Alternatives to Knative logo

Alternatives to Knative

Kubeless, Kubernetes, OpenFaaS, Fission, and Google Cloud Functions are the most popular alternatives and competitors to Knative.
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What is Knative and what are its top alternatives?

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
Knative is a tool in the Serverless / Task Processing category of a tech stack.
Knative is an open source tool with 3.4K GitHub stars and 712 GitHub forks. Here鈥檚 a link to Knative's open source repository on GitHub

Top Alternatives to Knative

  • Kubeless

    Kubeless

    Kubeless is a Kubernetes native serverless Framework. Kubeless supports both HTTP and event based functions triggers. It has a serverless plugin, a graphical user interface and multiple runtimes, including Python and Node.js. ...

  • Kubernetes

    Kubernetes

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...

  • OpenFaaS

    OpenFaaS

    Serverless Functions Made Simple for Docker and Kubernetes

  • Fission

    Fission

    Write short-lived functions in any language, and map them to HTTP requests (or other event triggers). Deploy functions instantly with one command. There are no containers to build, and no Docker registries to manage. ...

  • 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 ...

  • Istio

    Istio

    Istio is an open platform for providing a uniform way to integrate microservices, manage traffic flow across microservices, enforce policies and aggregate telemetry data. Istio's control plane provides an abstraction layer over the underlying cluster management platform, such as Kubernetes, Mesos, etc. ...

  • AWS Lambda

    AWS Lambda

    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. ...

  • Cloud Foundry

    Cloud Foundry

    Cloud Foundry is an open platform as a service (PaaS) that provides a choice of clouds, developer frameworks, and application services. Cloud Foundry makes it faster and easier to build, test, deploy, and scale applications. ...

Knative alternatives & related posts

Kubeless logo

Kubeless

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Kubernetes Native Serverless Framework
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PROS OF KUBELESS
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    CONS OF KUBELESS
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      related Kubeless posts

      related Kubernetes posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber | 34 upvotes 路 3.2M views

      How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

      Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

      Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

      https://eng.uber.com/distributed-tracing/

      (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

      Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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      Yshay Yaacobi

      Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

      Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

      After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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      OpenFaaS logo

      OpenFaaS

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      Serverless Functions Made Simple for Kubernetes and Docker
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        Fission logo

        Fission

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        Serverless Functions as a Service for Kubernetes
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        PROS OF FISSION
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          CONS OF FISSION
            No cons available

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            Google Cloud Functions logo

            Google Cloud Functions

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            A serverless environment to build and connect cloud services
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            Kestas Barzdaitis
            Entrepreneur & Engineer | 16 upvotes 路 356.9K views

            CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.

            CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

            AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

            It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

            The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

            In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

            Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

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            Tim Nolet
            Founder, Engineer & Dishwasher at Checkly | 6 upvotes 路 100.9K views

            AWS Lambda Serverless Amazon CloudWatch Azure Functions Google Cloud Functions Node.js

            In the last year or so, I moved all Checkly monitoring workloads to AWS Lambda. Here are some stats:

            • We run three core functions in all AWS regions. They handle API checks, browser checks and setup / teardown scripts. Check our docs to find out what that means.
            • All functions are hooked up to SNS topics but can also be triggered directly through AWS SDK calls.
            • The busiest function is a plumbing function that forwards data to our database. It is invoked anywhere between 7000 and 10.000 times per hour with an average duration of about 179 ms.
            • We run separate dev and test versions of each function in each region.

            Moving all this to AWS Lambda took some work and considerations. The blog post linked below goes into the following topics:

            • Why Lambda is an almost perfect match for SaaS. Especially when you're small.
            • Why I don't use a "big" framework around it.
            • Why distributed background jobs triggered by queues are Lambda's raison d'锚tre.
            • Why monitoring & logging is still an issue.

            https://blog.checklyhq.com/how-i-made-aws-lambda-work-for-my-saas/

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            Istio logo

            Istio

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            Open platform to connect, manage, and secure microservices, by Google, IBM, and Lyft
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            Anas MOKDAD
            Shared insights
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            Kong
            Istio

            As for the new support of service mesh pattern by Kong, I wonder how does it compare to Istio?

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            AWS Lambda logo

            AWS Lambda

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            Jeyabalaji Subramanian

            Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

            We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

            Based on the above criteria, we selected the following tools to perform the end to end data replication:

            We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

            We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

            In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

            Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

            In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

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            Tim Nolet
            Founder, Engineer & Dishwasher at Checkly | 20 upvotes 路 1.5M views

            Heroku Docker GitHub Node.js hapi Vue.js AWS Lambda Amazon S3 PostgreSQL Knex.js Checkly is a fairly young company and we're still working hard to find the correct mix of product features, price and audience.

            We are focussed on tech B2B, but I always wanted to serve solo developers too. So I decided to make a $7 plan.

            Why $7? Simply put, it seems to be a sweet spot for tech companies: Heroku, Docker, Github, Appoptics (Librato) all offer $7 plans. They must have done a ton of research into this, so why not piggy back that and try it out.

            Enough biz talk, onto tech. The challenges were:

            • Slice of a portion of the functionality so a $7 plan is still profitable. We call this the "plan limits"
            • Update API and back end services to handle and enforce plan limits.
            • Update the UI to kindly state plan limits are in effect on some part of the UI.
            • Update the pricing page to reflect all changes.
            • Keep the actual processing backend, storage and API's as untouched as possible.

            In essence, we went from strictly volume based pricing to value based pricing. Here come the technical steps & decisions we made to get there.

            1. We updated our PostgreSQL schema so plans now have an array of "features". These are string constants that represent feature toggles.
            2. The Vue.js frontend reads these from the vuex store on login.
            3. Based on these values, the UI has simple v-if statements to either just show the feature or show a friendly "please upgrade" button.
            4. The hapi API has a hook on each relevant API endpoint that checks whether a user's plan has the feature enabled, or not.

            Side note: We offer 10 SMS messages per month on the developer plan. However, we were not actually counting how many people were sending. We had to update our alerting daemon (that runs on Heroku and triggers SMS messages via AWS SNS) to actually bump a counter.

            What we build is basically feature-toggling based on plan features. It is very extensible for future additions. Our scheduling and storage backend that actually runs users' monitoring requests (AWS Lambda) and stores the results (S3 and Postgres) has no knowledge of all of this and remained unchanged.

            Hope this helps anyone building out their SaaS and is in a similar situation.

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            Cloud Foundry logo

            Cloud Foundry

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            Deploy and scale applications in seconds on your choice of private or public cloud
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