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AI Cloud Infrastructure for Production Systems
Bitontree builds and runs the cloud infrastructure behind production AI: scalable, secure deployment on AWS, Azure, and GCP, with MLOps, observability, and cost control. We operate it after launch, not just hand it over.
What Production AI Needs From Cloud Infrastructure
Production AI runs on infrastructure that looks different from a normal web app. The compute is spikier, the cost is higher, and a small config mistake can quietly burn a large bill or leak data. That is the part we own.
We build and run the cloud foundation that AI systems depend on: hosting for models and inference, autoscaling for agent workloads, pipelines that deploy and version models safely, and the observability and cost controls that keep all of it accountable. We work across AWS, Azure, and GCP, and we pick per project rather than by habit.
You need real AI infrastructure when:
- You self-host models or run GPU inference and need it to scale and stay affordable
- Agent workloads spike unpredictably and must autoscale without falling over
- You need MLOps: repeatable deploys, versioning, and rollback for models and prompts
- A vector database and data stores have to run reliably alongside your app
- Security, data residency, or cost control are board-level concerns
Managed model APIs are enough when your volume is modest and latency and data residency are not constraints. We will tell you when a managed API beats running your own infrastructure, because paying for complexity you do not need is its own kind of waste.
What We Build and Run
The cloud layer production AI depends on.
Model & Inference Hosting
Deployment for self-hosted or managed models, with GPU scheduling where it is needed and sensible fallbacks when a provider is down.
Autoscaling Agent Workloads
Scaling that follows real demand, so spiky agent traffic stays responsive without paying for idle capacity.
MLOps Pipelines
CI/CD for models and prompts: versioning, staged rollout, and rollback, so a change is safe and reversible.
Observability & Tracing
Metrics, logs, and traces across models, agents, and services, so you can see latency, errors, and what each run actually did.
Cost Control & Right-Sizing
Budgets, alerts, and right-sized compute, so AI spend stays visible and predictable instead of arriving as a surprise.
Secure Networking & Secrets
Private endpoints, network isolation, and managed secrets, so models and data are not exposed by default.
Reference Architecture for Production AI on the Cloud
A production AI platform is a set of layers, each with its own scaling and failure behavior. Here is the shape we build toward, then adapt to your cloud and constraints.
- API gateway and app layer handle auth, routing, and rate limiting before any request reaches a model.
- Agent and inference services run the AI logic and scale independently from the rest of the app.
- Model endpoints are either managed provider APIs or self-hosted GPU services, chosen by cost, latency, and data rules.
- Vector DB and data stores hold retrieval data and application state, sized for the query load they actually see.
- Queues and workers absorb spikes and run long jobs off the request path, so user-facing latency stays low.
- Observability and CI/CD sit across everything: tracing for what happened, and infrastructure as code so the whole platform is reproducible and reviewable.
How We Deploy and Operate AI Infrastructure
From assessment to infrastructure we run for you.
Assess Workload & Cloud
We profile the workload (latency, volume, data rules) and choose the cloud and services that fit, not the ones we default to.
Infrastructure as Code
Everything is defined in Terraform, so the platform is reproducible, reviewable, and not trapped in someone's console clicks.
Containerization
Services are containerized for consistent deploys across environments and clean scaling.
CI/CD for Models
Pipelines that build, test, and roll out models and prompts with versioning and a clear rollback path.
Autoscaling & Load
Scaling policies and load testing, so the platform holds up when agent traffic spikes.
Observability & Alerting
Dashboards and alerts for latency, errors, and saturation, so problems surface before users feel them.
Cost Monitoring & Ops
Budgets, anomaly alerts, and ongoing right-sizing. After launch we keep operating and tuning the platform.
AWS vs Azure vs GCP for Production AI
A clearer view of where each cloud tends to fit for production AI workloads, from managed models to data residency and operations.
| Decision point | AWS | Azure | GCP |
|---|---|---|---|
| Managed AI services | Bedrock, SageMaker, and a broad AI service catalog | Azure OpenAI, AI Foundry, and Microsoft ecosystem fit | Vertex AI, Gemini, and strong data/ML tooling |
| GPU and inference options | Mature GPU options and broad regional coverage | Strong GPU options and OpenAI-aligned model access | GPU and TPU options for model-heavy workloads |
| Data and vector layer | OpenSearch, RDS, S3, and pgvector paths | Azure AI Search, Postgres, Blob, and Fabric integrations | BigQuery, AlloyDB, Cloud Storage, and pgvector patterns |
| Security posture | Strong IAM, VPC, KMS, and enterprise controls | Strong enterprise identity, compliance, and Microsoft-native governance | Strong data governance, IAM, and analytics-first controls |
| Best fit | Teams already deep in AWS or needing broad infrastructure flexibility | Microsoft-heavy organizations and regulated enterprise AI rollouts | Data-heavy teams, analytics workloads, and Vertex AI-centered stacks |
AI Infrastructure Use Cases by Industry
Where we run production AI platforms.
Healthcare
Compliant hosting for clinical AI, with data residency and audit logging.
SaaS
Multi-tenant AI infrastructure that scales with your customer base.
Logistics
Resilient platforms for real-time, event-heavy AI workloads.
Legal
Private, access-controlled hosting for sensitive document AI.
Ecommerce
Autoscaling infrastructure for search, recommendation, and support AI under traffic spikes.
Manufacturing
Hybrid and edge-aware deployment for operational AI.
Production Patterns We've Shipped
Real builds whose deployment and operations patterns we reuse for AI infrastructure. Not every one is a pure infrastructure project; they are the production patterns this platform work is built from.
Timeline & Engagement
How an embedded AI infrastructure engagement runs, from workload assessment to production operations.
Step 1: Discovery & Assessment (1-3 weeks)
We map workloads, cloud constraints, security needs, traffic patterns, and cost targets. Deliverables: infrastructure scope, cloud recommendation, deployment plan, and operational success metrics.
Workload mapping
Cloud fit assessment
Security requirements
Cost targets
Deployment plan
Step 2: First Environment (3-6 weeks)
We stand up the first production-shaped environment with infrastructure as code, networking, secrets, CI/CD, and observability. You get a working foundation ready for internal workloads.
Infrastructure as code
Network design
Secrets management
CI/CD setup
Observability baseline
Step 3: Production Deployment (6-12+ weeks)
We harden deployment, autoscaling, tracing, alerting, rollback paths, and cost controls, then move the AI workload into production with operational dashboards.
Autoscaling
Tracing and alerts
Rollback paths
Cost controls
Production launch
Step 4: Continuous Operations (ongoing)
We stay embedded to monitor reliability, cost, latency, security posture, and workload growth so the platform keeps improving after launch.
Reliability monitoring
Cost reviews
Latency tuning
Security reviews
Capacity planning
Security & Governance for AI Infrastructure
The controls that keep an AI platform safe and accountable.
Secrets & Key Management
Keys and credentials live in a managed secrets store, scoped per environment and rotated, never baked into images or code.
Network Isolation & Private Endpoints
Models and data sit behind private networking, so they are not reachable from the public internet by default.
Data Residency & PII
We can keep data in a required region and build HIPAA-aware handling. We are not a certified entity ourselves; we build to your compliance requirements.
Access Control & Audit Logging
Least-privilege roles and audit logs across the platform, so access is limited and every action is traceable.
Cost Guardrails
Budgets and alerts on the spend-heavy parts (GPU, inference, egress), so cost stays controlled instead of surprising you at month end.
Frequently Asked Questions
Which cloud should we use for AI?
Usually the one where your data and team already live, adjusted for the models and tooling you need. AWS, Azure, and GCP can all run production AI well; we choose with you against real constraints.
Should we self-host models or use managed APIs?
Managed APIs are simpler and often cheaper at modest volume. Self-hosting makes sense when you need control over cost at scale, latency, or data residency. We help you decide rather than defaulting to one.
How do you control AI and GPU costs?
Right-sized compute, autoscaling that follows real demand, budgets and anomaly alerts, and ongoing review of the spend-heavy parts like GPU, inference, and egress.
How do you deploy and version models?
With MLOps pipelines: versioning, staged rollout, and rollback for both models and prompts, so a change is safe and reversible.
Is our data secure and isolated?
Models and data sit behind private networking with least-privilege access and audit logging. We can keep data in a required region and build HIPAA-aware handling, built to your compliance requirements.
Can you deploy on-prem or in a private cloud?
Yes. We can target a private cloud or on-prem environment when data residency or control requirements call for it.
How long does it take to set up?
A first working environment usually lands in 3 to 6 weeks. Full production deployment typically runs 6 to 12 weeks or more, depending on scale and compliance needs.
Do you run it after launch?
Yes. We keep operating the platform, tuning cost and performance, and scaling it as your AI footprint grows.
Let's build your AI cloud infrastructure
Tell us about your workloads, your cloud, and your constraints.
6+
Years Of Experience
40+
Skilled Professionals
105+
Projects Delivered
35+
Global Clientele Served