AWS Lambda VS Kernel-based Virtual Machine

• Scalability
  AWS Lambda automatically scales your application by running your code in response to each trigger. This means no manual intervention is required to handle varying levels of traffic.
• Cost-effectiveness
  You only pay for the compute time you consume. Billing is metered in increments of 100 milliseconds and you are not charged when your code is not running.
• Reduced Operations Overhead
  AWS Lambda abstracts the infrastructure management layer, so there is no need to manage or provision servers. This allows you to focus more on writing code for your applications.
• Flexibility
  Supports multiple programming languages such as Python, Node.js, Ruby, Java, Go, and .NET, which allows you to use the language you are most comfortable with.
• Integration with Other AWS Services
  Seamlessly integrates with many other AWS services such as S3, DynamoDB, RDS, SNS, and more, making it versatile and highly functional.
• Automatic Scaling and Load Balancing
  Handles thousands of concurrent requests without managing the scaling yourself, making it suitable for applications requiring high availability and reliability.

Possible disadvantages

• Cold Start Latency
  The first request to a Lambda function after it has been idle for a certain period can take longer to execute. This is referred to as a 'cold start' and can impact performance.
• Resource Limits
  Lambda has defined limits, such as a maximum execution timeout of 15 minutes, memory allocation ranging from 128 MB to 10,240 MB, and temporary storage up to 512 MB.
• Vendor Lock-in
  Using AWS Lambda ties you into the AWS ecosystem, making it difficult to migrate to another cloud provider or an on-premises solution without significant modifications to your application.
• Complexity of Debugging
  Debugging and monitoring distributed, serverless applications can be more complex compared to traditional applications due to the lack of direct access to the underlying infrastructure.
• Cold Start Issues with VPC
  When Lambda functions are configured to access resources within a Virtual Private Cloud (VPC), the cold start latency can be exacerbated due to additional VPC networking overhead.
• Limited Execution Control
  AWS Lambda is designed for stateless, short-running tasks and may not be suitable for long-running processes or tasks requiring complex orchestration.

• Open Source
  KVM is an open-source virtualization technology, making it freely available and highly flexible for users and developers to modify and adapt to specific needs.
• Linux Integration
  As a part of the Linux kernel, KVM benefits from seamless integration with Linux, resulting in improved performance and security due to consistent updates and comprehensive support from the Linux community.
• Scalability
  KVM supports a wide range of guest operating systems and can scale effectively to accommodate both small and large deployments, making it suitable for diverse environments and cloud infrastructure.
• Performance
  KVM, utilizing hardware virtualization extensions like Intel VT and AMD-V, provides near-native performance for virtualized environments, ensuring efficient resource usage.
• Security
  KVM leverages Linux security features, such as SELinux and cgroups, to provide robust isolation between virtual machines, enhancing security and reducing the risk of compromise.

Possible disadvantages

• Complexity
  KVM can be complex to set up and manage, especially for users unfamiliar with Linux environments, requiring a steep learning curve and potentially more time to configure correctly.
• Resource Overhead
  While generally efficient, some users may experience higher overhead compared to container-based virtualization, particularly for workloads that do not require complete hardware virtualization.
• Limited Support for Non-Linux Hosts
  KVM is inherently tied to the Linux kernel, meaning that its use on non-Linux hosts requires additional layers, which may complicate deployment and management efforts.
• Less Feature-Rich Compared to Some Competitors
  Some virtualization solutions, such as VMware, might offer more built-in enterprise features, requiring additional tools or configurations for KVM to match similar feature sets.
• Hardware Dependency
  KVM requires hardware with virtualization support (such as Intel VT or AMD-V), which could limit its use on older or budget hardware lacking these features.

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AWS Lambda Vs EC2 | Serverless Vs EC2 | EC2 Alternatives

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  AWS Lambda is suitable for applications with varying workloads and those already using the AWS ecosystem.

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Tracking Kernel-based Virtual Machine since Mar 2021.