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TurboPrivate AI — Self-Hosted Enterprise AI Platform

Switch from OpenAI in 30 seconds. Drop-in compatible API with built-in safety, governance, and 40–60% cost reduction.

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Run powerful LLMs on your own hardware — with enterprise safety, governance, and full data sovereignty.


Quick Start

One-Click Install

curl -fsSL https://get.turboprivate.ai | bash

Or via pip

pip install turboprivate-ai
turbo deploy --provider bare-metal --gpu auto
turbo model serve meta-llama/Llama-3.1-8B --quant int4
turbo chat

Docker Compose (Hardware-Aware)

git clone https://github.com/Kubenew/turboprivate-ai.git
cd turboprivate-ai

# Auto-detects GPU / Apple Silicon / CPU
curl -fsSL https://get.turboprivate.ai | bash

# Or manually:
docker compose -f docker-compose.gpu.yml up -d    # NVIDIA GPU
docker compose -f docker-compose.mac.yml up -d     # Apple Silicon
docker compose -f docker-compose.cpu.yml up -d     # CPU fallback

Why TurboPrivate AI?

Feature TurboPrivate AI Ollama vLLM OpenAI API
Data Sovereignty ✅ Full ✅ Full ✅ Full ❌ Cloud
Enterprise Safety ✅ Mythos Safe (7 verifiers) ❌ None ❌ None ⚠️ Basic
OpenAI Compatible ✅ 100% ✅ Partial ✅ Partial ✅ Native
INT4/AWQ Quantization ✅ TurboQuant v3 ✅ GGUF ✅ AWQ N/A
RAG Pipeline ✅ Built-in ❌ External External ❌ External
Audit Trail ✅ Immutable JSONL ❌ None ❌ None ⚠️ Limited
RBAC / Multi-tenant ✅ Enterprise ❌ None ❌ None ✅ Enterprise
Kubernetes Native ✅ Helm + K3s ❌ Manual ⚠️ Manual N/A
Cost (RTX 4090) ~8x cheaper Free Free $5-10/M tokens

🏢 For Enterprises

TurboPrivate AI is the Enterprise On-Premise AI Gateway — a secure, compliant orchestration layer between your corporate data and open-source models.

What We Are

  • ✅ Secure Wrapper: Mythos Safe gate with 7 verifiers (injection, PII, toxicity, etc.)
  • ✅ OpenAI Parity: 100% compatible API — swap base_url and you're done
  • ✅ SAP HANA Native: Direct, secure RAG connector with SQL injection guard + RLS
  • ✅ Audit & Compliance: Immutable JSONL logs, GDPR/HIPAA/SOC 2 ready
  • ✅ Hardware Agnostic: GPU, Apple Silicon, or CPU — auto-optimized

What We Are Not

  • ❌ Model Training: We don't train models from scratch
  • ❌ Custom UI: We integrate Open WebUI / LibreChat instead of building our own
  • ❌ Vector DB: We connect to Qdrant, Milvus, pgvector — we don't replace them

See docs/ENTERPRISE.md for architecture details.

Security & Compliance

  • Full data sovereignty: Nothing leaves your infrastructure
  • Mythos Safe: 7-layer defense (injection, PII, toxicity, hallucination, etc.)
  • Audit trail: Immutable JSONL logs with SIEM integration
  • RBAC: Fine-grained access control with OIDC/SAML support
  • Compliance ready: GDPR, HIPAA, SOC 2, PCI-DSS, ISO 27001

See SECURITY.md and docs/COMPLIANCE.md for details.

Enterprise Integrations

  • SAP HANA: Vector store + RAG pipeline (Guide)
  • SAP AI Core: BYOM deployment support
  • Kubernetes: Helm charts, HPA, multi-cluster
  • Observability: Prometheus, Grafana, OpenTelemetry
  • Secrets: HashiCorp Vault, AWS Secrets Manager, K8s Secrets

Support & SLAs

Tier Response Includes
Community GitHub Issues OSS core, docs, community support
PoC / Pilot 48h 4-8 week trial, 2 models, training
Enterprise 4h SLA 99.5%, unlimited models, TAM
Enterprise Plus 1h Multi-cluster, custom verifiers, SOC2

📅 Book a 30-min PoC Call | ✉️ Contact Sales


📊 Performance (RTX 4090)

Model Quant Tokens/sec VRAM Cost vs Cloud
Llama 3.1 8B INT4 110+ ~5.8 GB ~8x cheaper
Qwen2.5 32B INT4 45+ ~22 GB ~6x cheaper
Llama 3.1 70B INT4 18+ ~48 GB ~5x cheaper

Independent benchmarks: benchmarks/


🛡️ Architecture

CLI / SDK / Dashboard
        ↓
   API Gateway (FastAPI · Auth · Rate Limiting)
        ↓
┌─────────────────┐  ┌───────────────────┐
│  Mythos Safe    │  │  TurboQuant INT4  │
│  Verifiers ·    │  │  vLLM/llama.cpp   │
│  Audit Trail    │  │  Inference Engine │
└─────────────────┘  └───────────────────┘
        ↓
   Memory & RAG (TurboMemory · pdf2struct)
        ↓
──────────┐ ┌──────────┐ ┌──────────┐
│  K3s     │ │Monitoring│ │ Storage  │
│  Cluster │ │Prom/Graf │ │ PG/Redis │
└────────── └──────────┘ └──────────┘

🎬 Demo

TurboPrivate AI deployment demo


Documentation


🔄 Changelog

0.1.9 (2026-05-17)

  • Optimized SAP HANA Secure Connector: pre-compiled regex for high-throughput PII masking
  • Air-gapped installer support: --offline flag + local compose file detection
  • CI/CD security pipeline: automated SQL injection blocking tests
  • Performance tuning: vectorized masking hints, reduced regex overhead

0.1.8 (2026-05-17)

  • SAP HANA Secure RAG Connector: SQL injection guard, RLS mapping, PII masking
  • Hardware-aware installer: auto-detects NVIDIA / Apple Silicon / CPU
  • Docker Compose profiles: gpu.yml, mac.yml, cpu.yml for optimal deployment
  • README overhaul: "What We Are / Are Not" transparency, Enterprise Gateway positioning
  • Modular architecture: decoupled inference backends, plug-and-play vector DBs

0.1.7 (2026-05-17)

  • SECURITY.md with threat model, hardening guide, SBOM, responsible disclosure
  • CONTRIBUTING.md with dev setup, testing, PR guidelines
  • Enterprise Deployment Guide: air-gapped, HA, secrets, proxy, hardware sizing
  • Compliance readiness: GDPR, HIPAA, SOC 2, PCI-DSS, ISO 27001, EU AI Act
  • One-click installer (install.sh) + docker-compose.full.yml with GPU passthrough
  • GitHub issue templates: bug report, feature request, security report
  • README overhaul: feature comparison table, "For Enterprises" section, badges

0.1.6 (2026-05-16)

  • SAP HANA integration guide: cost calculator, security checklist, BYOM, compliance
  • Enterprise hardening best practices
  • SECURITY.md and CONTRIBUTING.md added

0.1.5 (2026-05-16)

  • SAP HANA vector store integration (LangChain + HanaDB)
  • FastAPI RAG endpoint with similarity search
  • Document ingestion with PDF/text + HNSW index

0.1.4 (2026-05-13)

  • Production Helm charts (configmap, ingress, services)
  • TurboQuant v3: AWQ + INT4 mixed-precision
  • K3s provisioner with multi-node discovery
  • vLLM backend: speculative decoding + prefix caching

Full changelog →


📄 License

Apache 2.0 — see LICENSE.


Built by Kubenew — ex-HPE engineer, 12+ years enterprise infrastructure

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