Run Your AI Models in Production
— Not Just in a Notebook
Training a model is 10% of the work. Deploying it reliably — with proper GPU management, latency optimization, cost controls, and monitoring — is the other 90%. QuickHire AI infrastructure engineers handle the production layer.
4hr/$100 · Sprint Pack 10 days/$1,700 · No lock-in
AI Infrastructure Capabilities
Production-grade AI infrastructure from GPU clusters to cost-optimized serving pipelines.
Who Books AI Infrastructure
AI Product Lead
"Models are in dev — cannot get them to production"
Your ML team trained a great model. But productionizing it — inference endpoints, latency budgets, monitoring, cost controls — is a different skill set. QuickHire bridges the gap from notebook to production.
ML Engineer
"Model serving bottlenecks at scale"
Throughput is maxed, latency is spiking, and you do not know whether to scale GPU instances, tune batching, or switch to quantized models. Our infrastructure engineers diagnose and fix serving bottlenecks fast.
CTO
"AI infrastructure costs are spinning out of control"
Your monthly GPU bill just tripled. Token costs for your LLM features have no ceiling. QuickHire implements cost controls, caching layers, and rightsizing that typically cut AI infrastructure spend by 40–60%.
The AI Infrastructure Gap
Most ML teams can train a model. Very few can run it in production at scale without burning through GPU budget.
Model Trained ≠ Production Ready
A Jupyter notebook model is not a production service. Inference latency, error handling, monitoring, auto-scaling, and cost controls are all engineering work that happens after training.
GPU Management Is Specialized
GPU memory, CUDA optimization, batching strategies, and quantization require specialized knowledge. A developer who can't configure vLLM will pay 4× more than necessary for the same throughput.
Cost Blowouts Are Common
Without token budget controls, rate limiting, and cost monitoring, AI infrastructure costs can 10× overnight. Most teams discover this after the bill arrives, not before.
What We Deploy
LLM API Endpoints
Production REST APIs wrapping managed LLM APIs (OpenAI, Anthropic, Google) — with rate limiting, caching, fallback routing between providers, and usage tracking.
Self-Hosted Model Serving
vLLM and TGI for open-source models (Llama 3, Mistral, Mixtral, custom fine-tuned) on GPU clusters — optimized for throughput, latency, and cost.
RAG Pipeline Infrastructure
Vector database deployment (Pinecone, Weaviate, pgvector at scale), embedding service endpoints, retrieval APIs, and the full RAG serving stack.
AI Data Pipelines
ETL pipelines feeding vector stores and fine-tuning datasets, document ingestion pipelines, and knowledge base refresh automation — reliable and monitored.
AI Infrastructure Stack
Monitoring AI in Production
Latency Tracking
p50, p95, p99 inference latency by model, endpoint, and request type. Alert thresholds matched to your SLA commitments.
Token Usage & Cost
Real-time token consumption by model, by user, by feature. Budget alerts and automatic rate limiting before costs escalate.
Drift Detection
Monitoring output distributions for semantic shift over time — detecting when your model's behavior changes without explicit model updates.
Pricing
Simple, Transparent Pricing
Every session includes a vetted expert + dedicated PM. Cancel anytime.
India · INR
GST Invoice · GST included
Starter
Best for first timers & quick tasks
/ session
GST included
- 1 vetted expert
- Dedicated PM included
- Cancel after session
- Tax-compliant invoice
Full Day
Most chosen for serious delivery
/ session
GST included
- 1 vetted expert
- Dedicated PM included
- Daily progress report
- Priority assignment
- Tax-compliant invoice
Available in 14 countries · Other currencies available at checkout
FAQ
Frequently Asked Questions
We manage the full AI production stack: GPU cluster provisioning and management on AWS (p3/p4 instances, A10G, A100), GCP (A100/H100 nodes), or Azure (NDv4 series), self-hosted model serving via vLLM and TGI for open-source LLMs, RAG pipeline infrastructure (vector databases, embedding services, retrieval APIs), LLM API proxy layers with caching (Redis) and rate limiting, AI monitoring dashboards, and cost optimization for both managed API and self-hosted workloads.
AI model deployment has unique challenges: GPU memory management (models require specific GPU VRAM allocations and can't be arbitrarily scaled like CPU workloads), inference optimization (quantization, batching, KV cache management affect latency and throughput dramatically), cost unpredictability (token-based pricing for managed APIs vs. GPU rental for self-hosted), and monitoring complexity (tracking not just uptime but model accuracy, drift, and output quality over time).
Managed APIs for most applications: faster to ship, no GPU management overhead, always-current models, and often cheaper at moderate scale. Self-hosted models for: privacy requirements (no data leaving your infrastructure), cost optimization at high volume (>10M tokens/day), latency requirements (<100ms), custom fine-tuned models, or regulatory compliance (HIPAA, financial data). We help you model the cost crossover point for your specific usage.
vLLM is an open-source inference serving library that uses PagedAttention to dramatically improve GPU memory efficiency for large language model serving — typically 2–4× more throughput than naive implementations. We use vLLM when self-hosting models like Llama 3, Mistral, or custom fine-tuned models for high-throughput production workloads. TGI (Text Generation Inference by Hugging Face) is the alternative for Hugging Face model ecosystem integrations.
GPU cost management includes: right-sizing GPU instances (A10G for smaller models, A100/H100 for large models), spot/preemptible instances for batch inference workloads (60–80% cheaper), auto-scaling based on queue depth, model quantization (INT8/INT4) to reduce memory requirements and fit larger batch sizes, and scheduled scaling (scale to zero during off-hours for non-critical workloads). We instrument cost per inference and alert when it exceeds thresholds.
AI monitoring goes beyond uptime. We track: latency (p50, p95, p99 for inference time), throughput (requests/second, tokens/second), token usage and cost per request, semantic drift detection (comparing output distributions over time), error rates by error type, and user feedback signals. Tools: LangSmith, Helicone, Langfuse, and custom Prometheus metrics with Grafana dashboards tailored for LLM workloads.
Move Your AI from Notebook to Production
AI infrastructure engineer + PM in 10 minutes. LLM deployment, RAG infrastructure, and AI monitoring — production-grade from day 1.
Deploy My AI Infrastructure →4hr/$100 · Sprint Pack 10 days/$1,700 · Cancel anytime
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