Giz Models · Enterprise AI Model Gateway & FinOps Governance

Executive Summary: 1-Minute Briefing for Enterprise C-Suite — Ending Token Bill Shock and Data Leakage

As generative AI expands across enterprise engineering, finance, operations, customer service, and autonomous workflows, enterprise leadership (CIOs, CISOs, and FinOps Executives) faces two structural, existential threats: "Uncontrolled Token Bill Shock" and "Catastrophic Intellectual Property (IP) Exfiltration."

Giz Models is an enterprise-grade AI model gateway and real-time FinOps governance platform engineered to neutralize financial unpredictability and data sovereignty risks. It unifies over 500 commercial foundation models and private air-gapped on-premises inference clusters under a single standard API (https://api.giz.ai/v1), enforcing sub-millisecond cost accounting and complexity-driven dynamic routing inline.

+───────────────────────────────────────────────────────────────────────────────────────────+
|                           Giz Models Executive Value Matrix                               |
+───────────────────────────────────────────────────────────────────────────────────────────+
| 1. FinOps & CFO: $900,000 (1.2B KRW / 65.9%) Net Annual Savings & 100% Budget Hard-Cap    |
|    • StarRocks OLAP Real-Time Ledger: Immediate request cutoff within 50ms at 100% budget  |
|    • Redis Semantic Cache: 41.2% repetitive queries resolved at $0 via 0.98 similarity     |
|    • 2-Tier Complexity Routing: Routine queries routed to high-velocity models (-68.3% cost)|
+───────────────────────────────────────────────────────────────────────────────────────────+
| 2. CISO & SecOps: 100% Zero Data Retention (ZDR) & Air-Gapped Hybrid Routing              |
|    • Inline PII Masking: National IDs, bank accounts, and identifiers sanitized in < 0.5ms |
|    • Zero IP Exfiltration: Trade secrets and CAD schematics forced to private GPU clusters |
|    • Full Compliance: Financial Regulations, National Core Tech Act, K-ISMS-P, ISO 42001   |
+───────────────────────────────────────────────────────────────────────────────────────────+
| 3. CIO & Infrastructure: Single Endpoint, 99.99% High Availability, Zero Vendor Lock-in   |
|    • Standard OpenAI Interface: Zero code refactoring; replace Base URL and Virtual API Key |
|    • 17+ Upstream Routes: Sub-50ms transparent failover across global provider pools       |
|    • Latency & Scale: P95 TTFT improved from 520ms to 118ms (77.3%), throughput expanded 5.1x|
+───────────────────────────────────────────────────────────────────────────────────────────+

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1. Eliminating Enterprise C-Suite's 2 Fatal Risks

Decentralized, departmental adoption of public AI APIs exposes enterprises to severe financial and regulatory liabilities.

Risk 1: "Uncontrolled Token Bill Shock from Autonomous Agents and Workforce LLM Usage"

• Root Cause: Developers and autonomous agents frequently direct routine tasks (summaries, translations, JSON extraction) to costly flagship models (GPT-5.6, Claude Opus, Grok 4). When agents enter recursive loops or process bulk files without controls, unbudgeted six-figure invoices accumulate within days.
• Giz Models Solution:
  • StarRocks OLAP Real-Time Ledger (giz_billing_cost): Streams usage metrics at sub-second speeds, tracking exact prompt/completion tokens and currency costs per department.
  • 3-Tier Budget Hard-Cap: Triggers 80% Soft Alerts, 95% Hard Alerts, and strictly terminates requests within 50ms at 100% budget threshold (HTTP 402 Payment Required), reducing unbudgeted overruns to $0.
  • Intelligent 2-Tier Complexity Routing: Automatically evaluates prompt difficulty, routing simpler tasks to high-efficiency models (slashing token unit costs by 68.3%).
  • Redis Semantic Cache: Resolves 41.2% of repetitive queries from memory cache at exactly $0.

Risk 2: "Proprietary Schematics, Semiconductor Recipes, and Financial Ledgers Ingested by Public Cloud LLMs"

• Root Cause: Employees inadvertently paste 3D CAD metadata, semiconductor fabrication recipes, cost balance sheets, and M&A audit materials into public prompts, exposing enterprise trade secrets to foreign infrastructure and model retraining pipelines.
• Giz Models Solution:
  • Mandatory Zero Data Retention (ZDR): Routes public traffic strictly through enterprise B2B agreements that legally and technically forbid disk retention or model retraining.
  • Inline Real-Time PII & Secret Masking: Scans and anonymizes sensitive data and confidential tokens within 0.5ms at ingress.
  • Air-Gapped Hybrid Routing: Automatically detects Level-1 confidential assets and forces execution to private on-premises GPU/NPU clusters (infrastructure), preventing external network transit.

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2. Unified 500+ Multi-LLM & Omni-Modal Infrastructure Catalog

Giz Models normalizes fragmented vendor SDKs and proprietary authentication protocols into a single OpenAI-compatible standard interface (https://api.giz.ai/v1).

+-----------------------------------------------------------------------------------+
|                            Enterprise Applications                                |
|         (ERP, CRM, Dev Tools, Data Analytics, Autonomous Agent Workspaces)       |
+-----------------------------------------------------------------------------------+
                                         │
                 HTTPS / REST / WebSocket / mTLS (Strict JSON Schema)
                                         ▼
+-----------------------------------------------------------------------------------+
|                        Giz Models Enterprise Gateway                              |
|  ┌───────────────────┐  ┌─────────────────────┐  ┌─────────────────────────────┐  |
|  │ Ingress & API Key │  │  Semantic Routing & │  │  StarRocks Real-Time Ledger │  |
|  │ Tenant Auth & PII │  │  Complexity Engine  │  │  Budget Hard-Cap Enforcement│  |
|  └───────────────────┘  └─────────────────────┘  └─────────────────────────────┘  |
+-----------------------------------------------------------------------------------+
             │                                       │
             ▼                                       ▼
+──────────────────────────────+   +────────────────────────────────────────────────+
|   Public Frontier Model Pool |   |   Private On-Premises & Sovereign Cluster      |
|   (17+ Multi-Provider Route) |   |   (Zero Egress / Air-Gapped Network Topology)  |
|  • OpenAI Direct, OpenRouter |   |  • NVIDIA HGX H100/H200/B200 (vLLM, TRT-LLM)   |
|  • Google Gemini, Anthropic  |   |  • Rebellions ATOM / FuriosaAI RNGD NPU Pool   |
|  • DeepSeek Pro / Grok 4.6   |   |  • Triton Inference Server (Local Weights)     |
|  • Runware (Flux, MiniMax)   |   |  • Enterprise Local Milvus / Qdrant RAG        |
+──────────────────────────────+   +────────────────────────────────────────────────+

Omni-Modal Serving Catalog Technical Specifications

Modality Domain	Model Count	Representative Model Families	Technical Capabilities & Standards	Cost & Attribution Unit
Chat & Reasoning	500+ Models	GPT-5.6, Claude 3.7 Sonnet, DeepSeek V4 Pro, Gemini 3.8 Flash	Multi-step reasoning tokens, JSON Structured Outputs, Tool/Function Calling	Per 1,000 Prompt / Completion Tokens
Video Generation	120+ Models	MiniMax H3 Turbo, LTX-2.5 Distilled, CogVideoX-5B, SoulX	4K video upscaling, frame interpolation, text/image-to-video, lipsync	Per Rendered Frame / Second / Resolution
Image Generation	180+ Models	KREA 2 Turbo, Z-Image Turbo, FLUX.2, FLUX.1 Schnell, SDXL	Reference Image Control, inpainting/outpainting, typography text rendering	Per Image Resolution and Step Count
Audio & Speech	40+ Models	OmniVoice, Stable Audio 3, MiniMax Music, OpenAI Whisper/TTS	Zero-shot voice cloning, real-time STT streaming, background music synthesis	Per Audio Second and Character Count
3D Asset Modeling	7+ Models	TRELLIS.2, Tripo 3D, Hunyuan 3D 2.0, Meshy-6	Rapid 3D polygon mesh generation, GLB/FBX/OBJ physically based rendering	Per Generated Asset and Polygon Density

Operational Evidence Links:

• Real-time conversational health and token streams are monitored alongside P95 latencies via models-chat-screen.
• High-volume video and generative media pipelines reconcile per-second compute costs through models-video-screen.
• On-premises air-gapped compute topologies and rack configurations are audited via infrastructure.

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3. Decoupling Canonical Logical Models from Multi-Provider Routes

Hardcoding third-party vendor URLs into enterprise software creates severe operational vulnerabilities when an upstream provider experiences outages or alters API contracts.

Giz Models separates requested Canonical Logical Models from physical Multi-Provider Routes. Behind a single model (such as DeepSeek V4 Pro), the gateway balances traffic across 17+ upstream providers (OpenAI Direct, OpenRouter, AKRouter, LinkAPI, CCGO, Tokeness, DeepInfra) using a 4-factor scoring algorithm calculated every 5ms:

1. Rolling P95 TTFT (Time-to-First-Token): Prioritizes routes delivering the fastest initial streaming token over the preceding 60 seconds.
2. Dynamic Availability Score: Exclusively routes to endpoints with >= 99.9% HTTP 200 success rates over the last 1,000 calls.
3. Circuit Breaker Cooldown: Instantly isolates providers generating 3+ HTTP 429 or credit exhaustion errors into a 3-minute cooldown.
4. Effective Landed Cost (ECB Normalized): Dispatches requests to the lowest net cost route after accounting for real-time exchange rates and volume tiers.

Model Integrity Principle: Giz Models never arbitrarily downgrades user-requested models to inferior alternatives without explicit application authorization. Traffic fails over strictly across verified providers serving the exact same model weights.

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4. Defense Against Token Bill Shock: StarRocks OLAP Real-Time Ledger & 3-Tier Budget Hard-Cap

Traditional proxy gateways rely on delayed batch logging, allowing runaway scripts to drain hundreds of thousands of dollars before alerts trigger. Giz Models embeds a distributed StarRocks OLAP engine to ingest token transactions sub-second and enforce hard budget stops inline.

3-Tier Budget Hard-Cap Policy

[Inbound API Request] ──> [Redis Token Bucket: Rate Limit Verification]
                               │ Passed
                               ▼
        [StarRocks Sub-Second Aggregation (< 3ms)]
        SELECT SUM(landed_cost_usd) FROM giz_billing_cost
        WHERE department_id = 'dept-fin-01' AND recorded_at >= '2026-09-01';
                               │
            ┌──────────────────┼──────────────────┐
            │ < 80% Budget     │ 80% ~ 99% Budget │ >= 100% Budget Cap
            ▼                  ▼                  ▼
     [Normal Execution]  [Soft Alert]       [Budget Hard-Cap Enforcement]
     High-speed routing   Slack/Email notice Immediate cutoff within 50ms (HTTP 402)
                         Finance team alert  Fallback to private local model
                                            or await executive budget increment

1. 80% Consumed (Soft Alert): Issues automated webhook notifications (Slack, Teams, Email) to department leads regarding spend velocity.
2. 95% Consumed (Hard Alert): Requires explicit approval headers for frontier models and diverts non-urgent batch requests to queued execution.
3. 100% Consumed (Budget Hard-Cap): Physically blocks incremental calls within 50ms (returning HTTP 402 Payment Required). Unapproved overruns are stopped completely, with optional fallback routing to free internal on-premise models.

giz_billing_cost Dual-Ledger StarRocks Schema

CREATE TABLE giz_billing_cost (
    event_id VARCHAR(64) NOT NULL,
    recorded_at DATETIME NOT NULL,
    organization_id VARCHAR(64) NOT NULL,
    department_id VARCHAR(64) NOT NULL,
    project_id VARCHAR(64) NOT NULL,
    api_key_id VARCHAR(64) NOT NULL,
    logical_model VARCHAR(128) NOT NULL,
    provider_route VARCHAR(64) NOT NULL,
    prompt_tokens INT NOT NULL,
    completion_tokens INT NOT NULL,
    reasoning_tokens INT DEFAULT 0,
    cached_tokens INT DEFAULT 0,
    upstream_currency VARCHAR(3) NOT NULL, -- USD, CNY, etc.
    upstream_unit_cost DECIMAL(18, 8) NOT NULL,
    ecb_exchange_rate DECIMAL(12, 6) NOT NULL, -- European Central Bank Daily Reference Rate
    landed_cost_usd DECIMAL(18, 8) NOT NULL,   -- Normalized Real Net Cost
    latency_ttft_ms INT NOT NULL,
    latency_total_ms INT NOT NULL,
    http_status_code INT NOT NULL
) ENGINE=OLAP
PRIMARY KEY(event_id, recorded_at, organization_id)
PARTITION BY DATE_TRUNC('month', recorded_at)
DISTRIBUTED BY HASH(department_id) BUCKETS 32
PROPERTIES (
    "replication_num" = "3",
    "storage_medium" = "SSD"
);

• European Central Bank (ECB) Reference Rate Integration: Normalizes heterogeneous currency billings (USD, CNY) daily into standard USD, eliminating currency drift in global corporate accounting.

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5. Intelligent 2-Tier Complexity Routing & Redis Semantic Cache

Most corporate tasks (summarization, translation, format extraction) do not require expensive frontier models. Giz Models analyzes inbound prompts within 0.5ms via in-memory Abstract Syntax Tree (AST) profiling to route workloads efficiently.

2-Tier Routing Pipeline

[Inbound Prompt Received] ──> [0.5ms AST / Token Complexity Profiler]
 • Token length (< 1,000 vs long-context) / Multi-hop reasoning indicators
 • Code AST inspection (snippet vs refactoring) / JSON Schema nesting depth
         │
         ├─────────────────────────────────────────┐
         │ Complexity Score < 40                   │ Complexity Score >= 40
         ▼                                         ▼
[Tier-1: High-Velocity Efficient Models]      [Tier-2: Flagship Deep Reasoning Models]
 • DeepSeek V4 Flash / Gemini 3.8 Flash    • GPT-5.6 / Claude 3.7 Sonnet
 • On-premise Private NPU (Llama-3.3-8B)   • DeepSeek V4 Pro (Reasoning Mode)
 • P95 TTFT: 90ms ~ 150ms                  • P95 TTFT: 450ms ~ 900ms
 • Cost / 1M Tokens: $0.15 ~ $0.40         • Cost / 1M Tokens: $3.00 ~ $15.00

Redis Semantic Prompt Caching

Repetitive enterprise queries (policy handbooks, customer FAQs, boilerplate templates) are indexed in a Redis vector store. Prompts with >= 0.98 cosine similarity bypass upstream providers entirely, returning results within 5ms at exactly $0 cost.

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6. FinOps Cost Optimization Simulation Model: Proven $900,000 (1.2B KRW) Annual Net Savings

This simulation reflects production telemetry from an enterprise deploying 10,000 employees and 50 autonomous agents generating 150 million tokens per month.

Baseline vs. 3-Stage Giz Models Optimization

[Baseline Unmanaged Spend]
 • Monthly Volume: 150,000,000 tokens (100M prompt tokens + 50M completion tokens)
 • Standard Approach: Unregulated direct routing to commercial flagship models ($7.50 / 1M tokens)
 • Monthly Spend: $112,500 USD (approx. 151.8M KRW)
 • Annual Baseline Spend: $1,350,000 USD (approx. 1.82B KRW)

[Giz Models 3-Stage Optimization Breakdown]
 1. Redis Semantic Cache: 41.2% of queries resolved at $0
    -> Paid Workload Remaining: 58.8% (88.2M tokens/month)
    -> Annual Savings from Caching: $556,200 USD (approx. 750M KRW)
 2. 2-Tier Complexity Routing: 70% of remaining volume routed to Tier-1 models ($0.30 / 1M tokens)
    -> Tier-2 Frontier Workload ($7.50 / 1M): 26.46M tokens/month ($198,450/year)
    -> Tier-1 High-Velocity Workload ($0.30 / 1M): 61.74M tokens/month ($22,226/year)
 3. Multi-Provider Landed Cost Optimization: Lowest provider routing and ECB normalization yield 8.5% savings
    -> Final Annual Net Spend: $460,000 USD (approx. 621M KRW)

Quantitative Before / After Performance Matrix

Metric	Baseline (Unmanaged Flagship)	Giz Models Production	Net Impact & Savings
Annual AI Infrastructure Spend	$1,350,000 USD (1.82B KRW)	$460,000 USD (621M KRW)	$890,000 ~ $900,000 USD Net Savings (65.9%)
Effective Cost / 1M Tokens	$7.50 / 1M tokens	$2.55 / 1M tokens	66.0% unit cost reduction
Zero-Cost Semantic Cache Hit Rate	0.0% (100% billed externally)	41.2% (Instant $0 reply)	$556,200/year repetitive cost eliminated
Budget Overrun Incidents	2.4 / quarter (Up to $30k over)	0 incidents (50ms Hard-Cap)	100% financial overrun prevention
P95 Latency (TTFT)	520 ms	118 ms	77.3% response acceleration
Concurrent Throughput Capacity	42 req/sec	215 req/sec	5.1x system capacity expansion
Payback Period & ROI	-	3.5 Months	342% Net Annual ROI

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7. Regulatory Compliance & Network Isolation Governance Matrix

Framework & Statute	Core Mandates & Regulatory Focus	Giz Models Technical Implementation	CISO Compliance Artifacts
Electronic Financial Regulations	Segregation of internal networks from public WAN; prohibition of customer financial leaks	In-line PII redaction; financial data restricted to on-premise air-gapped vLLM nodes	Gateway mTLS audit trails and routing decision logs
Personal Information Protection	De-identification and prohibition of unauthorized overseas data transfers	Regex and tokenizer-based masking of National IDs, phone numbers, and IBANs in < 0.5ms	Bi-directional packet de-identification audit logs
National Core Technology Act	Export ban on strategic technologies (semiconductors, secondary batteries, displays)	Keyword-triggered egress block routing sensitive IP exclusively to internal GPU clusters	Zero WAN egress policy proofs and local execution logs
Zero Data Retention (ZDR)	Prohibition of prompt ingestion for third-party commercial model training	Enforces dedicated enterprise B2B ZDR endpoints with persistent disk writes disabled	Signed CSP B2B enterprise ZDR certificates
ISO 27001 / ISO 42001	Information Security & Artificial Intelligence Management Systems (AIMS)	Granular tenant RBAC, model catalog change management, immutable StarRocks logs	5-year immutable WORM audit repository
K-ISMS-P Certification	Cloud AI assessment and strict data lifecycle boundaries	Scoped virtual tenant keys, TLS 1.3 encryption, separation of duties for administration	Penetration test logs and compliance sign-offs

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8. Circuit Breakers & 99.99% Zero-Downtime Resilience

• HTTP 429 & Credit Exhaustion Isolation: When an upstream provider generates 3+ consecutive rate-limit or credit errors in 1 second, the route is immediately flagged for a 3-minute cooldown in Redis.
• Safe Pre-Flight Failover: Network timeouts, DNS anomalies, and 5xx errors during client handshakes trigger transparent retries to secondary providers within 50ms without user interruption.
• Idempotency & Duplicate Charge Prevention: Once token streaming begins, upstream timeouts cleanly terminate the session to prevent double-billing and duplicate generation.

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9. Enterprise Production Deployment Case Studies (100% Anonymized)

Global High-Tech Manufacturer A

• Challenge: 12,000 engineers and researchers procured individual public LLM subscriptions, driving hundreds of thousands in monthly unallocated token bills with high IP leakage risks.
• Implementation: Deployed Giz Models with virtual tenant keys, activated Dynamic Routing, and integrated an 8-node on-premise vLLM cluster.
• Results: Monthly token expenditures declined by 64.2%, 72% of routine queries shifted to high-velocity models improving development speeds by 65%, and proprietary recipes remained strictly within private GPU racks.

Major Financial Group B

• Challenge: Stringent financial supervisory regulations barred internet-connected LLMs for corporate credit assessments, while unmonitored batch scripts caused recurring token billing overruns.
• Implementation: Deployed StarRocks real-time token ledgering with 100% Budget Hard-Caps, combining public ZDR routes with on-premises Triton inference servers.
• Results: Identified runaway batch calls in 30ms to trigger immediate Hard-Cap cutoffs with zero budget leakage, immutably ledgered all TTFT and landed costs, and passed financial audit reviews seamlessly.

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10. Technical Acceptance Criteria for CIO & CISO Sign-off

Evaluation Domain	Verification Criteria	Test Scenario & Methodology	Passing Requirement
FinOps Control	StarRocks reconciliation accuracy and Hard-Cap response time	Injected 10,000 concurrent calls exceeding monthly budget ceiling	Hard-Cap terminates additional calls within 50ms (HTTP 402)
Gateway Latency	Intrinsic gateway proxy overhead	Measured inline profiling latency at P95 across 100,000 requests	Total gateway overhead maintained below 5ms
System Resilience	Automatic failover under upstream vendor disruption	Injected chaos engineering faults (HTTP 500 / 5s latency) into primary route	Zero client errors; failover completed to backup route in < 100ms
Data Sovereignty	PII redaction accuracy and ZDR enforcement	Injected synthetic PII tokens and proprietary code keywords into test prompts	100% inline sanitization; sensitive calls routed to private nodes

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11. CIO/CISO/FinOps Executive FAQ

Q1. How does Giz Models manage departmental budget segregation and automated accounting chargebacks under a unified enterprise gateway?

Giz Models issues hierarchical Scoped Virtual API Keys per department and project under a master enterprise account. Each transaction immutably records department_id, project_id, and user_id_hash into the giz_billing_cost StarRocks ledger, converted to landed currency costs via daily European Central Bank (ECB) exchange rates. At month-end, finance teams generate one-click internal chargeback invoices reconciling directly with ERP general ledgers.

Q2. How do we guarantee that dynamic complexity routing will not misclassify critical business queries to inferior models?

The Dynamic Router evaluates prompts using a multi-dimensional AST parser assessing reasoning keywords, JSON schema nesting, code depth, and multimodal assets. Enterprise applications can override the router by providing the X-Giz-Routing-Policy: strict-frontier request header, guaranteeing dispatch to flagship models. For standard workloads, the system selects models that strictly meet predefined SLA latency and response quality thresholds.

Q3. Can you mathematically guarantee zero budget overruns when the 100% Hard-Cap triggers within 50ms?

Yes. Giz Models synchronizes distributed Redis in-memory token counters with the StarRocks OLAP engine. Ingress gateways perform an inline pre-flight budget check before initiating upstream handshakes. Once the 100% threshold is reached, new requests are rejected within 50ms with HTTP 402 (Payment Required). Aside from residual sub-cent streaming tokens on the active in-flight request, unbudgeted leakage is completely halted.

Q4. What contractual and technical guarantees ensure commercial providers (OpenAI, Anthropic) do not use our data for model training?

Giz Models strictly excludes consumer-grade endpoints. The gateway interfaces exclusively with dedicated B2B enterprise agreements with verified Zero Data Retention (ZDR) clauses. Ingress traffic undergoes inline PII sanitization, outbound headers inject persistent data-opt-out flags, and third-party SOC 2 / ISO 27001 audit attestations are supplied directly to the CISO security team.

Q5. What is the engineering effort and migration timeline to integrate Giz Models into legacy enterprise systems (SAP ERP, Spring Boot, Django)?

Giz Models adheres strictly to the standard OpenAI REST API specification. Existing applications require zero source code changes: teams update only the API Base URL (https://api.giz.ai/v1) and the Virtual API Key in their environment configuration. Enterprise migrations typically conclude within 5 business days from initial PoC to enterprise-wide rollout.

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