Azure Cosmos DB Pricing Guide (2025)

If you’ve explored Azure Cosmos DB, you’ll have noticed that its pricing model isn’t a simplistic “one-size-fits-all” approach. Instead, it offers flexibility, albeit with added complexity.

Whether you’re developing a globally distributed application or tinkering with smaller datasets, grasping how Cosmos DB structures its charges for compute, storage, and bandwidth can help you avoid unexpected costs on your Azure bill.

This article will dissect the pricing models of Cosmos DB, compare them, and provide tips to help you optimise your expenses.

1. Understanding Charges: How Azure Cosmos DB Prices its Services

Cosmos DB pricing hinges on three core elements:

1. Compute (Throughput) – This reflects the number of Request Units per second (RU/s) that you utilise or reserve.
2. Storage – This encompasses the amount of data you store, including indexes and backups.
3. Bandwidth – This involves data transferred out of Azure or between regions.

2. Getting to Grips with Request Units (RUs)

Before we dive into the various pricing tiers, it’s crucial to understand Request Units (RUs).

• Every read, write, or query operation you execute consumes RUs.
• A simple point read (1KB) could cost as little as 1 RU.
• A more intricate query, complete with filters or aggregates, could consume hundreds of RUs.

💡 Consider RUs as the currency of Cosmos DB — the greater the complexity or volume of your tasks, the higher the RUs consumed.

3. Exploring Compute Pricing Models

Azure provides three primary ways to cover compute costs:

a) Provisioned Throughput (Manual)

You reserve a certain number of RU/s, billed hourly — regardless of whether you’re fully utilising them.

• Best for: Consistent workloads with predictable traffic.
• Example Cost (West Europe, 2025): 100 RU/s → around £5.84/month (single-region, no zones).

💡 If you provision 10,000 RU/s but only use 5,000, you’re still on the hook for the entire 10,000.

b) Autoscale Provisioned Throughput

You designate a maximum RU/s limit, and Azure automatically scales between 10% and 100% of that threshold, charging based on the peak RU/s used in an hour.

• Best for: Workloads that experience spikes (like e-commerce during promotions).
• Example Cost: 100 RU/s max → approximately £8.76/month (about 50% more than manual provisioning).

💡 This option is excellent for unpredictable workloads, although it’s slightly pricier than fixed throughput.

c) Serverless

No pre-provisioning required; you pay solely for the actual RU consumption alongside your storage costs.

• Best for: Low-traffic applications, prototypes, or batch jobs.
• Example Cost: £0.25 per 1 million RUs consumed + £0.25 per GB stored.

⚠️ A minimum storage fee usually applies (~5GB), which means even with minimal activity, anticipate a charge of around £1.25–£2/month.

4. Understanding Storage Costs

• Charges accrue per GB each month.
• This includes data, indexes, and backups.
• Storage fees are applied per container in each region (important if you replicate data across areas).

💡 If you operate multi-region writes, remember that storage costs will multiply based on the number of regions.

5. Bandwidth Pricing

• Fees apply for data egress (data leaving Azure).
• Replication across regions incurs additional bandwidth costs.
• Intra-region reads and writes are free of charge.

💡 For applications serving users worldwide, factor in bandwidth costs along with storage and compute expenses.

6. Free Tier Options

Azure provides a Free Tier for Cosmos DB:

• 1,000 RU/s of provisioned throughput
• 25 GB of storage
• Free for the life of one Cosmos DB account per subscription.

This setup is ideal for development or small production applications — just keep in mind that egress fees still apply.

7. Benefits of Reserved Capacity Discounts

If you anticipate consistent workloads and can commit for 1–3 years, Reserved Capacity can reduce your provisioned throughput costs by as much as 63%.

⚠️ This only applies to provisioned throughput and doesn’t affect serverless options or bandwidth fees.

8. Pricing Comparison at a Glance

*Sample costs are recorded for single-region West Europe, 2025.

9. Tips for Optimising Costs

1. Tailor your RU/s – Utilize Azure Monitor to track usage and make adjustments.
2. Use Autoscale judiciously – Avoid setting unnecessary high maximum RU/s.
3. Go Serverless for sporadic workloads — Especially when usage is below 25% of the time.
4. Activate TTL (Time to Live) – Automatically clear old data to conserve storage.
5. Consolidate containers – Reduce the overhead of per-container storage costs.
6. Capitalize on the Free Tier – Even in production for minor applications.

Final Thoughts

Azure Cosmos DB offers unparalleled performance as a globally distributed database, but navigating its pricing model can be daunting if you’re unfamiliar with RUs, throughput modes, and potential hidden costs like bandwidth.

The crux is selecting the most suitable model for your workload patterns:

• Steady Workloads – Opt for Provisioned Throughput
• Spiky Workloads – Choose Autoscale
• Infrequent Use – Select Serverless
• Minimal Requirements – Use the Free Tier

By closely monitoring usage and adjusting over time, you can maintain predictable costs while ensuring fast, dependable performance for your users.

Strategies for Azure Cosmos DB Optimisation

Adjusting Provisioned Throughput

• Ongoing Monitoring – Keep an eye on essential performance metrics such as RU consumption, latency, and request rates. Use Azure Cosmos DB metrics to derive insights into your usage trends and spot areas for enhancement.
• Adjust RUs based on workload analysis – Gain an understanding of your application’s workload patterns. Avoid over-provisioning during quieter periods by identifying peak usage times and shifts in demand.
• Take advantage of the auto-scale feature – This feature dynamically adapts provisioned throughput to match real-time requirements. It scales up during peak times for optimal performance and reduces costs during lower usage periods.

Maximising Storage Efficiency

• Implement Time-to-Live (TTL) policies – Apply TTL for documents that only have a limited lifespan. This will ensure that outdated data is automatically removed, minimising stored data costs.
• Identify Unused Data – Investigate your data usage patterns to locate documents or records that are accessed infrequently. Consider options such as archiving or deleting this data to free up storage.
• Use Data Compression – Assess the various compression algorithms offered by Azure Cosmos DB. Opt for those that balance compression efficiency with processing demands, consequently lowering storage costs without hampering query performance.

Reducing Data Transfer Expenses

• Strategically Choose Regional Pairing – Select Azure regions wisely, taking into account where your users are located. When configuring Cosmos DB, prioritise regions that have lower data transfer fees.
• Utilise Azure Data Factory for Data Movement – Use Azure Data Factory’s Data Flow activities to simplify complex data transformations and enhance overall efficiency. Scrutinise the pricing tiers of Azure Data Factory to find the plan that best meets your data movement needs and budget constraints.

How to Optimise Azure Cosmos DB Expenses Using Native Tools

Estimate Your Workload Costs with the Capacity Planner

Use the Azure Cosmos DB capacity calculator to forecast the costs associated with your workload before creating the resource. This can assist you in making informed decisions regarding resource provisioning, scaling, and selecting cost-effective configurations.

For a high-level estimate of your intended workload for Azure Cosmos DB, follow these steps:

• Access the Azure Cosmos DB capacity planner website.
• Fill out the required details about your workload.
• Select Calculate.

The Cost Estimate table will show the total expenses for both storage and provisioned throughput. To see a detailed breakdown of throughput needed for different Create, Read, Update, and Delete (CRUD) operations and queries, click on the Show Details link. Remember to hit Calculate after changing any field to update your anticipated costs quickly.

Review Costs Using Azure Cost Analysis

Azure Cost Analysis empowers users to visualise their Azure Cosmos DB expenses in tabular and graphical formats over various timeframes, providing a comprehensive cost breakdown. This helps users understand how their resources contribute to overall expenses.

To review your Azure Cosmos DB costs in Cost Analysis:

• Log into your Azure portal.
• Navigate to your subscription’s Cost Analysis section in the menu.
• Select Add filter, choose Service name, and then select “Azure Cosmos DB” to view associated costs.

Here’s an example showing expenses specifically related to Azure Cosmos DB, along with forecast costs based on historical spending.

Establish Budgets Using Azure Cost Management

To optimise expenses associated with Azure Cosmos DB, consider creating budgets in Azure Cost Management. Set spending limits, receive alerts when approaching thresholds, and focus your budget on Azure Cosmos DB by filtering for that service. Adjust your budgets based on insights gathered, encouraging accountability and facilitating strategic resource allocation. This proactive approach aids in efficient usage and helps avert unforeseen cost overruns.

Steps for Budget Creation

1. Access your Azure subscription’s Cost Management section.
2. Go to the Budget section.
3. Select the Add budget option.

Potential Challenges in Cost Optimisation Using Native Tools

Complex Pricing Structures

The diverse pricing framework of Cosmos DB which includes provisioned throughput, storage, and data transfer presents challenges in accurately interpreting costs. The distinct pricing models across service components contribute to this complexity, making it difficult to navigate and optimise expenses. Users must make informed decisions regarding allocations and utilities within this complicated pricing landscape.

Challenges with Provisioned Throughput

Distributing provisioned throughput effectively at the container level can be a struggle due to varying workload requirements across different tables and containers. Poor allocation can negatively affect both database performance and costs, leaving users tasked with the challenge of balancing efficiency and expenses while managing and distributing provisioned throughput across different entities within Cosmos DB.

Monitoring Challenges

Monitoring performance, usage, and costs effectively across a distributed Cosmos DB with data spread across multiple regions can be daunting. Identifying optimisation opportunities requires comprehensive oversight, real-time analysis, and a deep understanding of data distribution, demanding a resource-intensive strategy for extracting insights and optimising performance.

Reduce Azure Cosmos DB Costs with Turbo360’s Cost Analyzer

Consider a software development firm with several teams managing various projects using Azure Cosmos DB, featuring numerous tables and containers across different subscriptions. The company noted a steady rise in Azure Cosmos DB costs and is keen to optimise its expenses.

Let’s explore how Turbo360’s Azure Cost Analyzer includes features that assist in reducing these costs:

Watch the use case video on “Spike in Azure storage costs – Monitoring and troubleshooting”

Transparent Cost Management

Utilise the Cost Analyzer to group subscriptions by team, adjusting filters at the group level for precise cost visualisation and efficient tracking of Azure Cosmos DB expenses.

Identifying Cost Variance

Cost Analysis allows for a comparison of Azure Cosmos DB resource spending across different time periods, highlighting variances that can help in identifying anomalies and understanding spending trends.

Pay for Actual Usage through Optimisation

Manually scaling down throughput for Cosmos containers, tables, and databases across subscriptions can be laborious, especially during off-hours. The Cost Analyzer’s optimisation feature simplifies this process by listing each container, table, and database within the Cosmos account. By scheduling downgrades during non-business hours, users can ensure that resource scaling is aligned with actual usage, preventing budget overruns.

Rightsizing Throughput and Spotting Idle Resources

The Rightsizing feature within the Cost Analyzer provides recommendations based on actual resource usage for every container, table, and database in your Cosmos account. It identifies possibilities for adjusting throughput and recognises idle resources that can lead to cost savings.

Conclusion

Optimising costs associated with Azure Cosmos DB is a vital strategic undertaking that extends beyond immediate financial savings. It plays a crucial role in an organization’s competitiveness, adaptability, and ability to foster innovation over time. Turbo360’s Cost Analyzer offers powerful functionalities like Cost Analysis, Monitoring, and Optimisation, enabling effective cost management, the identification of savings opportunities, and the overall optimising of Azure Cosmos DB expenses.