The question of Azure Synapse versus Snowflake is becoming more important as the world faces a massive surge in data. By 2025 global data will reach 175 zettabytes. Such rapid growth turns selecting the best data warehouse solution into both a technical and a financial choice.
When looking at Snowflake and Azure Synapse, their pricing models make them different. Snowflake uses a Pay-As-You-Go system that measures costs by the second. Azure Synapse, on the other hand, calculates compute costs. But pricing differences go beyond these payment methods. Azure Synapse Analytics is claimed to be 14 times faster than other cloud platforms and uses 94% less money to run.
This blogpost will break down the pricing of both platforms, reveal any hidden fees, and help you figure out which one could save your company the most, depending on how your team works and your needs.
Snowflake vs Azure Synapse: Cost Comparison Breakdown
To plan your budget, you need to understand the cost structures of top data warehouse tools. Each platform uses a different pricing method, and this affects your overall expenses.
Snowflake offers a pricing system based on usage. You pay for the storage and computing resources you actually use. It measures compute costs using Snowflake credits billed by the second, with a minimum of 60 seconds. So, if your query takes 3 minutes to finish, you are charged for those exact 3 minutes.
Unlike Snowflake, Azure Synapse bills its compute services. Even if your data warehouse runs for half an hour in a month, they will charge for a full hour. Their pre-purchase options begin at 5,000 Synapse Commit Units (SCUs) for $4,750 and go up to 260,000 SCUs costing $259,200.
Snowflake organizes its pricing into four editions, with each having a different credit cost:
- The Standard edition costs $2.00 per credit
- Enterprise edition costs $3.00 per credit
- Business Critical edition charges $4.00 per credit
- Virtual Private Snowflake (VPS) comes with custom pricing
Azure Synapse bases its pricing on several parts. These include SQL Pools (both serverless and dedicated), Apache Spark Pools, data storage, and data pipelines. For dedicated SQL pools, you pay for the compute power you set up even if you’re not using it all the time.
Storage fees vary quite a bit too. Snowflake asks $23.00 per TB each month for capacity storage and $50.00 per TB each month if you go with on-demand storage. Azure Synapse also charges $23.00 per TB for data storage, which counts the warehouse size and seven days of incremental snapshots.
Snowflake’s auto-suspend feature stands out because it pauses resources that are not being used. Azure Synapse also has a function to pause dedicated SQL pools when they are idle. However, things like serverless SQL pools and data lake storage keep adding costs even when not used.
Both platforms also give serverless options to handle irregular workloads, which can help lower expenses.
Azure Synapse vs Snowflake: Hidden Costs and Ways to Save
Apart from the listed pricing plans, both data warehouses bring certain hidden costs that could affect how much you end up spending.
Unexpected costs in processing and compute often appear with consumption-based models. Compute needs tend to rise during busy analytics times such as at the end of the month for reporting. These spikes drive noticeable jumps in expenses. When data is moved out of the platforms, both Snowflake and Azure Synapse apply egress fees. Snowflake charges about $0.02 per GB, and Azure Synapse charges closer to $0.09 per GB.
Adding business intelligence tools brings more costs. BI tools often have user-based pricing, so costs increase when teams grow. Onboarding and training also come with extra expenses since working with data warehouses needs a good grasp of the architecture and how to run queries.
Both options involve different cost factors. Azure Synapse bills even if you use it for less than an hour meaning you might pay for time you did not use. On the other hand, Snowflake uses per-second billing after the first minute, which could provide better control over expenses.
To reduce costs:
- Use auto-suspend settings: Snowflake’s auto-suspend stops warehouses when they sit idle. In Azure Synapse, you can pause dedicated SQL pools through synapse pipelines.
- Be smart about data transfers: Cut down on region-to-region data movements and think about using content delivery networks to handle popular data. This can help lower egress costs.
- Optimize the size of your resources: In Snowflake, change the idle auto-suspend time to 1 minute instead of leaving it at 5 minutes. This adjustment can save you 20 to 40 minutes of unused compute time every day. For Azure, go with serverless options if your workload is not constant.
A Gartner study in 2022 reported that Snowflake users experienced a 30% reduction in total costs over three years when compared to older data warehouses. However, the cost-effectiveness of a platform depends on the unique workload patterns of your business.
How to Pick a Data Platform That Fits Your Budget and Needs?
Choosing the right data platform means analyzing your organization’s requirements, not just its budget. The decision between Snowflake and Azure Synapse hinges on the size of your business and how it operates.
Snowflake works well for small and medium businesses. Its pricing model based on consumption, helps these businesses control spending. The architecture delivers strong performance with low delay, and businesses can adjust storage and compute. Since billing happens per second after the first minute, companies with on-and-off usage can save a lot.
Azure Synapse often suits bigger companies better. It handles large data loads, like terabytes and more offering great data warehousing solutions for managing huge datasets. Its analytics service brings together data warehousing and big data tools simplifying tasks in more complex setups.
Snowflake works well in areas such as:
- Managing big data warehouses to analyze retail trends
- Ensuring compliance with rules in the finance world
- Streamlining supply chains in manufacturing
Azure Synapse shines in other areas like:
- Breaking down massive telecom data for insights
- Running live analytics in streaming platforms
- Handling complex AI models in the healthcare industry
Companies using Microsoft tools already may find Azure Synapse handy since it connects with things like Power BI and Azure ML. On the flip side, Snowflake’s design works better for those wanting flexibility across multiple cloud platforms.
As businesses shift into hybrid cloud setups, 93% agree that having multi-cloud tools to analyze and manage data helps them adapt to changes. Since 89% are now using multi-cloud strategies using a platform that works across different environments becomes more important.
You should base your choice on how your business plans to grow. Smaller companies expecting fast expansion might find Snowflake helpful because it scales well. Bigger companies, on the other hand, might prefer Azure Synapse since it handles heavy complex analytics better.
Conclusion
The choice between Snowflake and Azure Synapse depends on what your data needs are and how your organization is set up. After looking at both options, it’s clear that each has strengths suited to specific situations. Deciding between them depends on how you plan to use the platform. Businesses with occasional analytical demands might find Snowflake’s consumption model more budget-friendly. On the other hand, companies that rely on steady computing power could gain by using Azure Synapse’s dedicated resources. Hope this blogpost can help you make the right decision between these two options. If you need assistance to deploy, manage and scale cloud based applications for optimal business growth, then connect with our experts to leverage our Microsoft Azure development services.
