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Deployment Options

DocAI Fabric offers flexible deployment options to fit your infrastructure requirements, security policies, and operational preferences.

Choose Your Deployment Model​

OptionBest ForInfrastructureManaged By
SaaSFastest start, no infra to manageOur cloudUs
Managed in AzureNew subscription inside your private network, we handle everythingYour AzureUs (everything)
Self-Hosted (On-Premises)Full control, on-premises (standalone or highly available)Your serversYou
Managed in AWSAWS-native environmentsYour AWSUs (deployment & updates)
GCPGCP-native environmentsYour GCPYou

Comparison​

FeatureSaaSManaged AzureManaged AWSSelf-HostedGCP
Setup timeMinutesDays (your network team prepares subnets and DNS)~1 hour~2 hours~2 hours
Data locationOur Azure tenantYour Azure subscriptionYour AWS accountYour infrastructureYour cloud
UpdatesAutomaticManaged by usManaged by usManualManual
Custom domainIncludedSupportedSupportedYour responsibilityYour responsibility
ScalingAutomaticAutomatic (Container Apps)Automatic (ECS)ManualManual
AI servicesIncludedYour Azure OpenAIAWS Bedrock + Textract (your account)Your endpointsYour endpoints
MonitoringIncludedYour Application InsightsYour CloudWatchYour toolsYour tools

What Every Deployment Needs​

Regardless of deployment model, DocAI Fabric requires:

  • An LLM for classification and extraction: Azure OpenAI (GPT-4.1-mini or newer recommended), Anthropic Claude on Amazon Bedrock for AWS deployments (in development), or any OpenAI-compatible endpoint you connect on the Models page (including your own on-premises GPU servers for self-hosted deployments)
  • An OCR service: Azure Document Intelligence (Read model), Amazon Textract for AWS deployments (in development), or the built-in PaddleOCR and Tesseract engines that ship with self-hosted deployments and need no cloud service
  • Container runtime: Docker or a managed container service
  • Redis: for job queue and caching
  • Blob / file storage: for document persistence