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AI Service
AI
Infrastructure

Model Hosting & Scaling

Multi-cloud and on-prem model serving, autoscaling, batching, quantization, and cost-optimized inference.

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Deliverables

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Outcomes

SLA

Production Ready

Model Hosting & Scaling
Overview

Multi-cloud and on-prem model serving at scale.

Multi-cloud and on-prem model serving, autoscaling, batching, quantization, and cost-optimized inference.

Deliverables

What you get

Multi-cloud and on-prem model serving at scale.

01

Model serving infrastructure

02

Autoscaling

03

Batching & quantization

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Cost optimization

Common Challenges

Problems we help you overcome

01

Inference latency spikes under load

Models cannot handle traffic bursts without manual intervention or over-provisioned infrastructure.

02

High GPU/compute costs

Unoptimized serving configurations waste compute resources on every inference request.

03

Single-cloud lock-in

Deployment is tied to one provider with no portability or failover strategy.

Key Capabilities

What we bring to the table

Autoscaling inference

Dynamic scaling based on queue depth, latency SLOs, and cost budgets.

Quantization & batching

INT8/FP16 quantization and dynamic batching to maximize throughput per GPU.

Multi-cloud serving

Portable serving stacks on Kubernetes with cloud-agnostic deployment patterns.

Industries

Industries We Serve

Healthcare & Life Sciences

Clinical NLP, coding automation, triage assistants (HIPAA-ready).

Financial Services

Fraud detection, automated underwriting, compliance monitoring.

Legal & Compliance

Contract review, e-discovery, regulatory tracking.

Retail & E-commerce

Personalization, search, conversational commerce.

Manufacturing & Industrial

Predictive maintenance, CV inspection, supply-chain optimization.

Telecom & Edge

Customer automation, low-latency on-device inference.

Cybersecurity

Threat detection, SOC automation.

Public Sector & Energy

Document automation, forecasting, citizen services.

Engagements

Pricing & Engagements

Discovery & Assessment

Fixed-fee 1–2 week assessment with roadmap.

POC-to-Pilot

Fixed-scope 2–6 week POC, includes data prep, prototype model, and success criteria.

Production & Managed Services

Subscription for hosting, monitoring, retraining, and support (SLA options).

Professional Services

Time-and-materials or outcome-based pricing for custom work.

Outcomes

Measurable impact

Measurable business impact from this engagement.

Lower inference costs

Higher throughput

Reliable scaling

FAQ

Frequently asked questions

What serving frameworks do you use?

Triton, vLLM, TorchServe, TensorFlow Serving, and custom FastAPI/gRPC endpoints depending on model type.

Can you reduce our current inference bill?

We typically find 25–50% savings through quantization, batching, model routing, and right-sizing infrastructure.

Do you support on-prem GPU clusters?

Yes. We deploy and optimize serving on NVIDIA DGX, bare-metal GPU servers, and private cloud environments.

Proof

Case Study

Problem

A regulated enterprise needed domain-accurate LLM responses without exposing sensitive data to public APIs.

Solution

LLM Customization & RAG, MLOps & ModelOps, Responsible AI & Governance

Outcome

40% reduction in human review time, 99.2% factual accuracy on domain tasks, and predictable inference costs within 90 days.

Contact us for the full case study
Get Started

Ready to deploy with confidence?

Multi-cloud and on-prem model serving, autoscaling, batching, quantization, and cost-optimized inference.

Get a free consultation

Book a free 30-minute consultation to define a POC and estimate impact.

Why Choose Us

  • Industry focus + measurable outcomes: domain models with validated ROI metrics.
  • POC-to-production playbook: repeatable 2–6 week POC that moves to production fast.
  • SLA-backed production support: uptime, latency, and retraining SLAs.
  • Compliance-first: HIPAA/GDPR/PCI-ready architectures and audited pipelines.