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Azure

Azure AI Deployment

FISTA Solutions deploys AI workloads on Azure inside your existing governance: private endpoints and network isolation, Entra ID identity and role scoping, content filtering configuration, quota and capacity planning, evaluation gates in CI, and cost attribution per workload.

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150+
projects delivered
50+
companies served
99.9%
verified uptime
47%
efficiency gains
12+
countries reached

What we build

What does Azure AI deployment include?

An Azure AI deployment covers landing zone alignment, private endpoints and network rules, Entra ID roles and managed identities, service provisioning and content filter configuration, application runtime on your chosen compute, evaluation pipelines, and cost governance.

  1. 01

    Network and identity

    Private endpoints, network rules, Entra ID roles, and managed identities rather than shared keys.

    Foundation
  2. 02

    Service provisioning

    Resource and deployment provisioning as infrastructure-as-code, with content filter policies applied.

    Provisioning
  3. 03

    Application runtime

    Your application or agent on App Service, Container Apps, or AKS with the right scaling model.

    Runtime
  4. 04

    Evaluation pipeline

    Golden-set evaluation in CI, so model or prompt changes are measured before release.

    Quality
  5. 05

    Observability

    Tracing, redacted prompt logging, and dashboards for quality, latency, and spend.

    Operations
  6. 06

    Cost governance

    Per-workload attribution with budgets and alerts, plus quota planning against projected demand.

    Economics

Requirements

Which requirements shape Azure AI deployment?

Azure AI deployments live or die on capacity planning and identity discipline. Requirements cover private networking, managed identity rather than keys, quota and capacity management, content filtering behavior, and logging that respects data-handling commitments.

Azure: requirements and how FISTA Solutions builds to them
RequirementWhy it mattersHow FISTA implements it
Private networkingAI endpoints should not be publicly reachable.Private endpoints and network rules aligned to your landing zone, with data flows documented.
Managed identityShared API keys are a standing risk.Entra ID managed identities with role assignments scoped per workload, and no long-lived keys in configuration.
Quota and capacityCapacity constraints cause production failures.Quota planning against projected demand, with provisioned capacity where latency guarantees matter.
Content filteringFilter behavior affects application UX.Filter policy configured deliberately per workload, with application handling of filtered responses designed.
Data handlingPrompt content may be sensitive.Redaction before logging, retention limits, and configuration reviewed against your data commitments.

Where AI fits

How should you sequence Azure AI deployment?

Sequence an Azure deployment so identity and capacity are settled before applications depend on them: provision through infrastructure-as-code, prove the boundary, add evaluation and observability, migrate behind a gateway, then tune capacity with real traffic.

  1. 01

    1. Provision as code

    Resources, networking, and role assignments defined in infrastructure-as-code from the start.

  2. 02

    2. Prove the boundary

    Private networking and managed identity validated with a small non-critical workload.

  3. 03

    3. Add evaluation

    Golden-set evaluation in CI before applications depend on model behavior.

  4. 04

    4. Migrate behind a gateway

    A gateway so routing, model selection, and failover are configuration rather than code.

  5. 05

    5. Plan capacity

    Quota and provisioned capacity tuned once real traffic patterns and latency needs are visible.

Cost and timeline

What does Azure AI deployment cost, and how long does it take?

Cost is driven by token volume, capacity mode, and surrounding infrastructure; timeline by subscription access, quota approval, and security review. FISTA does not quote blind: the scoping call returns an architecture, a cost model, and a phased plan.

Quota and capacity approvals are a real schedule item on Azure and frequently the longest one. Discovery identifies what is needed and starts those requests immediately.

Capacity mode is a cost and latency decision together. FISTA models expected traffic and latency requirements so the choice between on-demand and provisioned capacity is evidence-based.

Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.

Get a scoped quote

Delivery

How does FISTA deliver an AI deployment?

FISTA deploys AI in four phases: an assessment that inventories workloads, data boundaries, and constraints and produces a target architecture; a platform build with networking, identity, secrets, and observability as code; a migration with evaluation gates and shadow traffic; and a production cutover with dashboards, budgets, runbooks, and rollback.

  1. 1

    Assess and target

    Workload inventory, data classification, latency and volume profile, compliance constraints, and a target architecture with cost model.

    Output

    Target architecture, cost model

  2. 2

    Build the platform

    Networking, identity, key management, model endpoints, gateway, tracing, and evaluation pipeline delivered as infrastructure-as-code.

    Output

    Platform as code, control matrix

  3. 3

    Migrate with gates

    Move applications behind the gateway, run evaluation and shadow traffic, and tune routing, caching, and capacity.

    Output

    Eval reports, shadow results

  4. 4

    Cut over and operate

    Graduated production rollout, dashboards for quality, latency, and cost, runbooks, on-call, and a change process with rollback.

    Output

    Production platform with SLOs

Why FISTA

Why choose FISTA Solutions for Azure AI deployment?

FISTA deploys Azure AI inside your landing zone with managed identity, private networking, and evaluation gates, and plans capacity before it constrains you. Work is contracted through a US entity with full IP assignment.

Azure specifics

  • Resources, networking, and role assignments are defined as infrastructure-as-code and aligned to your landing zone.
  • Managed identities replace shared keys, with role assignments scoped per workload.
  • Quota and capacity are planned against projected demand rather than discovered during a production incident.
  • Golden-set evaluation runs in CI so model and prompt changes are measured before they reach users.

How FISTA engineers

  • Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
  • AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
  • Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
  • One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.

What you get as a client

  • 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
  • A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
  • US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
  • Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.

Clear answers

What platform teams ask before deploying AI.

Straightforward guidance for evaluating scope, fit, and the next step.

01Can we keep AI traffic on our private network?

Yes, through private endpoints and network rules aligned to your landing zone, with data flows documented so your security team can verify the boundary rather than trust a summary.

02How do you handle authentication?

Entra ID managed identities with per-workload role assignments, avoiding long-lived shared keys in application configuration entirely.

03What about capacity and quota limits?

They are planned against projected demand during design, with provisioned capacity where latency guarantees matter. Quota requests often take longer than the engineering, so they start immediately.

04Can we use Claude models on Azure?

Model and service availability on Azure changes over time and by region, so FISTA confirms current availability during design rather than publishing a list that ages. A gateway keeps provider choice a configuration decision.

05How long does an Azure AI deployment take?

A first production workload typically takes weeks to a couple of months, with subscription access, quota approvals, and security review as the usual gating items.

Continue exploring

Related capabilities

AI gateway and observabilityProvider-agnostic routing, tracing, and governance.Hybrid AI deploymentRunning across cloud and private infrastructure deliberately.AI enablementEnterprise AI platform and adoption programs.

Scoped in writing before you commit

Deploy AI under the identity and network controls you already run.

Bring your landing zone and workloads. The scoping call returns an architecture, a capacity plan, and a cost model.

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