How does Acadify guarantee zero data egress outside our corporate VPC?

We deploy all model clusters (vLLM on Kubernetes) and proprietary vector stores directly inside private subnets within your AWS VPC, Azure VNet, or GCP Project. Ingress and egress occur strictly via AWS PrivateLink or internal VPC peering. No public IP routing is enabled.

How do you handle tenant-level security and document permissions in RAG?

We apply Access Control Lists (ACLs) directly at the vector query layer. In pgvector and Qdrant, each embedded chunk inherits security metadata and the search engine filters by authenticated user permissions prior to semantic vector calculation.

What is your approach to model quantization versus full-precision serving?

For high-velocity workloads, we benchmark AWQ and FP8 formats against FP16 baselines. In production tests across 70B models, FP8/AWQ retain >99.2% accuracy while reducing GPU memory by 50% and doubling concurrency throughput.

Can we sign a Business Associate Agreement (BAA) for HIPAA compliance?

Yes. Because deployments execute inside your dedicated cloud environment, all processing is governed under your existing cloud provider BAA (AWS, Azure, or GCP). Acadify provisions encrypted KMS volumes, TLS 1.3 in transit, and in-line PII redaction.

SOC 2 TYPE II • ZERO DATA RETENTION • VPC ENCLAVES

Private Enterprise AI &
Production LLM Infrastructure

Deploy and scale generative AI workloads directly inside your AWS, Azure, or Google Cloud environment. Private vLLM clusters, production RAG pipelines, and automated safety controls—without leaking sensitive customer data or getting locked into public API vendors.

Schedule Architecture Review Explore Architecture

Need enterprise procurement or custom project scoping? Speak with Enterprise Sales.

AWS PrivateLink & Azure VNet

vLLM & TensorRT-LLM Serving

99.95% Availability SLA

acadify-gateway ~ us-east-1-prod (VPC Enclave)

LIVE INGESTION

Time to First Token (TTFT)

14.2 ms (Streaming)

Inference Throughput

1,640 tok/sec

Semantic Cache Hit Rate

48.2% (0-Token Cost)

Data Egress Outside VPC

0.00 KB (Air-Gapped)

23:10:37.666 INFERENCE vLLM worker-04: FP8 KV-Cache load at 41% • TTFT 14.8ms

23:10:41.166 AUDIT SOC 2 Enclave Log written to S3 Glacier Vault (Encrypted KMS)

23:10:44.666 ROUTER Tenant Health Check: 12 nodes healthy • Zero packet loss

23:10:48.165 GATEWAY POST /v1/chat/completions (Batch: 32 tokens, Stream: true)

23:10:51.666 GUARDRAIL Presidio Filter: 0 violations detected (Latency: 1.4ms)

23:10:55.165 CACHE Semantic Cache Miss → Routing to private vLLM pod cluster

23:10:58.665 INFERENCE vLLM worker-04: FP8 KV-Cache load at 41% • TTFT 14.8ms

23:11:02.165 AUDIT SOC 2 Enclave Log written to S3 Glacier Vault (Encrypted KMS)

< 180ms

Avg. Streaming TTFT

Sub-200ms first-token latency on dedicated GPU instances with continuous batching.

65%

Inference Cost Savings

Continuous batching, FP8 quantization, and vector semantic caching cut compute spend.

100%

VPC Data Isolation

Zero prompts, embeddings, or fine-tuning datasets leave your private cloud perimeter.

99.95%

Production SLA

High-availability Kubernetes clusters with automated health checks and failovers.

Architecture Pipeline

How Enterprise AI Operates in Production

From client application request to private GPU inference—every layer is hardened, audited, and isolated inside your cloud boundary.

Stage 1

API Gateway & Client Ingress

TLS 1.3 / mTLS Rate Limiting Tenant Auth

Inbound requests enter through an internal ingress controller protected by TLS 1.3 encryption, mTLS service authentication, and strict per-tenant rate limiters to prevent resource exhaustion and unauthorized access.

Stage 2

PII Sanitization & Prompt Firewalls

Presidio Masking Injection Defense Zero Retention

In-flight inspection strips out customer identifiers (credit card numbers, SSNs, personal contact info) and scans for prompt-injection attacks before any prompt reaches the model execution boundary.

Stage 3

Context Retrieval & Semantic Caching

Semantic Cache (<15ms) pgvector / Qdrant Document ACLs

Identical semantic queries return in under 15ms directly from vector cache with zero GPU spend. For new prompts, hybrid search queries both dense vector embeddings (pgvector/Qdrant) and BM25 keyword indices filtered by authenticated user permissions.

Stage 4

Private Model Serving on Dedicated GPUs

vLLM / TensorRT-LLM PagedAttention FP8 Precision Karpenter

Inference executes on managed Kubernetes GPU clusters (AWS EKS or Azure AKS) using vLLM or TensorRT-LLM with PagedAttention, continuous batching, and FP8 precision for fast token generation and optimal GPU memory utilization.

Stage 5

Observability, Tracing & Cost Tracking

Langfuse Tracing OpenTelemetry Cost Attribution

Full request traces, token latency metrics, and GPU compute costs are piped to Langfuse and OpenTelemetry, providing engineering and finance teams with transparent, real-time cost attribution across internal departments.

Core Capabilities

Enterprise Deployment Capabilities

Everything required to run mission-critical AI workloads in production, designed around your security and infrastructure requirements.

Private Cloud & VPC Isolation

Deploy LLMs directly in your AWS, Azure, or GCP private subnets using AWS PrivateLink or internal VPC peering. No public endpoints, and zero customer data leaves your cloud perimeter. Explore our FinTech compliance architecture case study for private-tenant document parsing workflows.

AWS PrivateLink Azure ExpressRoute Private Subnets

High-Throughput Model Serving

Run open-source weights (Llama 3, DeepSeek, Mistral) on dedicated GPU nodes with vLLM. Continuous batching and FP8 quantization cut hardware costs by 50%+ while maintaining sub-200ms latency.

vLLM 0.6+ TensorRT-LLM FP8 / AWQ

Hybrid RAG & Knowledge Retrieval

Beyond simple vector search: we build hybrid RAG pipelines integrating dense embeddings, BM25 keyword matching, and cross-encoder re-ranking with strict user-level document permissions.

pgvector Qdrant Cohere Rerank

Smart Model Routing & Fallbacks

Route routine customer queries to lightweight local models and escalate high-complexity prompts to larger models, with automatic failovers if an API experiences degraded performance.

Claude 3.5 Sonnet GPT-4o Auto-Failover

Data Masking & Prompt Firewalls

Inspect incoming requests in real-time to redact customer PII (credit card numbers, SSNs, API keys) and block prompt-injection attacks before tokens ever hit the model context window.

Presidio PII NeMo Guardrails Injection Filter

Observability & Cost Management

Track token usage, latency breakdowns, and cloud compute expenses by team or application using Langfuse and OpenTelemetry dashboards, giving engineering and finance full transparency.

Langfuse OpenTelemetry Cost Tracking

Infrastructure as Code

Repeatable, Auditable Infrastructure

We don't manage AI clusters through manual cloud console clicks. Every VPC endpoint, Kubernetes pod, and gateway configuration is defined in code, making deployments auditable, reproducible, and ready for your existing CI/CD pipelines.

Terraform blueprints Standardized modules for AWS, Azure, and Google Cloud private VPC setups.
Kubernetes Helm charts Automated vLLM serving deployments with GPU node autoscaling via Karpenter.
Custom gateway router Microsecond PII redaction, vector semantic caching, and model routing.

Request Architecture Blueprint

# AWS Private AI VPC Enclave with GPU Node Group
module "ai_vpc" {
  source  = "terraform-aws-modules/vpc/aws"
  name    = "acadify-ai-prod-enclave"
  cidr    = "10.50.0.0/16"

  azs             = ["us-east-1a", "us-east-1b"]
  private_subnets = ["10.50.1.0/24", "10.50.2.0/24"]
  enable_nat_gateway = false # Air-gapped: no direct public route
}

resource "aws_vpc_endpoint" "bedrock_privatelink" {
  vpc_id            = module.ai_vpc.vpc_id
  service_name      = "com.amazonaws.us-east-1.bedrock-runtime"
  vpc_endpoint_type = "Interface"
  subnet_ids        = module.ai_vpc.private_subnets
  private_dns_enabled = true
}

# Kubernetes vLLM High-Throughput Serving Manifest
apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-llama3-70b-service
spec:
  replicas: 2
  template:
    spec:
      containers:
      - name: vllm-server
        image: vllm/vllm-openai:v0.6.2
        args:
          - "--model", "meta-llama/Llama-3.3-70B-Instruct"
          - "--tensor-parallel-size", "4"
          - "--quantization", "fp8"
          - "--max-model-len", "16384"
          - "--enforce-eager"
        resources:
          limits:
            nvidia.com/gpu: "4"

# Async Gateway with Semantic Cache & PII Scrubbing
from fastapi import FastAPI, Request
from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer import AnonymizerEngine

app = FastAPI()
analyzer = AnalyzerEngine()
anonymizer = AnonymizerEngine()

@app.post("/v1/secure-chat")
async def route_prompt(payload: dict):
    # 1. PII Redaction
    results = analyzer.analyze(text=payload["prompt"], language="en")
    clean_prompt = anonymizer.anonymize(text=payload["prompt"], analyzer_results=results).text
    
    # 2. Check Vectorized Semantic Cache
    cached = await check_semantic_cache(clean_prompt, threshold=0.95)
    if cached:
        return {"response": cached, "cost": 0, "cached": True}
        
    # 3. Serve via private vLLM or Bedrock PrivateLink
    return await dispatch_inference(clean_prompt)

Deployment Comparison

Deployment Options: Hosted APIs vs. DIY vs. Acadify

A practical breakdown of data privacy, latency, hardware utilization, and ongoing maintenance trade-offs.

Swipe horizontally to compare all 4 options

Technical Dimension	Commercial API Wrapper	In-House DIY Setup	Acadify Architecture
Data Privacy Boundary	Public Internet Transmission	Variable by Project	100% Inside Your VPC
First-Token Latency (TTFT)	1,200ms – 3,000ms	400ms – 900ms	< 180ms Streaming
Sensitive Data & PII	Sent Directly to Vendor	Basic regex scripts	Automated In-Flight Masking
Hardware Cost Efficiency	High retail token pricing	Idle GPU cluster waste	65% Savings (PagedAttention)
Repeat Query Handling	Re-billed Every Request	Exact string match only	Semantic Cache (0-Token Cost)
Compliance Readiness	Dependent on vendor TOS	Manual audit preparation	SOC 2 / HIPAA BAA Ready
Fallback & Cascades	Single Vendor Dependency	Manual switchover code	Automated Model Cascade
RAG Document Security	Global document access	Flat search indices	User-Level Document ACLs

Supported Stack

Supported Technologies & Frameworks

We integrate with your existing cloud providers, runtimes, and databases rather than forcing a proprietary platform.

Serving & Runtimes

vLLM 0.6+

TensorRT-LLM

Triton Inference Server

AWQ & FP8 Quantization

Cloud Infrastructure

AWS Bedrock & EKS

Azure OpenAI & AKS

GCP Vertex AI & GKE

Terraform & Helm

Data & Vector Stores

PostgreSQL + pgvector

Qdrant Vector Engine

Neo4j & GraphRAG

Cohere Rerank Model

Security & Governance

NVIDIA NeMo Guardrails

Microsoft Presidio PII

Langfuse Tracing

OpenTelemetry

Frequently Asked Questions

Technical & Deployment FAQ

Clear answers to common questions about cloud boundaries, GPU cold-starts, and compliance.

We deploy model serving runtimes (such as vLLM on Kubernetes) and vector databases directly inside your own private cloud VPC (AWS, Azure, or GCP). Communication occurs through internal subnets and private endpoints like AWS PrivateLink. Your prompts, embeddings, and outputs never pass through public vendor logging or third-party servers.

We combine three techniques: first, vLLM's PagedAttention handles sudden concurrent bursts in memory without queuing crashes. Second, warm-standby autoscaling via Kubernetes Karpenter provisions fresh GPU pods in under 45 seconds. Third, our router can temporarily cascade non-sensitive overflow traffic to private managed cloud endpoints during unprecedented spikes.

We apply row-level security metadata to each document chunk in pgvector or Qdrant. When a user submits a query, the search engine enforces filters based on the user's verified role and access permissions before retrieving relevant context, ensuring users never see data from departments or tiers they aren't authorized to access.

For production workloads on modern hardware (such as NVIDIA H100 or L4 GPUs), we typically recommend FP8 or AWQ quantization. Benchmarks on 70B parameter models show FP8 maintains over 99% of original FP16 accuracy while halving the GPU memory footprint, allowing you to serve larger models on smaller, more cost-effective node pools.

Yes. Because your deployment operates inside your own dedicated cloud account, your existing BAA with AWS, Azure, or Google Cloud covers the underlying compute and storage. We configure KMS volume encryption at rest, TLS 1.3 in transit, and in-line PII redaction to satisfy strict compliance audits.

Private Enterprise AI & Production LLM Infrastructure