Cloud Platforms Overview

Deploy and operate Fiddler natively on leading cloud platforms

Run Fiddler AI Observability in your preferred cloud environment with native platform integrations. Deploy as a fully managed Partner AI App on AWS SageMaker, run in your Kubernetes clusters, or leverage cloud-native services for ML model monitoring at scale.

Why Cloud Platform Integrations Matter

Modern AI systems are built on cloud infrastructure. Fiddler's cloud platform integrations ensure you can:

  • Maintain Data Sovereignty - Keep all your data and models within your existing cloud security boundaries

  • Simplify Procurement - Subscribe through cloud marketplaces with consolidated billing

  • Leverage Existing Infrastructure - Use your current IAM, networking, and security configurations

  • Scale Seamlessly - Cloud-native deployment automatically scales with your ML workloads

  • Reduce Operational Overhead - Managed platform integrations eliminate manual infrastructure management

AWS Deployments

AWS SageMaker Partner AI App

Run Fiddler as a fully managed Partner AI App within Amazon SageMaker.

Best for: Enterprise AWS customers wanting seamless SageMaker integration with zero external infrastructure

Key Features:

  • Fully managed infrastructure within your AWS account

  • Native SageMaker Studio integration

  • No external accounts or data transfer required

  • Automatic updates and maintenance handled by AWS

  • Consolidated AWS billing through Marketplace

Deployment Options:

  • 30-Day Free Trial - Full functionality for up to 5 models (infrastructure costs apply)

  • Tiered Subscriptions - Small, Medium, and Large configurations for different team sizes

  • AWS Marketplace - Monthly or annual subscriptions with flexible pricing

Status: ✓ GA - Production-ready

Get Started with AWS SageMaker Partner AI App →

Related AWS Integrations:

Multi-Cloud & Kubernetes

While AWS SageMaker Partner AI App is our featured cloud platform integration, Fiddler supports deployment across multiple cloud providers:

Supported Deployment Patterns

Cloud Platforms:

  • AWS - Native SageMaker Partner AI App (recommended), EC2, EKS

  • Azure - Azure ML integration (contact sales), AKS deployment

  • Google Cloud - GKE deployment, Vertex AI connectivity

  • Private Cloud - On-premises Kubernetes with cloud connectivity

Kubernetes Deployments:

  • Helm Charts - Production-grade Kubernetes deployment templates

  • Operator Pattern - Automated lifecycle management for Fiddler clusters

  • Multi-Cluster Support - Monitor ML models across distributed Kubernetes environments

  • Cloud-Agnostic - Run on EKS, AKS, GKE, or on-premises Kubernetes

Container Orchestration:

  • Docker Compose - Development and testing environments

  • Docker Swarm - Small-scale production deployments

  • Kubernetes - Enterprise production deployments

Deployment Architecture Patterns

Pattern 1: Fully Managed (Recommended for AWS)

Use AWS SageMaker Partner AI App for zero-infrastructure management:

AWS Account (Your VPC)
├── SageMaker Studio (UI Access)
├── Fiddler Partner AI App (Managed)
│   ├── Compute (Managed by AWS)
│   ├── Storage (Your S3 buckets)
│   └── Database (Managed RDS)
└── Your ML Models (SageMaker Endpoints)

Advantages:

  • No operational overhead

  • Automatic scaling and updates

  • AWS handles infrastructure security

  • Seamless Studio integration

Pattern 2: Self-Managed Kubernetes

Use Helm Charts for full control over infrastructure:

Cloud Provider (Your Kubernetes Cluster)
├── Fiddler Namespace
│   ├── API Server Pods
│   ├── Worker Pods
│   ├── Database (StatefulSet)
│   └── Storage (PersistentVolumes)
└── Ingress/Load Balancer

Advantages:

  • Full infrastructure control

  • Cloud-agnostic deployment

  • Custom security configurations

  • On-premises compatibility

Pattern 3: Hybrid Multi-Cloud

Deploy Fiddler in one cloud, monitor models across all:

Primary Cloud (Fiddler Installation)
├── Fiddler Platform
└── Centralized Monitoring Dashboard

Connected Clouds
├── AWS (SageMaker models)
├── Azure (Azure ML models)
├── GCP (Vertex AI models)
└── On-Premises (Legacy models)

Advantages:

  • Unified observability across clouds

  • Centralized governance and compliance

  • Flexible hybrid architecture

Getting Started

For AWS Customers

Quick Start Path:

  1. Subscribe - Get Fiddler from AWS Marketplace

  2. Deploy - Use SageMaker Partner AI Apps one-click deployment

  3. Configure - Run the Quick Setup Script for IAM roles

  4. Monitor - Connect your SageMaker models and LLM applications

Full AWS Deployment Guide →

Need Help?

For Kubernetes Deployments

Prerequisites:

  • Kubernetes 1.21+ cluster

  • Helm 3.8+ installed

  • Storage provisioner (for persistent volumes)

  • Ingress controller (for external access)

Installation:

# Add Fiddler Helm repository
helm repo add fiddler https://helm.fiddler.ai
helm repo update

# Install Fiddler
helm install fiddler fiddler/fiddler \
  --namespace fiddler \
  --create-namespace \
  --set license.key=<your-license-key> \
  --set ingress.enabled=true \
  --set ingress.hostname=fiddler.your-domain.com

Contact Sales for Kubernetes Deployment →

For Other Cloud Providers

Azure Customers:

Google Cloud Customers:

Migration & Upgrade Paths

From Other Monitoring Platforms

Migrating from Competitor Platform:

  1. Parallel Deployment - Run Fiddler alongside existing monitoring

  2. Data Migration - Import historical metrics and model metadata

  3. Gradual Cutover - Model-by-model transition with zero downtime

  4. Training & Onboarding - Dedicated support during migration

Common Migration Sources:

  • Arize - Direct migration tools available

  • Weights & Biases - Model metadata import supported

  • DataRobot - Custom migration scripts available

  • Custom Solutions - API-based migration assistance

Upgrading Within Fiddler

AWS SageMaker Partner AI App Upgrades:

  • Tier Upgrades - Scale from Small → Medium → Large as needed

  • Version Updates - Automatic updates during maintenance windows

  • Trial to Production - Requires redeployment (preserve data with migration scripts)

Self-Managed Upgrades:

  • Helm Upgrades - Standard helm upgrade process

  • Rolling Updates - Zero-downtime deployments

  • Backup & Restore - Automated backup before each upgrade

Security & Compliance

Data Residency

AWS SageMaker Partner AI App:

  • All data stays within your AWS account and region

  • No external data transfer to Fiddler's infrastructure

  • Choose your preferred AWS region during deployment

Self-Managed Deployments:

  • Full control over data location

  • Support for air-gapped environments

  • On-premises deployment options

Compliance Certifications

  • SOC 2 Type II - Fiddler platform certified

  • GDPR - Data processing agreements available

  • HIPAA - Compliant deployment options for healthcare

  • FedRAMP - In progress (contact for timeline)

Security Features

  • Encryption at Rest - All stored data encrypted (AWS KMS, customer-managed keys)

  • Encryption in Transit - TLS 1.3 for all network communication

  • SSO Integration - SAML 2.0, OIDC support (Azure AD, Okta, AWS SSO)

  • RBAC - Role-based access control with fine-grained permissions

  • Audit Logging - Complete audit trail of all platform activities

Cost Optimization

AWS SageMaker Partner AI App Pricing

Total Cost of Ownership (TCO):

  • Software License - Billed through AWS Marketplace

  • Infrastructure - AWS resource costs (EC2, RDS, S3, etc.)

  • Data Transfer - Minimal costs (data stays in your VPC)

Tier Selection Guidelines:

Team Size
Models Monitored
Recommended Tier
Est. Monthly Cost*

< 10 users

< 20 models

Small

$500 - $1,000

10-50 users

20-100 models

Medium

$1,500 - $3,000

50+ users

100+ models

Large

$3,000 - $6,000

*Infrastructure costs only (software license additional)

Cost Optimization Tips:

  • Start with Small tier for POCs and development

  • Use Reserved Instances for production workloads (20-40% savings)

  • Right-size tier based on actual usage metrics

  • Scheduled scaling for non-production environments

Self-Managed Cost Optimization

  • Spot Instances - Use for worker nodes (50-70% savings)

  • Auto-Scaling - Scale compute based on monitoring load

  • Storage Tiering - Move historical data to cheaper storage classes

  • Multi-Tenancy - Share Fiddler instance across multiple teams

Monitoring Infrastructure Health

Platform Metrics

AWS SageMaker Partner AI App:

  • View infrastructure health in SageMaker Console

  • CloudWatch metrics for Fiddler components

  • Automatic alerting for platform issues

  • AWS Support for troubleshooting

Self-Managed:

  • Prometheus metrics exported by default

  • Grafana dashboards for infrastructure monitoring

  • Health check endpoints for uptime monitoring

  • Integration with existing observability stack

Support & Resources

AWS SageMaker Partner AI App

Self-Managed Deployments

  • Enterprise Support - 24/7 support with SLA guarantees

  • Deployment Assistance - Professional services for setup

  • Training - Platform administrator training programs

  • Community - Join our Slack community

Related Integrations

Need Help Choosing?

Not sure which deployment option is right for you? We're here to help:

Featured Integration: The AWS SageMaker Partner AI App is our recommended deployment for AWS customers, offering the fastest time-to-value with zero operational overhead. Learn more →

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