

Snowflake and Make compete in technology, focusing on data management and automation, respectively. Snowflake has the advantage in scalability and robust data management solutions, while Make is preferred for its flexibility in automation and seamless integration capabilities.
Features: Snowflake provides advanced data warehousing with features like time travel, scalable data processing, and robust security. Make offers an intuitive platform with strong integration flexibility, easy automation setup, and is particularly user-friendly for non-coders.
Room for Improvement: Snowflake needs enhanced cost management features and a simplified user interface. It could also improve stored procedures and real-time capabilities. Make could offer more developer-friendly features, reduce platform loading times, and increase security clarity.
Ease of Deployment and Customer Service: Snowflake primarily utilizes a public cloud setup with robust customer support but could improve response times and SLA clarity. Make offers both public cloud and on-premises options with positive feedback for its technical support and documentation.
Pricing and ROI: Snowflake's pricing is credit-based, offering pay-as-you-go flexibility, but it can be significant at scale. Users highlight potential ROI through efficient data handling. Make's pricing is competitive, appealing especially to smaller enterprises, providing a cost-effective option for automation tasks.
I have indeed seen a return on investment as it has saved us hundreds of hours in repetitive tasks, streamlining our follow-up to the leads that we are generating.
I implemented a booking system for my client that previously required data to be entered directly into Google Sheets and reminders to be sent manually; using Make, they have saved about 50% of their time, which equals one labor resource, translating to a significant amount of money saved.
With that extra time each month, I could focus more on sales and upscaling my business, so it is really worth it.
We have escalated a few issues that we faced during some integrations, and we received reasonable responses from Make support.
They were doing the best job for my use cases and my problems.
When I had a problem during the pricing payment, the customer support handled it very well.
We sought this documentation multiple times but faced difficulty in obtaining it.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
I am satisfied with the work of technical support from Snowflake; they are responsive and helpful.
When you have an error, it is very hard to do error handling and debugging.
It can handle increasing workloads or more complex automations easily, but I need to set up each and every component carefully.
Make's scalability is very good, and if the pricing were lower, I could scale a lot more.
Snowflake is very scalable and has a dedicated team constantly improving the product.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
Recently, Snowflake has introduced streaming capabilities, real-time and dynamic tables, along with various connectors.
Snowflake is highly stable and performs well even with large data sets exceeding terabytes, maintaining stability throughout.
Snowflake is very stable, especially when used with AWS.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
There should be clarity about whether the data is secure while passing through these automations or integrations created within Make.
I would love to have more detailed logs, step-by-step error tracing, and better visualization of failed executions, as I think it would improve the user experience significantly.
The lagging problem needs to be solved.
Enhancements in user experience for data observability and quality checks would be beneficial, as these tasks currently require SQL coding, which might be challenging for some users.
What things you are going with to ask the support and how we manage the relationship matters a lot.
If more connectors were brought in and more visibility features were added, particularly around cost tracking in the FinOps area, it would be beneficial.
Licensing was affordable.
I found a solution that allows me to use Make almost for free, just using the Docker on-premises.
It's cost-effective and it's pocket-friendly.
When it comes to cloud support, the setup cost is very cheap compared to other platforms, such as Oracle or PostgreSQL, which typically require higher costs.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
Make has positively impacted my organization by enabling us to solve use cases for hundreds of clients across hundreds of different platforms, providing the customization capabilities to automate accounting and invoicing processes that save dozens of man-hours a month, and allowing us to build custom churn, retention, and engagement costs that have driven a 30% reduction in churn.
Instead of spending several days implementing and testing API integrations inside our FastAPI back end, I was able to build the workflows in a few hours using Make.
The task that I would complete in a span of one day is completed in a matter of minutes by using Make.
We had a comparison with Databricks and Snowflake a few months back, and this auto-scaling takes an edge within Snowflake; that's what our observation reflects.
I have used the Snowflake Zero-Copy Cloning feature in the past while prototyping data in lower environments. This feature is helpful as it saves a lot of time during the data replication process.
Snowflake has contributed to significant cost savings.
| Product | Mindshare (%) |
|---|---|
| Make | 0.5% |
| Snowflake | 0.6% |
| Other | 98.9% |


| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 1 |
| Large Enterprise | 6 |
| Company Size | Count |
|---|---|
| Small Business | 30 |
| Midsize Enterprise | 20 |
| Large Enterprise | 61 |
Make is a robust automation platform that streamlines workflows, connecting apps to enhance productivity. Tailored for tech-savvy users, it offers dynamic automation solutions that optimize processes and facilitate seamless integration of disparate systems.
At its core, Make empowers businesses to automate tasks through an intuitive builder with drag-and-drop capabilities. Ideal for professionals who need to integrate systems efficiently, it supports a wide range of applications, aiding in the creation of complex workflows without the need for extensive coding. Users value its adaptability, making it a popular choice for enhancing operational efficiency.
What features does Make offer?In industries like retail and technology, Make has become essential for automating inventory management, order processing, and customer relationship tasks. Companies leverage its capacity to connect multiple databases, CRM systems, and sales platforms, driving growth and operational excellence.
Snowflake provides a modern data warehousing solution with features designed for seamless integration, scalability, and consumption-based pricing. It handles large datasets efficiently, making it a market leader for businesses migrating to the cloud.
Snowflake offers a flexible architecture that separates storage and compute resources, supporting efficient ETL jobs. Known for scalability and ease of use, it features built-in time zone conversion and robust data sharing capabilities. Its enhanced security, performance, and ability to handle semi-structured data are notable. Users suggest improvements in UI, pricing, on-premises integration, and data science functions, while calling for better transaction performance and machine learning capabilities. Users benefit from effective SQL querying, real-time analytics, and sharing options, supporting comprehensive data analysis with tools like Tableau and Power BI.
What are Snowflake's Key Features?
What Benefits Should You Look for?
In industries like finance, healthcare, and retail, Snowflake's flexible data warehousing and analytics capabilities facilitate cloud migration, streamline data storage, and allow organizations to consolidate data from multiple sources for advanced insights and AI-driven strategies. Its integration with analytics tools supports comprehensive data analysis and reporting tasks.
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