Make

I started using Make  for a specific use case because I needed to create a workflow to upload videos using the API from YouTube. I decided to create an automation workflow to upload a new video every day without manual intervention.

My main use case for Make  is to create a workflow where I connect to Gemini to generate ideas, then trigger a video API to create a video from this prompt, and afterwards, use the Gemini API to create a title for the video.

Basically, I have a scheduler that starts every evening to facilitate how this automation works.

My main goal was to create an automation workflow without manual intervention, and this workflow creates and uploads a video to YouTube. From the same idea, there are different videos and different titles, and I do not write them down in Excel; everything is inside Make within this workflow. I was trying to reduce my manual effort.

The best features Make offers include its very visual interface, which stands out for me.

The visual interface provided by Make is valuable because it gives a global view and a global understanding of what comes after what.

My experience with Make has been a sour point because I thought it would be much easier to create, but I found it really tricky.

What made it challenging for me was dealing with the API keys and navigating between different locations; it should be from one platform, and for example, creating an API key should be one action instead of having to investigate where it is located.

I wish the connection to the API was much easier and smoother for how Make can be improved.

I chose a rating of seven out of ten because when you are trying to handle a complex workflow, the visual builder is great but very hard to manage. When I was trying to build a complex automation, I needed error handling, and it is really difficult to handle, which made it messy.

Actually, I did not finish this project with Make; I could not manage to finish the full workflow, and I decided to leave it as it is, implementing it a different way because it is very tricky.

Make has not been stable for me, and I did not appreciate it as much, mostly because of the problems I mentioned.

As CTO at MyDubai.io, I have been working for about a year, but with application development, I have worked for over a decade.

Make has not been stable for me, and I did not appreciate it as much, mostly because of the problems I mentioned.

When it comes to Make's scalability, I find that when you create a huge workflow, it is very difficult because it is very complex to create a complex workflow, and when you have an error, it is very hard to do error handling and debugging.

I did not reach out to any customer support team regarding Make.

I used a traditional solution like Python before using Make, which essentially created the same ideas that I wanted, just using traditional programming.

The idea to use Make came to me when I met it the first time, and after not finishing it successfully, the idea remained, but I ended up implementing it using traditional programs.

I downloaded the Docker  and installed Make on-premises in my organization.

I think the price is fair, but I found myself paying too much for every subscription; that is a different story. I found a solution that allows me to use Make almost for free, just using the Docker  on-premises, making pricing a consideration since it was free for me.

My advice to others looking into using Make is to start with a very simple workflow and not try to overcomplicate it; when you finish the simple workflow, you can add more to it, otherwise it will become messy very quickly, making it very difficult to handle errors.

I think overall Make is a really powerful tool, but there are definitely a few areas it could be improved based on my experience. My overall rating for Make is seven out of ten.

In automating any kind of user workflow or business process workflow, everything leads back to how data is being transferred, modified, or saved across systems while using Make  automations. For example, if we have our CRM  systems on monday.com  and we are using a Google Form to collect responses for a particular item, we use Make  to understand the responses and pass it properly into the monday CRM  and send relevant communications using email or text.

In our current organization, we have automated well over 600 processes including all the major workflows of student onboarding, student discontinuation, team member handling, tickets, and CRM manipulation. We have across 2,000 to 3,000 scenarios in the system, with over 500 to 600 workflows automated.

In terms of communicating between systems and between platforms, Make would be the primary tool of choice because instead of going about creating or writing API codes for multiple platforms, Make creates a seamless connection where modules come in and take care of most of the heavy lifting of code.

The visual workflow builder is something that stands out prominently, providing a no-code platform which connects across multiple different integrations and platforms. This means that even making changes to a particular platform can trigger a different functionality on a separate platform, so integrations definitely come into the picture. Additionally, the ability to create webhooks on the go is another particular feature that stands out.

Having an in-built or in-house data store to manipulate data and store it across multiple different scenarios is one more feature which particularly makes it stand out. The new work grid feature makes it visually very appealing to see how the automation workflow works.

Make has positively impacted my organization by reducing the number of hours that go into validating and verifying the flow of a particular repetitive process, which would otherwise take multiple hours, to just a few minutes to review for more than 1,000 or 2,000 case scenarios. Any fallback cases also go seamlessly into the incomplete executions DLQ for me to analyze and resolve.

The ability to have further customizations to certain functions and modules that are pre-existing may be something that could be improved. Regular updates to cover up any bugs or issues or proper repeated community asks is also essential.

Pricing is definitely something that is on a higher tier where we are consuming around 1.5 to 2 million operations per month, which makes the bill quite substantial. Keeping that in mind, there can be instances where the scenarios can be optimized properly to reduce operation count. One other thing that probably needs improvement is proper documentation of internal automations, which could be a tool that queries all the current scenarios and acts as a chatbot or helper to find certain values or parameters used across scenarios.

I have been using Make for about three to four years.

My advice to others looking into using Make is to go through the documentation first and go through the training and certification courses. Those are pretty detailed and almost cover all aspects that you would need to implement in an automation scenario. I would rate this product an 8 out of 10.

I have been using Make  for almost five to six months. My main use case for Make  is to automate certain tasks like social media posting and article posting on WordPress. As I work in FitPyo, we need to post blogs or articles on our website regularly, so rather than doing it manually, I created an automation workflow that fetches the posts and automatically publishes them to Facebook, Instagram, and other social media websites. Article posting is one of the major use cases for Make.

I decided to automate social media and article posting first due to the need to post tons of articles daily and to make the process really fast and effective. We use Make because we are a startup where many tasks need to be done on the go. I created an entire automation workflow so that articles can be fetched, rewritten with GenAI models, and posted on our website. The same applies to social media posting as we need to post frequently on Instagram and LinkedIn.

Regarding my main use case with Make, I am trying to automate WhatsApp messaging through Make for client interactions to facilitate direct replies. I am still figuring out how to do that. I am also considering posting a YouTube video through Make, although I feel Make is too complicated for that. I am also looking into using n8n  to see if it might be a better option.

The best features Make offers are its ease of use with drag and drop functionality. Compared to other automation tools like n8n , which is complicated, I find that Make is very simple. The standout features include ease of navigation and error checking, and I really enjoy the drag and drop feature personally.

I feel that if you are a beginner looking to get into automation, you should start with Make because it provides an environment where you can practice automation more flexibly, although integration and API can be a hindrance.

Make has positively impacted my organization by reducing our manual workflow and workload by almost twenty to thirty percent, which I feel is great. Many tasks that previously took time, such as article posting and writing, have seen a reduction of almost forty to forty-five percent in time. This allows our team to focus on innovating and developing our wearable products.

I feel that Make can be improved in terms of API handling and integrations. While n8n provides many integrations, Make has a limited set that can be enhanced for better performance.

I think error handling can also be improved, and the turnaround time should be addressed to enhance the user experience further.

I have been working with Make for almost six to seven months. To be precise, it would be almost seven months and ten to twenty days. I am very versed with Make and automation.

Make is stable, and I can confidently say that.

Make's scalability depends on the use case, but I find that it is scalable, especially for my needs.

Customer support for Make is good. I get responses and replies when I need assistance.

I have already shared a lot of information with you.

Make is deployed in my organization as a hybrid cloud solution.

I have seen a return on investment with Make, as the manual workflow is reduced by about ten to twelve people. Our turnaround time has improved by thirty to forty percent, which highlights the operational efficiency gained through automation.

My experience with pricing, setup cost, and licensing is that pricing is fine and not that expensive. I need to consider effectiveness in automation, and I feel everything is good to go without much to add.

I previously tested with Zapier , but I am not a fan of it. Make is a win for me.

Before choosing Make, I evaluated other options such as Zapier , but I found that Zapier was more suited for social media posting alone. I needed something in a broader format, and I feel Make can be trusted for that.

I am using the time saved through Make automation to develop my own line of products because, at the end of the day, product matters. A specific project arising from that extra capacity is the ability to brainstorm many ideas that can lead to long-term profits.

My advice for others looking into using Make is to tailor automation based on the use case rather than an organization's needs, focusing on effectiveness in automation. I would rate this review a seven out of ten.

I have been working in my current field for the last three years and have been using Make  for two months.

I have used Make  for two projects. The first project is medical case analysis, where if a patient uploads any case details, we handle that and provide solutions in three fields of a hospital: allopathy, homeopathy, and Ayurveda. Ayurveda is in Indian medical sciences, and from these three options, the patient can decide which one to choose and what tests are included.

In this medical case analysis project, I took five case studies of five different patients and translated the manual data using API to shift it to Google AI Studio. I made it there and used Make's JSON response from the output.

In my second project, which is about the UPSC exam, I converted PDFs given in manual format into HTML. I used data manipulations along with Make's built-in data parsers and regular expressions modules to analyze each question and integrate the options by iterators, making the process sequential.

The best features Make offers, in my experience, include the infrastructure of hosting and error handling, which I have found reduces many errors during projects. Additionally, it allows for secure and reliable API integration with Google AI Studio and provides visual debugging for the mock test data.

During my projects, the visual debugging helped me connect to AI Studio. JSON represents large data, which is converted into a shorter image that I can explain easily to patients.

I have two to three drawbacks I would like to add.

There is a file size constraint, as when I try to upload a PDF question bank of 200 pages, it shows an error and the file cannot process the data. The second drawback is the complex native app modules, as every single API does not integrate properly. I have tested some other API integrations, and they have not worked as expected. The third issue is that if I encounter one error, it halts the execution, which causes a delay.

These drawbacks impact my workflow as I have to separate the PDF into two or three parts due to the file constraints. For native app API integration, I use standard APIs like Google AI Studio, and for error handling, checking manually works better if just one error occurs. Additionally, I notice Make is heavily dependent on regular expressions. For example, if a question is written as 1Q, it sees it as one question.

I feel I have covered most of the points regarding improvements needed. I choose a rating of eight out of ten because I feel the file constraint part and native modules for API integration should be improved while making the workflow process. I do not have any other improvements needed for Make apart from the file constraint issue, which must be looked into, and the visual clutter issues when uploading more than three to five patients at once.

Make is stable in my experience.

Make's scalability is good.

When I had a problem during the pricing payment, the customer support handled it very well. I also received good responses when posting in the community forum while doing the project.

Previously, I have tried to use Zapier , which was very simple and linear, but I switched to Make because it provides visual representations that I find beneficial.

I have seen a return on investment, particularly in my second project where I converted PDFs into HTML format, saving costs on paper for approximately ten thousand questions and reducing time by about fifty percent. For patient case studies, I was able to present options in allopathy, homeopathy, and Ayurveda clearly and more quickly.

I have used the free version of Make and I find the pricing reasonable, especially with discounts available.

Before choosing Make, I evaluated Zapier , but after a friend's recommendation to use Make, the workflow has been very smooth for me.

Make saves me roughly forty-five percent to sixty percent of time since the analysis part and solution are provided below the question.

My advice to others looking into using Make is that you can create a very smooth workflow compared to others, and the visual representation is excellent for presenting data or any complex problems, especially for complex systems such as the neural, respiratory, and reproductive systems. The built-in modules make it easy to convert PDFs without needing to search for other modules to integrate.

I'm using Make  for integration with GoHighLevel, which is a CRM  tool. I integrate data from Google Sheets or the GHL CRM  to automate repetitive tasks. I use Make  nearly every day for whatever task comes my way, including reconciliation of bills and transaction tracking in Google Sheets, as well as GoHighLevel work. When I receive leads from Meta, I integrate those as well.

I have created an Airbnb scraper that extracts all details from Airbnb, including reviews and pinned locations, which are not very easy to obtain. I used Claude to create Python code, integrated it through Make, and used Apify . Make sends the command and prompt to Apify , which runs the code and scrapes all the details from Airbnb, then updates them in Google Sheets. Once any row is updated, Make automatically sends all details to every user. For example, when my name is entered in the Google Sheet, Make takes the name, email ID, company, and everything based on the flow I have configured, and sends the email automatically to that person. We have a company called Rental Abode, which is similar to Airbnb and we are building it now. I target them by scraping all details from Airbnb and sending it to them using Make.

Creating scenarios and sending them is really straightforward. I was doing manual tasks in the sheet before, and now Make makes everything easy for me. I can write the interior logic, whatever loops or anything I want, and whatever I was doing manually, Make now does it automatically. I only need to write one code and create a scenario, and based on that, it executes everything. This scenario is essentially a webhook, allowing integration from one site to any other site, which is quite interesting.

The data storage feature is also excellent. Whenever I'm using data, I can store all of it in Make, and whenever I need it, I can access it. The templates that Make provides are good, with everything built-in. I can use the data structure smoothly, and the custom app feature is also one of the best, as it's a good option for anyone who wants to explore.

The outcome is far better than before. Every employee used to work manually, and I also used to do manual tasks. Now Make does everything automatically. Whenever any task comes up, I'm always thinking about how to automate it with Make, which is something positive.

Sometimes the platform is too laggy and loads slowly. The credits are also getting used up too quickly, which takes too much credit. If the credits could be reduced, that would be more efficient.

The lagging problem needs to be solved. Sometimes it lags a lot with long workflows, taking too much time or giving errors like not running.

I have been working in my current field for more than two to three years.

As of now, Make is stable for me.

Make has scalability. Whenever a new task comes to mind, I think about automating it with Make, which is good. However, I see many cheaper competitors emerging, so understanding their features could help.

I have not reached out to customer support because I haven't encountered any issues that I couldn't solve.

I was doing a manual approach completely before Make.

The setup was easy. I completed everything related to pricing, setup costs, and licensing within two or three hours.

I was supposed to hire a business analyst, but due to Make, I realized it has this much capacity. I can do multiple tasks as a founder, but I don't have time. So I created automations, saving me around 7 to 8 lakh INR.

Earlier I used to spend too much time understanding the insights from my projects. Now I only spend four to five hours, but I can get everything in one hour or even 30 seconds. It's easy. After one click, it's pulling data from my database, updating in ChatGPT, and then analyzing and updating in the document. I can understand within five minutes what exactly is going on in the business and its direction.

I looked into Zapier , but it was more costly, so I chose Make.

If you want to automate everything, Make is the only thing you can use to integrate and automate all the platforms, especially if you don't want to work manually.