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  1. Stackups
  2. Application & Data
  3. Serverless
  4. Serverless Task Processing
  5. AWS Lambda vs Zoho Catalyst

AWS Lambda vs Zoho Catalyst

OverviewDecisionsComparisonAlternatives

Overview

AWS Lambda
AWS Lambda
Stacks26.0K
Followers18.8K
Votes432
Zoho Catalyst
Zoho Catalyst
Stacks9
Followers24
Votes0

AWS Lambda vs Zoho Catalyst: What are the differences?

AWS Lambda vs Zoho Catalyst

AWS Lambda and Zoho Catalyst are two popular serverless computing platforms that offer various features and functionalities for developers. However, there are several key differences between these two platforms.

  1. Deployment and Scaling: AWS Lambda provides seamless deployment and scaling capabilities, allowing users to easily deploy their functions and automatically scale them based on the incoming request load. Zoho Catalyst, on the other hand, lacks autoscaling capabilities and requires manual configuration for scaling resources, which may be time-consuming and less efficient.

  2. Integration and Compatibility: AWS Lambda offers extensive integration with other AWS services, allowing developers to create complex workflows using different AWS services. Zoho Catalyst, on the other hand, has limited integration options and may not be as compatible with other external services or platforms.

  3. Pricing and Cost: AWS Lambda follows a pay-as-you-go pricing model, charging users based on the number of function invocations and the duration of each invocation. Zoho Catalyst, on the other hand, has a fixed pricing structure that may not be as cost-effective for applications with varying workloads or infrequent function invocations.

  4. Runtime Environments: AWS Lambda supports multiple runtime environments, including Node.js, Python, Java, and more, providing developers with the flexibility to choose their preferred programming language. Zoho Catalyst, however, has limited runtime options and may not support all programming languages, which can be a limiting factor for developers.

  5. Monitoring and Logging: AWS Lambda provides comprehensive monitoring and logging capabilities, allowing users to track function invocations, errors, and performance metrics using AWS CloudWatch. Zoho Catalyst, however, has limited logging and monitoring options, which may make it challenging to troubleshoot and optimize applications.

  6. Ecosystem and Community Support: AWS Lambda has a vast and active developer community, with extensive documentation, tutorials, and third-party libraries available for support. Zoho Catalyst, being a relatively newer platform, may have a smaller and less active community, which can affect the availability of resources and support for developers.

In Summary, AWS Lambda and Zoho Catalyst differ in terms of deployment and scaling capabilities, integration options, pricing models, runtime environments, monitoring and logging features, and ecosystem/community support. Developers should consider these differences to choose the platform that best suits their requirements and preferences.

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Advice on AWS Lambda, Zoho Catalyst

Tim
Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceonHerokuHerokuAWS LambdaAWS Lambda

When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

  • Developer Experience trumps everything.
  • AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
  • If you need geographic spread, AWS is lonely at the top.

The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

  • Pure, uncut, self hosted Kubernetes — way too much complexity
  • Managed Kubernetes in various flavors — still too much complexity
  • Zeit — Maybe, but no Docker support
  • Elastic Beanstalk — Maybe, bit old but does the job
  • Heroku
  • Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

  • Developer Experience / DX 🤓
  • Ops Experience / OX 🐂 (?)
  • Cost 💵
  • Lock in 🔐

Read the full post linked below for all details

357k views357k
Comments

Detailed Comparison

AWS Lambda
AWS Lambda
Zoho Catalyst
Zoho Catalyst

AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

It is a serverless development platform that helps developers build applications, APIs, microservices, and more with the help of serverless functions, databases, filestores, and other useful components and APIs.

Extend other AWS services with custom logic;Build custom back-end services;Completely Automated Administration;Built-in Fault Tolerance;Automatic Scaling;Integrated Security Model;Bring Your Own Code;Pay Per Use;Flexible Resource Model
Serverless Functions; Serverless Relational database; File storage; OCR, Object detection; Function orchestration
Statistics
Stacks
26.0K
Stacks
9
Followers
18.8K
Followers
24
Votes
432
Votes
0
Pros & Cons
Pros
  • 129
    No infrastructure
  • 83
    Cheap
  • 70
    Quick
  • 59
    Stateless
  • 47
    No deploy, no server, great sleep
Cons
  • 7
    Cant execute ruby or go
  • 3
    Compute time limited
  • 1
    Can't execute PHP w/o significant effort
No community feedback yet
Integrations
No integrations available
Node.js
Node.js
Java
Java

What are some alternatives to AWS Lambda, Zoho Catalyst?

Azure Functions

Azure Functions

Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.

Google Cloud Run

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Serverless

Serverless

Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

Google Cloud Functions

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

Knative

Knative

Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center

OpenFaaS

OpenFaaS

Serverless Functions Made Simple for Docker and Kubernetes

Nuclio

Nuclio

nuclio is portable across IoT devices, laptops, on-premises datacenters and cloud deployments, eliminating cloud lock-ins and enabling hybrid solutions.

Apache OpenWhisk

Apache OpenWhisk

OpenWhisk is an open source serverless platform. It is enterprise grade and accessible to all developers thanks to its superior programming model and tooling. It powers IBM Cloud Functions, Adobe I/O Runtime, Naver, Nimbella among others.

Cloud Functions for Firebase

Cloud Functions for Firebase

Cloud Functions for Firebase lets you create functions that are triggered by Firebase products, such as changes to data in the Realtime Database, uploads to Cloud Storage, new user sign ups via Authentication, and conversion events in Analytics.

AWS Batch

AWS Batch

It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted.

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