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AI deployment

AI Deployment Services

FISTA Solutions provides AI deployment services that take LLM applications, RAG systems, and agents from prototype to production on AWS Bedrock, Azure OpenAI, Google Vertex AI, private clouds, on-premise clusters, and edge devices. Each of the 16 pages covers one target: architecture, security, cost, observability, and rollout.

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

Choose the deployment target

Where can FISTA deploy your AI systems?

FISTA deploys on cloud platforms (AWS Bedrock, Azure OpenAI, Google Vertex AI, Claude via Anthropic and cloud marketplaces), on private and on-premise infrastructure (private cloud, on-premise GPU clusters, open-source LLMs on vLLM and Kubernetes, air-gapped, hybrid, edge and on-device), and as operations (LLMOps platforms, fine-tuning pipelines, RAG deployment, AI gateways and observability, cost optimization).

Cloud platforms

  1. 01AWS BedrockAWS Bedrock deployment servicesOpen
  2. 02AzureAzure OpenAI deployment servicesOpen
  3. 03Google Vertex AIVertex AI deployment servicesOpen
  4. 04Claude DeploymentClaude deployment servicesOpen

Private & on-premise

  1. 01On-Premise LLMon-premise LLM deploymentOpen
  2. 02Private Cloud AIprivate cloud AI deploymentOpen
  3. 03Open-Source LLMsopen source LLM deploymentOpen
  4. 04Kubernetes & vLLMvLLM deployment servicesOpen
  5. 05Air-Gapped AIair-gapped AI deploymentOpen
  6. 06Edge AIedge AI deploymentOpen

Operations & optimization

  1. 01LLMOpsLLMOps servicesOpen
  2. 02Fine-TuningLLM fine-tuning servicesOpen
  3. 03RAG DeploymentRAG deployment servicesOpen
  4. 04AI GatewayAI gateway developmentOpen
  5. 05AI Cost OptimizationAI cost optimization servicesOpen
  6. 06Hybrid AIhybrid AI deploymentOpen

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 ai deployment?

Because the specification is written before the code, the engineers are AI-native and reviewed, the contract is with a US entity, and the delivery rhythm is weekly demos on your environment. The two lists below are the same on every page in this section; the page-specific reasons are on each page.

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.

01Should we deploy on a cloud AI platform or self-host models?

Managed platforms (Bedrock, Azure OpenAI, Vertex AI) win on time-to-production, model breadth, and compliance inheritance. Self-hosting open-weight models wins when data cannot leave your network, when volume makes GPU economics favorable, or when you need full control of weights. FISTA models both against your traffic and constraints before recommending.

02How do you secure an LLM deployment?

Private endpoints and VPC peering, customer-managed keys, secrets in a vault, least-privilege service identities, prompt and output logging with PII handling, input and output guardrails, and a threat model covering prompt injection and data exfiltration. Controls are written into the deployment spec and verified before go-live.

03What does LLMOps include?

Prompt and model versioning, an evaluation harness run in CI, canary and shadow deployments, tracing of every request, quality and cost dashboards, drift alerts, and a rollback path. FISTA builds it on your tooling or on open standards such as OpenTelemetry.

04How do you control AI inference cost?

Model routing by task, caching, prompt compression, batching, right-sized context, provisioned versus on-demand capacity decisions, and budgets with alerts per application. Cost is a first-class metric in the deployment dashboards, alongside latency and quality.

05Can FISTA operate the deployment after go-live?

Yes. Options are handover with runbooks and training, a managed operations retainer with SLOs and on-call, or embedded engineers inside your platform team. Everything is delivered as infrastructure-as-code so any option is reversible.

Continue exploring

Related capabilities

AI enablementPrivate LLMs, RAG pipelines, and enterprise AI platforms.AI agent developmentThe agents that run on the deployment you are planning.AI developmentThe applications, RAG systems, and models being deployed.

Scoped in writing before you commit

Move from prototype to a production AI platform you can defend.

Tell us the workloads, the data boundaries, and the cloud. The scoping call returns a target architecture, control matrix, and phased plan.

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