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AWS Lambda vs Cloud Functions for Firebase: What are the differences?
Introduction
AWS Lambda and Cloud Functions for Firebase are two popular serverless computing platforms. Both platforms offer the ability to run code without the need to provision or manage servers. However, there are key differences between AWS Lambda and Cloud Functions for Firebase that are worth considering when choosing a serverless platform for your application.
Pricing: AWS Lambda offers a pay-as-you-go pricing model, where you are charged based on the number of requests and the duration of each request. On the other hand, Cloud Functions for Firebase has a usage-based pricing model, where you are charged based on the number of invocations, compute time, and network egress. The pricing structure and rates for each platform can significantly impact the cost of running your serverless functions.
Vendor lock-in: AWS Lambda is a service provided by Amazon Web Services (AWS), while Cloud Functions for Firebase is part of Google Cloud Platform (GCP). Choosing one platform over the other may result in vendor lock-in, as each platform has its unique set of features and integrations. Consider the ecosystem and services provided by each platform to ensure it aligns with your project requirements and the larger technology stack.
Language support: AWS Lambda supports a wide range of programming languages, including JavaScript, Python, Java, C#, and more. Cloud Functions for Firebase, on the other hand, primarily supports JavaScript. While you can use other languages with Cloud Functions for Firebase, it requires additional configuration and setup. The language support provided by each platform is important to consider depending on your team's skillset and the compatibility with your existing codebase.
Trigger options: Both AWS Lambda and Cloud Functions for Firebase offer a variety of trigger options, such as HTTP requests, events from cloud services, and database changes. However, Cloud Functions for Firebase has better native integration with Firebase services like Firestore and Realtime Database, making it easier to trigger functions based on changes in your Firebase data. AWS Lambda, on the other hand, provides integrations with a wider range of AWS services and can be used in more general-purpose scenarios.
Runtime environment: AWS Lambda allows you to define the runtime environment for your functions, providing more flexibility in terms of choosing the specific version of the language runtime and any required dependencies. Cloud Functions for Firebase, on the other hand, uses a managed runtime environment that abstracts away the infrastructure details, allowing you to focus solely on writing the function logic. Depending on your requirements, the level of control over the runtime environment may influence your choice of platform.
Community and documentation: Both AWS Lambda and Cloud Functions for Firebase have active communities and extensive documentation resources. However, AWS Lambda has a larger and more mature community with a wide range of third-party libraries, developer tools, and resources available. This can be beneficial if you require advanced features or if you need assistance from the community to solve specific challenges.
In Summary, AWS Lambda and Cloud Functions for Firebase differ in pricing models, language support, trigger options, runtime environments, and the size of their respective communities. Consider these differences when choosing a serverless platform for your application, as they can impact the cost, flexibility, ease of development, and availability of resources and community support.
Need advice on what platform, systems and tools to use.
Evaluating whether to start a new digital business for which we will need to build a website that handles all traffic. Website only right now. May add smartphone apps later. No desktop app will ever be added. Website to serve various countries and languages. B2B and B2C type customers. Need to handle heavy traffic, be low cost, and scale well.
We are open to either build it on AWS or on Microsoft Azure.
Apologies if I'm leaving out some info. My first post. :) Thanks in advance!
I recommend this : -Spring reactive for back end : the fact it's reactive (async) it consumes half of the resources that a sync platform needs (so less CPU -> less money). -Angular : Web Front end ; it's gives you the possibility to use PWA which is a cheap replacement for a mobile app (but more less popular). -Docker images. -Kubernetes to orchestrate all the containers. -I Use Jenkins / blueocean, ansible for my CI/CD (with Github of course) -AWS of course : u can run a K8S cluster there, make it multi AZ (availability zones) to be highly available, use a load balancer and an auto scaler and ur good to go. -You can store data by taking any managed DB or u can deploy ur own (cheap but risky).
You pay less money, but u need some technical 2 - 3 guys to make that done.
Good luck
My advice will be Front end: React Backend: Language: Java, Kotlin. Database: SQL: Postgres, MySQL, Aurora NOSQL: Mongo db. Caching: Redis. Public : Spring Webflux for async public facing operation. Admin api: Spring boot, Hibrernate, Rest API. Build Container image. Kuberenetes: AWS EKS, AWS ECS, Google GKE. Use Jenkins for CI/CD pipeline. Buddy works is good for AWS. Static content: Host on AWS S3 bucket, Use Cloudfront or Cloudflare as CDN.
Serverless Solution: Api gateway Lambda, Serveless Aurora (SQL). AWS S3 bucket.
Netlfiy Functions uses AWS Lambda under the hood, but Netlify adds some nice sugar. The biggest advantage is the local development experience with netlify-cli. This allows you to run your functions locally with local configuration or pull configs from the Netlify dashboard. I built a health-check endpoint in about 2 minutes, and my send-email function in less than an hour.
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.
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
Pros of AWS Lambda
- No infrastructure129
- Cheap83
- Quick70
- Stateless59
- No deploy, no server, great sleep47
- AWS Lambda went down taking many sites with it12
- Event Driven Governance6
- Extensive API6
- Auto scale and cost effective6
- Easy to deploy6
- VPC Support5
- Integrated with various AWS services3
Pros of Cloud Functions for Firebase
- Up and running4
- Multi-region1
- Affordable1
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Cons of AWS Lambda
- Cant execute ruby or go7
- Compute time limited3
- Can't execute PHP w/o significant effort1
Cons of Cloud Functions for Firebase
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What is AWS Lambda?
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