The Economics of Enterprise AI: Reducing TCO
An analytical breakdown showing how migrating from SaaS subscriptions to self-hosted models drastically reduces Total Cost of Ownership.
We deploy Private LLMs directly in your infrastructure.
Reduce hallucinations with proper data grounding.
Keep sensitive data under your control and cut API costs by 40-70%.
Zero data egress to third-party APIs.
Your CISO or Legal team blocks public APIs. You can’t send customer data, financial records, or proprietary code outside your network due to GDPR, HIPAA, or FINMA requirements.
THE FIX
We build Private LLMs inside your VPC. Data stays in your infrastructure. Compliance requirements become achievable.
You successfully built a PoC using SaaS AI. But as you scale to production, the pay-per-token pricing model destroys your IT budget and unpredictable API costs kill your product margins.
THE FIX
We migrate workloads to self-hosted models. Move from unpredictable API OPEX to predictable infrastructure costs.
Most enterprise RAGs hallucinate due to naive retrieval and dirty data, while exposing your IP to public APIs. We build deterministic, self-hosted pipelines that ground every response strictly in your validated, internal documents.
Data stays in your VPC
Full Audit Trails for Every Query
# Query stays inside VPC boundary
from vector_store import LocalChromaDB
def query_internal_docs(question: str) -> str:
db = LocalChromaDB(
host="10.0.1.15", # Private subnet only
ssl_verify=True
)
return db.similarity_search(
query=question,
k=5
)
Stop being locked into OpenAI, Azure OpenAI, or AWS Bedrock. We decouple your application layer from proprietary APIs and implement a vendor-agnostic architecture.
Self-hosted models inside your VPC (AWS, Azure, or on-premise)
We don’t just write prompts. We build production-ready systems that pass compliance checks.
No public APIs. We deploy self-hosted models (Llama, Mistral) in your private subnet with restricted outbound traffic. Your data, your infrastructure.
We build access controls directly into the Vector DB. The LLM only sees the context your employee is authorized to access.
We add a Guardrail layer in front of the Orchestrator to mask sensitive data before it hits the prompt.
When questions arise, auditors need answers. We implement SIEM tracing with PII-masked logs. Complete traceability without exposing sensitive data.
Beyond the prompt. Why enterprise CTOs choose Scalac’s Engineering DNA.
Proven Track Record in Regulated Data
FINTECH / PAYROLL
Challenge: Payroll data requires GDPR compliance with full data sovereignty.
Solution: Team extension with 15 senior engineers, Google Cloud infrastructure migration, security hardening.
Full data ownership in regulated environment.
Scalac.ai is the specialized AI division of Scalac, bringing true software engineering rigor to the world of Artificial Intelligence.
Backed by 10+ years of delivering bulletproof backends, data pipelines, and secure cloud architectures for global finance, healthcare, and enterprise clients.
PROJECTS DELIVERED
No. You don’t need a massive H100 cluster just for inference. We deploy heavily optimized, quantized models (using vLLM or TensorRT-LLM) that run blazing fast on cost-effective, readily available GPUs (like L40S or A10G instances). The result? Predictable, fixed OpEx that scales logically, instead of an unpredictable „pay-per-token” cloud tax that punishes you for growing.
An analytical breakdown showing how migrating from SaaS subscriptions to self-hosted models drastically reduces Total Cost of Ownership.
What is it, and why is your OpenAI API a technical debt that’s only beginning to grow?