Alternatives to Help Scout logo

Alternatives to Help Scout

Zendesk, Intercom, Apache Spark, Reamaze, and Jira Service Desk are the most popular alternatives and competitors to Help Scout.
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What is Help Scout and what are its top alternatives?

With best in-class-reporting, an integrated knowledge base, 50+ integrations and a robust API, Help Scout lets your team focus on what really matters: your customers.
Help Scout is a tool in the Help Desk category of a tech stack.

Top Alternatives to Help Scout

Help Scout alternatives & related posts

Zendesk logo

Zendesk

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The leading cloud-based customer service software solution.
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related Zendesk posts

related Intercom posts

Kirill Shirinkin
Kirill Shirinkin
Cloud and DevOps Consultant at mkdev | 12 upvotes 路 600.1K views

As a small startup we are very conscious about picking up the tools we use to run the project. After suffering with a mess of using at the same time Trello , Slack , Telegram and what not, we arrived at a small set of tools that cover all our current needs. For product management, file sharing, team communication etc we chose Basecamp and couldn't be more happy about it. For Customer Support and Sales Intercom works amazingly well. We are using MailChimp for email marketing since over 4 years and it still covers all our needs. Then on payment side combination of Stripe and Octobat helps us to process all the payments and generate compliant invoices. On techie side we use Rollbar and GitLab (for both code and CI). For corporate email we picked G Suite. That all costs us in total around 300$ a month, which is quite okay.

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Priit Kaasik
Priit Kaasik
Engineering Lead at Katana MRP | 7 upvotes 路 40.6K views

Sometimes #ad-blocking addons can cause a real headache when working with JavaScript apps. Onboarding assistants (Appcues + elevio ), chat (Intercom) and product usage insight (Hotjar) have all landed on their blacklists. I guess there is a perfectly good reason for this that I just don't know.

In order to fix this, we had to set up our own content delivery service. We chose Amazon CloudFront and Amazon S3 to do the job because it has a good synergy with Heroku PaaS we are already using.

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Apache Spark logo

Apache Spark

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Fast and general engine for large-scale data processing
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Eric Colson
Eric Colson
Chief Algorithms Officer at Stitch Fix | 19 upvotes 路 1.4M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

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Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 7 upvotes 路 665.4K views

Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

(Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

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Reamaze logo

Reamaze

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Helpdesk for sites and apps simplified
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PROS OF REAMAZE
    No pros available
    CONS OF REAMAZE
      No cons available

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      Jira Service Desk logo

      Jira Service Desk

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      It is a helpdesk request tracker
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      PROS OF JIRA SERVICE DESK
        No pros available
        CONS OF JIRA SERVICE DESK
          No cons available

          related Jira Service Desk posts

          FreshDesk logo

          FreshDesk

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          Refreshing the way thousands of help desk agents support their customers everyday, world wide
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          Front logo

          Front

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          Takes out the pain of shared inboxes (contact@, team@, jobs@...) by introducing collaboration in email
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          UserVoice logo

          UserVoice

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          Customer service and customer feedback tools that don't suck.
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