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Deployment

Trained model to
live endpoint.
One click.

Deploy to cloud, VPC, or on-premises without a DevOps team. Built-in quantisation, auto-scaling endpoints, 34ms avg latency, 99.98% uptime SLA.

The deployment bottleneck that keeps AI in the lab

One click takes your trained model to a live API endpoint - cloud, private VPC, or on-premises - with built-in INT8/INT4 quantisation, auto-scaling, and 34ms average latency. No Dockerfiles, no Helm charts, no DevOps team.

99.98%
Uptime SLA across all managed deployment targets
34ms
Average inference latency on quantized endpoints
4x
Cost reduction achieved through built-in quantization

How Deployment works

01

Select your deployment target

Choose from three deployment modes: managed cloud (QpiAI PRO handles all infrastructure), private VPC (your cloud account, our management layer), or on-premises (your data centre, air-gapped if required). Each option gives you the same one-click experience regardless of where the model runs.

02

Platform applies quantization and configures auto-scaling

QpiAI PRO automatically applies the optimal quantization scheme - INT8 or INT4 - for your latency and accuracy requirements. Auto-scaling rules are configured based on your traffic pattern. Endpoint URLs, API keys, and authentication are provisioned automatically. You never touch a config file.

03

Monitor live performance from the same dashboard

Once deployed, every endpoint is visible in the unified monitoring dashboard: real-time latency (p50 and p99), requests per second, error rates, and scaling events. If performance degrades, the platform alerts you before users notice. No separate observability stack to configure or maintain.

Key capabilities

One-Click Deployment

From trained model to live API endpoint in a single click. No Dockerfile, no Helm chart, no cloud console. Any team member can deploy without DevOps involvement.

Built-In Quantization (4x Cost Saving)

Automatic INT8 and INT4 quantization reduces model size and compute cost by up to 4x - with negligible impact on output quality for most production tasks. No manual optimisation needed.

Auto-Scaling Inference Endpoints

Traffic-responsive scaling adds and removes inference replicas automatically. Handle 10x traffic spikes without pre-provisioning - and scale back to zero when traffic drops to eliminate idle costs.

Multi-Target: Cloud / VPC / On-Premises

A single workflow covers managed cloud, private VPC, and on-premises data centres. Choose the deployment target that meets your data sovereignty, compliance, and latency requirements.

Real-Time Performance Monitoring

Latency, throughput, and error rate dashboards update in real time for every deployed endpoint. Drill into p99 latency spikes and trace them to specific model versions or traffic segments.

99.98% Uptime SLA

Enterprise-grade reliability backed by a contractual SLA. Multi-region failover, automated health checks, and incident response ensure your AI application stays online when it matters most.

Continue the workflow

Your model, live in production, in one click.

Start deploying today. No credit card, no DevOps team, no infrastructure expertise required.

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