Sentinel Integrations: Sovereign AI Benchmarking & Solution Map

Purpose: A standardized, data-driven framework for comparing Cloud AI (Google, OpenAI, Microsoft) vs. Self-Hosted (Sovereign) solutions.

Update Cadence: Weekly (Part of the Weekly Operations Review - WOR).

Status: Initializing Framework.

📊 Benchmarking & Cost Metrics (Real-World Data)

| Provider | Model | Tokens/Sec (Avg) | Cost per 1M Tokens (Input/Output) | Best Use Case | Benchmarking Method |

|:---|:---|:---|:---|:---|:---|

| Google | Gemini 1.5 Pro | 100+ | $1.25 / $5.00 | Massive Context (RAG) | Large Doc Analysis |

| OpenAI | GPT-4o | 80+ | $2.50 / $10.00 | Logic / General Purpose | Reasoning Bench |

| Microsoft | Azure GPT-4o | 60+ (Rate limited) | Enterprise Tier | Corporate Compliance | Throughput Cap Test |

| Sovereign | Llama 3 70B (Mac Studio) | 20-40 | $0.00 (Opex Only) | IP-Sensitive / Heavy Dev | Local Inference Latency |

🛠️ Need-to-Solution Map (Searchable Structure)

{
  "business_needs": [
    {
      "category": "Planning & Strategy",
      "requirement": "High reasoning, complex scheduling",
      "primary_solution": "OpenAI o1 / GPT-4o",
      "sovereign_fallback": "Llama 3 70B (Instruct)",
      "validation_test": "Logic-Gate-01"
    },
    {
      "category": "Medical Transcription",
      "requirement": "HIPAA compliance, domain terminology",
      "primary_solution": "Google Med-PaLM (Cloud)",
      "sovereign_fallback": "Whisper Large-v3 (Local)",
      "validation_test": "Term-Accuracy-MD"
    },
    {
      "category": "Video Recognition",
      "requirement": "Real-time frame analysis, zero cloud leak",
      "primary_solution": "Microsoft Florence (Azure)",
      "sovereign_fallback": "Llava / Moondream (Local)",
      "validation_test": "Object-Latency-V1"
    },
    {
      "category": "Legal Document Pipeline",
      "requirement": "Deep audit trail, massive context ingestion",
      "primary_solution": "Gemini 1.5 Pro (2M Context)",
      "sovereign_fallback": "DeepSeek-V3 (64k-128k Local)",
      "validation_test": "Context-Recall-L1"
    }
  ]
}

🧪 Benchmark Run Log (Lab History)

| Date | Host | Provider | Engine / LLM | Throughput (T/s) | Latency (ms) | Notes |

|:---|:---|:---|:---|:---|:---|:---|

| 2026-02-16 | signexus | Local | Node.js v25.6.0 | 62,482,643 | 0.001 | Logic Layer Overhead |

| [PENDING] | sivault-alpha | Local | Llama 3 8B (Ollama) | TBD | TBD | Sovereign Speed Tier |

| [PENDING] | Cloud | OpenAI | GPT-4o | TBD | TBD | Cloud Logic Tier |

| [PENDING] | Cloud | Google | Gemini 1.5 Pro | TBD | TBD | Cloud Context Tier |

🛡️ Sentinel Integration Test (SIT) Standard

To maintain a high-trust consultancy, all services are audited against these data points:

1. Latency: Time-to-first-token (TTFT).

2. Throughput: Sustained tokens per second (TPS).

3. Privacy: Zero-egress validation for local nodes.

4. Economic: Monthly amortized cost per 1M tokens.

🛡️ Validation & Audit Protocol (Isnad Standard)

To audit the claims of providers and our own local builds, every solution must pass these gates:

1. Throughput Validation: Automated script measuring actual T/s under variable load.

2. Context Integrity: "Needle in a Haystack" test for large context models.

3. Privacy Audit: Packet-sniffing during inference to ensure zero unencrypted egress.

4. Cost Reconciliation: Actual monthly bill vs. forecasted token usage.