Hybrid Cloud with AWS: 8 Tools and a Reference Architecture

What Is a Hybrid Cloud? 

A hybrid cloud integrates private and public cloud services to allow data and applications to be shared between them. This setup gives businesses more flexibility in data deployment and scalability while still having control over sensitive data. The hybrid approach is useful in environments that require regulatory compliance or where low-latency is needed, as it can deploy applications locally while leveraging public cloud resources for other operations.

Companies utilize hybrid cloud to maintain on-premises infrastructure for sensitive workloads while using the scalability of the public cloud. This model is a cost-effective strategy that ensures organizations remain agile in addressing varying workload needs. It supports tackling seasonal business changes without committing to new hardware investments.

Benefits of Building a Hybrid Cloud with AWS 

Accelerate Digital Transformation

Hybrid cloud solutions on AWS allow organizations to take advantage of the AWS platform while retaining control of critical systems internally. By embracing hybrid cloud, businesses can adopt emerging technologies and integrate them with legacy systems more easily.

Hybrid cloud also facilitates rapid application development and deployment. It enables DevOps practices through automation tools that function across on-premises and cloud infrastructures. AWS services integrate with existing systems, allowing teams to work more efficiently without disruptive overhauls.

Improve IT and Developer Productivity

AWS hybrid cloud solutions streamline IT operations by reducing the complexity inherent in managing disparate environments. Integrating tools for monitoring, management, and automation provides unified control over resources, thus minimizing administrative overhead. With consistent tools across hybrid environments, teams can focus more on app development rather than infrastructure maintenance.

For developers, hybrid cloud on AWS offers a platform to build, deploy, and iterate applications rapidly. AWS provides services and APIs that work across environments, offering a consistent development experience.

Learn more in our detailed guide to IT mapping

Deliver Differentiated Services and Experiences

Hybrid cloud enhances the delivery of differentiated services by enabling a versatile IT infrastructure capable of supporting innovative solutions. AWS facilitates the creation of tailored customer experiences through analytics services that provide insights from both cloud and on-premises data. This ecosystem supports personalized services, aligning business offerings with market demands.

Incorporating hybrid cloud allows businesses to quickly adapt to customer feedback, refine offerings, and address specific market needs. The agility of AWS hybrid solutions helps in launching new products and enhancing the reliability and performance of existing services.

AWS Hybrid Cloud Solutions and Tools

Building a hybrid cloud on AWS involves two complementary groups of solutions: tools that give you visibility into and control over a hybrid estate, and the AWS infrastructure services that extend AWS itself to on-premises and edge locations. The tools below are organized into these two categories.

Hybrid Cloud Management and Monitoring

1. Faddom

Faddom is an agentless application dependency mapping platform that discovers and continuously maps servers, applications, and their dependencies across on-premises and cloud environments. It connects to existing data sources and builds real-time maps, automatically grouping related servers into business applications. The software runs passively with read-only permissions and does not require installing agents, opening firewalls, or supplying server credentials. Deployment is self-service and lightweight, and first maps typically appear within an hour of installation. Maps update automatically around the clock, and the platform can operate entirely offline so collected data never leaves the environment.

Key features include:

• Agentless, passive discovery: Faddom collects data passively using read-only permissions, without installing agents on servers, opening firewalls, or providing credentials. This approach is designed to discover hosts, services, and communication flows while avoiding added load or security exposure on the monitored environment.
• Real-time dependency mapping: The platform builds interactive maps of how servers, services, and applications communicate, and automatically groups related servers into business applications. Maps are dynamic and refresh continuously, so the view reflects the current state of the environment rather than a one-time snapshot.
• Hybrid and multi-cloud coverage: Faddom maps on-premises servers and cloud instances together from multiple data sources to provide a single view across hybrid and multi-cloud topologies. It is platform-agnostic and connects to a range of virtualization and cloud environments to assemble the full picture.
• Offline operation and data residency: The software can run entirely offline without an internet connection, keeping all collected data inside the customer’s environment. This supports highly segmented or secure networks and avoids sending topology data to an external service.
• AI-driven correlation and analysis: Faddom uses AI-driven correlation to turn raw network data into application and dependency context, and Faddom AI allows users to query the environment in natural language. This adds an operational context layer for understanding, operating, and securing hybrid environments.
• Migration, change, and security use cases: The maps support wave-based cloud and data-center migration planning, change impact analysis, IT audit and compliance documentation, and security tasks such as identifying communication flows and exposed services across the estate.

Limitations (as reported by users on G2):

• Initial learning curve: Some users note that the breadth of features and the platform’s terminology take time to learn, and that becoming fully comfortable can require a short ramp-up period supported by onboarding.
• Reporting and export options: A few users would like richer reporting and dashboard export capabilities, such as more polished executive-level reports, noting that current exports cover core needs but could be expanded.
• Occasional duplicate host entries: Some users mention that a single server can occasionally appear under more than one hostname in the map, which may require minor cleanup when devices are renamed or migrated.

2. Amazon CloudWatch 

Amazon CloudWatch is AWS’s monitoring and observability service for applications and infrastructure. It collects and connects metrics, logs, and traces to provide a unified view of workloads and the resources that power them. CloudWatch can monitor workloads running on AWS, on premises, and on other clouds by ingesting telemetry through an OpenTelemetry-compatible agent. It includes dashboards, alarms, anomaly detection, and AI-assisted investigations for finding and resolving issues. It also integrates with open-source tooling such as Prometheus and Grafana.

Key features include:

• Unified metrics, logs, and traces: CloudWatch connects metrics, logs, and traces in one place so teams can see relationships between applications, workloads, and infrastructure. This is intended to help identify performance bottlenecks and dependencies without switching between separate tools.
• Cross-environment telemetry ingestion: Using the OpenTelemetry-compatible CloudWatch agent, the service ingests telemetry from workloads on AWS, on premises, and on other clouds. Ingestion charges are the same regardless of where the data originates, allowing a single monitoring approach across a hybrid estate.
• Infrastructure and application observability: CloudWatch provides built-in insights for databases, containers, and serverless functions such as Lambda, plus application performance monitoring. It surfaces metrics on latency, errors, and usage across the stack, including monitoring for generative AI workloads.
• Alarms, dashboards, and anomaly detection: Users can build dashboards, set alarms, and apply anomaly detection to spot deviations from normal behavior. Dashboards can be out-of-the-box or custom, and queries can be written in plain English or in languages such as SQL.
• AI-assisted operations (AIOps): CloudWatch investigations use AI-powered root cause analysis to help diagnose issues, and natural-language queries translate questions into precise log and metric searches. The service can also connect external agents to CloudWatch data through Model Context Protocol (MCP) servers.
• Open-source and open-standard support: The service integrates with Prometheus and Grafana and supports OpenTelemetry, letting teams collect, process, and export telemetry using open standards while retaining flexibility in their tooling.

Limitations (as reported by users on G2):

• Complex configuration: Some users report that setting up custom metrics, alarms, and integrations can be complex, and that new users face a learning curve before becoming productive.
• Cluttered interface and basic dashboards: Several reviewers describe the console as cluttered and harder to navigate than expected, and note that the built-in dashboards are relatively basic compared with dedicated visualization tools.
• Cost management at scale: Users note that pricing can be difficult to predict and manage, with costs rising as log ingestion, custom metrics, and detailed monitoring increase.

3. AWS Systems Manager 

AWS Systems Manager is a management service for operating nodes at scale across AWS, on-premises, and multicloud environments. It provides a centralized view of managed and unmanaged nodes across accounts and Regions, with details such as operating system, installed agents, and tags. The Systems Manager Agent (SSM Agent) runs on EC2 instances and on non-EC2 machines registered through hybrid activations, bringing on-premises and other-cloud servers under the same controls. It groups capabilities for node management, operations, change management, and applications. Common tasks include patching, secure remote access, and automation.

Key features include:

• Centralized node visibility: Systems Manager gives a single view of nodes across an organization’s AWS accounts and Regions as well as hybrid and multicloud environments. It can identify managed and unmanaged nodes and help remediate agent issues so more machines come under management.
• Hybrid and multicloud node management: Through hybrid activations and the SSM Agent, on-premises servers and machines in other clouds are registered as managed nodes. Standard and advanced instance tiers determine how many non-EC2 machines can be registered and which features, such as Session Manager, are available.
• Patch and configuration management: Patch Manager automates operating system and application patching across cloud and on-premises nodes on a defined schedule. Configuration tasks such as software installation, registry edits, and user management can be run across groups of nodes.
• Secure remote access: Session Manager provides interactive shell access to managed nodes without opening inbound ports, managing SSH keys, or maintaining bastion hosts. Sessions can be logged and governed through IAM policies for auditing and compliance.
• Automation and operations tooling: Systems Manager includes Automation runbooks, Maintenance Windows, Fleet Manager, Explorer, and OpsCenter for running and tracking operational tasks at scale. Application-focused tools such as Application Manager, AppConfig, and Parameter Store help manage configuration and application data.

Limitations (as reported by users on G2):

• Steep learning curve: Users note that capabilities such as Automation, Patch Manager, and State Manager require time to learn, and the breadth of the service can be challenging for those new to it.
• Initial setup complexity: Reviewers mention that configuring roles, permissions, and the SSM Agent is not beginner-friendly, and that some options can be hard to locate in the console.
• Cluttered console for newcomers: Some users describe the interface as cluttered and unintuitive for smaller teams, with day-to-day usability that could be smoother.

AWS Hybrid and Edge Infrastructure

4. AWS Outposts

AWS Outposts is a family of fully managed services that delivers AWS infrastructure and services to on-premises and edge locations for a consistent hybrid experience. It comes in multiple form factors, from 1U and 2U Outposts servers to 42U Outposts racks and multi-rack deployments. Outposts lets organizations run select AWS services locally while connecting to the broader set of services available in the parent AWS Region. It uses the same hardware, APIs, tools, and management controls available in the cloud. It targets workloads that need low-latency access to on-premises systems, local data processing, data residency, or migration of applications with local dependencies.

Source: Amazon

Key features include:

• Consistent on-premises AWS infrastructure: Outposts extends AWS compute, storage, networking, and other services into a customer’s facility using AWS-designed hardware. The same APIs, tools, and management controls used in the cloud apply locally for a consistent operations experience.
• Multiple form factors: The family includes 42U Outposts racks that scale from a single rack to multiple-rack deployments, and 1U or 2U Outposts servers for sites with limited space such as retail stores, branch offices, or factory floors. Racks include integrated networking gear, while servers provide local compute and networking in a smaller footprint.
• Locally supported AWS services: Outposts racks can run services such as Amazon EC2, ECS, EKS, EBS, S3 on Outposts, and RDS locally, and extend an Amazon VPC on premises. Servers support a smaller set including EC2, ECS, and AWS IoT Greengrass, with connectivity back to Region services.
• Fully managed delivery and operation: AWS delivers, installs, and manages the infrastructure; racks arrive assembled and are connected to power and network, and AWS remotely provisions capacity. This reduces the maintenance and operational tasks associated with running on-premises hardware.
• Targeted hybrid use cases: Outposts is used for low-latency compute such as manufacturing execution systems, high-frequency trading, or medical diagnostics, as well as data residency requirements, phased migration of applications with local dependencies, and local data processing such as data lakes or ML model training.

Limitations (as reported by users on PeerSpot):

• Deployment complexity: Users report that initial deployment can be challenging and has specific facility, power, and networking prerequisites, sometimes requiring coordination with AWS and colocation providers.
• Hardware-bound scaling: Because capacity is tied to physical units, expanding requires ordering additional hardware, which adds lead time compared with provisioning capacity in a Region.
• Cost and service coverage: Reviewers point to delivery and overall costs, limited local support for some AWS native services, and a desire for more flexible pricing for smaller organizations.
• Region dependency: Outposts is not designed to operate when disconnected from its parent AWS Region, so a stable connection back to AWS is required for normal operation.

5. AWS Local Zones

AWS Local Zones are infrastructure deployments that place core AWS services closer to large population and industry centers. They extend compute, storage, networking, and select database, analytics, and AI/ML services to metros that are not full AWS Regions, with 30+ locations across six continents. Local Zones connect back to a parent Region over the AWS network, so applications can use regional services while keeping latency-sensitive components nearby. They are built on the same AWS Nitro System and inherit security controls from AWS Regions. They are used for low-latency applications, data residency, and migrating latency-sensitive workloads.

Source: Amazon

Key features include:

• Services in more metros: Local Zones extend AWS services to 30+ metropolitan areas so latency-sensitive workloads can run closer to end users. Applications can address data residency requirements by keeping workloads and data in a specific metro.
• Connection to parent Region: Each Local Zone connects to its parent AWS Region over the AWS backbone, giving access to the full set of regional services. This lets architectures run latency-sensitive components locally while using regional services for everything else.
• Consistent AWS tooling and security: Local Zones use the same AWS infrastructure, APIs, and tools as Regions and are built on the AWS Nitro System. They inherit security controls from AWS Regions and support familiar high-availability and resiliency strategies.
• AI/ML capabilities: In select metros such as Atlanta, Dallas, and Phoenix, Local Zones offer GPU clusters and AWS Trainium with EC2 UltraClusters and EFA networking for large-scale training and inference. Distributed inference can run across Local Zones in many metros, with access to Amazon Bedrock and SageMaker.
• Flexible consumption: Capacity scales on demand without overprovisioning, with multiple instance types and pricing models including On-Demand, Savings Plans, and Spot. This lets workloads match resources to demand in each location.

Limitations (as reported by users on Gartner Peer Insights):

• Limited location availability: Local Zones are only available in select metros, so they cannot serve every region or user, and coverage may not match all deployment needs.
• Reduced service coverage: They offer a subset of services compared with full AWS Regions, and persistent services such as object storage and databases are often accessed through the parent Region rather than locally.
• Higher cost and limited instance types: Reviewers note that compute in a Local Zone can cost more than the equivalent in a Region’s Availability Zone, and the range of available instance types can be narrower.

6. AWS Snowball

AWS Snowball is a petabyte-scale data transport and edge computing service that uses physical devices to move large amounts of data into and out of AWS. It is intended for situations where transferring data over the network is impractical due to cost, time, or limited connectivity. Devices come in compute-optimized and storage-optimized options, and data is protected in transit by a ruggedized, tamper-evident enclosure and encryption. Beyond migration, Snowball devices can run local compute, including Amazon EC2 instances and AWS Lambda functions, in disconnected or remote environments. Once a device is returned, its data is copied to Amazon S3 and the device is securely erased.

Source: Amazon

Key features include:

• Petabyte-scale offline transfer: Snowball moves large data sets such as databases, backups, archives, and media libraries to AWS without relying on high-cost or slow network transfers. AWS prepares and ships the device, and after loading it is returned for ingestion into S3.
• Device options: Customers can choose Snowball Edge Compute Optimized or Storage Optimized devices depending on whether the priority is local processing or capacity. Jobs are created in the Snowball console and tied to an S3 bucket with notification-based tracking.
• Edge compute in disconnected environments: Devices can run Amazon EC2 AMIs and deploy AWS Lambda code locally, allowing data to be processed or analyzed on site where connectivity is limited or absent, such as factory floors or remote locations.
• Security and chain of custody: Data is protected with a ruggedized, tamper-evident chassis and encryption, and an E Ink shipping label simplifies return logistics. After data is moved to S3 and verified, the device is sanitized of customer information.
• Managed workflow: AWS OpsHub is used to unlock and manage the device, transfer data, and launch EC2 instances, providing a single tool to operate the device through the job lifecycle.

Limitations (as reported by users on PeerSpot):

• Not beginner-friendly: Users note that Snowball is easier for experienced professionals and can be difficult for beginners to start using without prior knowledge.
• Limited connectivity options: Reviewers would like more connectivity choices to make adding and moving data easier, and some report that stability could be improved.
• Availability and physical handling: Device availability is limited in some countries due to shipping restrictions, and the physical nature of the device introduces transfer time, handling, and logistics considerations.

7. Amazon ECS/EKS Anywhere

Amazon ECS Anywhere and Amazon EKS Anywhere extend AWS container management to customer-managed infrastructure. ECS Anywhere is a feature of Amazon ECS that lets teams run and manage container workloads on their own on-premises or edge hardware using the same ECS control plane, APIs, and tooling as in AWS. EKS Anywhere lets teams create and operate Kubernetes clusters on their own infrastructure using the same EKS-based experience, built on the open-source EKS Distro. Both are aimed at organizations that want a consistent AWS container experience while keeping workloads on existing hardware for compliance, data sovereignty, or latency reasons. Joining an ECS Anywhere cluster requires installing the SSM Agent and the ECS Anywhere agent on each server.

Source: Amazon

Key features include:

• Consistent container tooling on-premises: ECS Anywhere runs an in-Region ECS control plane so teams use the same APIs, cluster management, scheduling, and monitoring on their own hardware as they do in AWS. This avoids running and maintaining a separate on-premises orchestrator.
• Kubernetes clusters on your infrastructure: EKS Anywhere lets teams create and operate Kubernetes clusters on premises using the same EKS experience and the open-source EKS Distro. Clusters can be administered with familiar Kubernetes utilities such as the eksctl CLI and the Kubernetes API.
• Hybrid consistency: Both services unify how applications run across cloud and on-premises environments, helping standardize operations and tooling across locations. They can also run on AWS Outposts when customers want AWS-managed hardware on premises.
• Edge and data-processing workloads: ECS Anywhere supports running containerized data-processing workloads at edge locations on local hardware to keep latency low. EKS Anywhere supports workloads including GPU-based large language model training and inference on local infrastructure.
• Data sovereignty and existing investments: Both let organizations keep data and workloads on premises to meet regulatory or data-residency requirements while reusing existing infrastructure, supporting modernization without moving workloads to the cloud.

Limitations (based on publicly available sources):

• Support and subscription costs: While the software can be deployed at no charge, ongoing AWS support for EKS Anywhere requires an Enterprise Support plan plus an EKS Anywhere subscription, which can add significant annual cost depending on the number of clusters.
• Operational responsibility remains: As self-managed offerings, teams still handle on-premises tasks such as cluster upgrades, node scaling, right-sizing, and security hardening, which adds operational overhead compared with the fully managed cloud services.
• Infrastructure and configuration constraints: EKS Anywhere requires compatible on-premises infrastructure such as bare metal, vSphere, or CloudStack and restricts control plane customization, and ECS Anywhere requires installing the SSM Agent and ECS Anywhere agent on each registered server.

8. AWS Wavelength

AWS Wavelength brings AWS compute and storage services into telecommunications providers’ data centers at the edge of 5G networks. Wavelength Zones are deployments inside carrier networks that connect back to a parent AWS Region, so traffic from 5G devices can reach applications without leaving the telecom network. It targets applications that need very low latency, such as edge machine learning inference, gaming, and live video. It is built on the AWS Nitro System and is designed to help meet data residency and security requirements. Developers use familiar AWS services and APIs to build and deploy Wavelength workloads.

Source: Amazon

Key features include:

• Compute and storage at the 5G edge: Wavelength embeds AWS compute and storage in carrier data centers so applications run close to 5G end users. Traffic from devices reaches Wavelength Zone resources over the carrier network, reducing the number of network hops.
• Connection to a parent Region: Each Wavelength Zone is associated with a parent AWS Region and uses the same AWS services and APIs. Latency-sensitive components run at the edge while other components and data persistence run in the Region.
• Data residency and security design: Wavelength keeps data within specified geographic boundaries to help meet legal and regulatory requirements and is built on the AWS Nitro System as a sovereign-by-design security boundary. It meets industry security standards and certifications.
• Low-latency application support: The service supports use cases such as ML inference at the edge, gaming, live video production, and real-time analytics, using high-speed telco connectivity alongside AWS compute and storage.
• Familiar tooling: Developers use the same AWS tools for automation, deployment, security, and operations as in the cloud, supporting telecom, finance, public sector, healthcare, and gaming workloads.

Limitations (based on publicly available sources):

• Carrier and geographic constraints: Wavelength is only available where AWS has partnered with telecom providers, and its low-latency benefit applies mainly to devices on that metro and carrier network.
• Limited services and instance types: Wavelength Zones offer a subset of AWS services and a limited range of EC2 instance types and storage options compared with a full Region, with persistent and management services largely handled in the parent Region.
• Networking complexity: Connectivity requires a carrier gateway and uses subnet and routing patterns that differ from standard AWS networking, and ingress behavior can vary by carrier.