification follows the workload discovery.
Related
Related services
Get in touch
Tell us about your task — we’ll send a proposal within 24 hours
The first consultation is non-binding. We sign an NDA before discussing the use-case in detail.
Full technology stack
What we actually use in production. Versions intentionally omitted — products are regularly updated, but the philosophy and principles remain stable.
Nemotron 3 Super GPT-OSS 120B Qwen3 Coder 480B Gemma 3 Llama 3 DeepSeek vLLM Aphrodite Engine TensorRT-LLM Ollama llama.cpp Qdrant ChromaDB PGVector PostgreSQL 17+ Python (Asyncio) Go Bash Ansible eBPF OpenTelemetry Prometheus Grafana VictoriaMetrics ELK Stack AIOps self-healing MTTR < 5 min RAG pipelines Hybrid orchestration Sovereign AI Whisper Vosk ASR (offline) speech recognition