Azure Cosmos DB Pricing Guide (2025)

If you’ve ever looked into Azure Cosmos DB, you’ve probably realized that it’s not a “one-size-fits-all” pricing model. It’s flexible, but that flexibility comes with a bit of complexity.

Whether you’re building a globally distributed app or just experimenting with small datasets, understanding how Cosmos DB charges for compute, storage, and bandwidth will help you avoid surprises on your Azure bill.

In this post, we’ll break down Cosmos DB pricing models, compare them, and share tips to optimize costs.

1. The Basics: How Azure Cosmos DB Charges You

Cosmos DB pricing is built on three main components:

1. Compute (Throughput) – How many Request Units per second (RU/s) you use or reserve.
2. Storage – How much data you store (including indexes and backups).
3. Bandwidth – Data transfer out of Azure or between regions.

2. Understanding Request Units (RUs)

Before diving into pricing tiers, you need to know what Request Units (RUs) are.

• Every read, write, or query you run consumes RUs.
• A small point read (1KB) might cost 1 RU.
• A complex query with filters or aggregates might cost hundreds.

💡 Think of RUs as the currency of Cosmos DB — the more complex or larger your operations, the more RUs they consume.

3. Pricing Models for Compute (Throughput)

Azure gives you three main options for paying for compute:

a) Provisioned Throughput (Manual)

You reserve a fixed number of RU/s, billed hourly — even if you’re not using them.

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

💡 If you provision 10,000 RU/s and use only half, you still pay for 10,000.

b) Autoscale Provisioned Throughput

You set a maximum RU/s, and Azure automatically scales between 10% and 100% of that limit, charging for the highest RU/s used in an hour.

• Best for: Workloads with spikes (e.g., e-commerce during sales).
• Example Cost: 100 RU/s max → about $8.76/month (50% more than manual).

💡 Great for unpredictable workloads, but slightly more expensive than fixed throughput.

c) Serverless

No pre-provisioning. You only pay for the actual RU consumption, plus storage.

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

⚠️ Minimum storage billing applies (often ~5GB), so even with almost no activity, expect ~$1.25–$2/month.

4. Storage Costs

• Billed per GB per month.
• Includes data, indexes, and backups.
• Storage is billed per container per region (important if you replicate data).

💡 If you run multi-region writes, storage costs multiply per region.

5. Bandwidth Costs

• You’re charged for data egress (leaving Azure).
• Replication across regions also incurs bandwidth charges.
• Intra-region reads/writes are free.

💡 If your app serves global users, plan for bandwidth costs in addition to storage and compute.

6. The Free Tier

Azure offers a Free Tier for Cosmos DB:

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

Great for development or small production apps — just remember data egress still costs.

7. Reserved Capacity Discounts

If your workload is predictable and you can commit for 1–3 years, Reserved Capacity can save you up to 63% on provisioned throughput costs.

⚠️ Only applies to provisioned throughput — not serverless or bandwidth.

8. Quick Pricing Comparison Table

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

9. Cost Optimization Tips

1. Right-size your RU/s – Use Azure Monitor to track usage and adjust.
2. Leverage Autoscale wisely – Avoid setting max RU/s unnecessarily high.
3. Use Serverless for bursty workloads — Especially if active < 25% of the time.
4. Enable TTL (Time to Live) – Automatically delete old data to save storage.
5. Consolidate containers – Reduce per-container storage billing overhead.
6. Use the Free Tier – Even in production for small apps.

Final Thoughts

Azure Cosmos DB is a high-performance, globally distributed database, but its pricing model can be tricky if you’re not familiar with RUs, throughput modes, and hidden costs like bandwidth.

The key is to pick the right model for your workload pattern:

• Steady – Provisioned Throughput
• Spiky – Autoscale
• Infrequent – Serverless
• Tiny footprint – Free Tier

By monitoring usage and adjusting over time, you can keep costs predictable while still delivering fast, reliable performance for your users.

Strategies for Azure Cosmos DB optimization

Right-sizing provisioned throughput

• Continuous monitoring – Regularly observe the key performance metrics like Request Units (RU) consumption, latency, and request rates. Utilize Azure Cosmos DB metrics to gain insights into usage patterns and pinpoint potential areas for improvement.
• Adjust RUs by analyzing the workloads – Understand the workload patterns of your application. To ensure you are not over-provisioning during low-traffic periods, identify peak usage times and changes in demand.
• Utilizing auto-scale feature – The auto-scale feature dynamically adjusts provisioned throughput according to real-time demand. It increases during peak periods for optimal performance and decreases during periods of lower demand to reduce costs.

Optimizing storage usage

• Time-to-Live (TTL) policies – Enforce Time-to-Live (TTL) policies for documents with a limited lifespan. The automatic removal of expired data guarantees the prompt deletion of outdated information, reducing storage costs.
• Identify Unused Data – Analyze usage patterns of data to pinpoint documents or records with infrequent access. Evaluate options such as archiving or deleting unused data to release storage space.
• Implement Data compression – Evaluate various compression algorithms available in Azure Cosmos DB. Select algorithms that achieve a balance between compression ratios and processing overhead. Implementing data compression results in decreased storage costs without compromising query performance.

Minimizing data transfer costs

• Select Regional Pairing Wisely – Select Azure regions strategically by considering the geographical distribution of your user base. When configuring Cosmos DB, prioritize regions with reduced data transfer expenses.
• Utilizing Azure Data Factory for data movement – Utilize Azure Data Factory Data Flow activities to simplify intricate data transformations, streamline data movement processes, and enhance overall efficiency. Evaluate the pricing tiers of Azure Data Factory to select the tier that best fits your data movement needs and budget limitations.

How to optimize Azure Cosmos DB costs using the native tool?

Estimate your workload cost using the Capacity planner

Utilize the Azure Cosmos DB capacity calculator to predict the expenses associated with your workload before initiating the creation of the resource. This can help you make informed decisions about resource provisioning, scaling, and choosing cost-effective options.

Utilize basic mode when conducting a high-level estimate of a potential workload intended for Azure Cosmos DB. Here are the steps for using the capacity planner in basic mode:

• Go to the Azure Cosmos DB capacity planner website.
• Fill in the required details about your workload.
• Select Calculate.

The Cost Estimate table displays the overall expenditure for both storage and provisioned throughput. To view a detailed breakdown of the throughput needed for various Create, Read, Update, and Delete (CRUD) operations and queries, you can click on the Show Details link. Remember to select Calculate each time you modify the value of any field to update the estimated cost accordingly.

Monitor cost using Azure Cost Analysis

Azure Cost analysis allows users to view their Azure Cosmos DB costs in the form of tables and charts with different time intervals. It provides a comprehensive cost breakdown, enabling users to comprehend the contribution of their resources to the overall expenses.

To examine the costs associated with Azure Cosmos DB in Cost Analysis:

• Sign into your Azure portal
• Navigate to the subscription’s Cost Analysis section in the menu.
• To view the cost of the Azure Cosmos DB, select Add filter, select Service name, and choose “Azure Cosmos DB” as a service.

Here’s an example demonstrating expenses solely for Azure Cosmos DB and providing the forecast cost based on the historical spend of Azure Cosmos DB.

Create budgets using Azure Cost Analysis

To optimize Azure Cosmos DB expenses, create budgets in Azure Cost Management. Set spending limits, receive alerts on approaching thresholds, and focus on Cosmos DB costs by applying a service name filter (i.e. Azure Cosmos DB). Adjust budgets based on insights, promoting accountability and strategic resource allocation. This proactive approach ensures efficient usage and helps prevent unexpected cost overruns.

Here is an example showing the budget creation

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

Challenges in optimizing Azure Cosmos DB costs with the native tool

Complex Pricing Model

The pricing structure of Cosmos DB, which includes provisioned throughput, storage, and data transfer, is complex, posing challenges for accurate cost interpretation. Diverse pricing structures across service components contribute to this complexity, making it intricate to navigate and optimize resource expenses. Users are required to make informed decisions on allocation and usage within this intricate pricing environment.

Provisioned Throughput Complexity

Effectively distributing provisioned throughput in Cosmos DB at the container level is challenging due to diverse workload requirements across tables and containers. Inefficient allocation can adversely affect both database performance and costs. Users face the challenge of balancing performance and cost by effectively managing and distributing provisioned throughput across different entities within Cosmos DB.

Monitoring Overhead

Effectively monitoring performance, usage, and costs in distributed Cosmos DB, with data across regions, is challenging. Pinpointing optimization opportunities demands comprehensive oversight, real-time analysis, and a detailed understanding of data distribution. Managing the dynamic environment requires a resource-intensive strategy for efficient insights extraction and optimization.

Drive down your Azure Cosmos DB expenses with Turbo360’s Cost Analyzer

Consider a software development company with multiple teams working on different projects utilizing the Azure Cosmos DB with dozens of tables and containers spread out across different subscriptions and tenants the company has seen a steady increase in Azure Cosmos DB costs and wants to optimize the Azure Cosmos DB expenses.

Let’s see how Turbo360’s Azure Cost Analyzer provides features that help in reducing their Azure Cosmos DB expenses:

Usecase video on “Spike in Azure storage costs – Monitoring and troubleshooting”

Transparent Expense Management

In Cost Analyzer, organize subscriptions into groups for each team by adjusting the filters at the group level, enabling precise cost visualization and efficient tracking of Azure Cosmos DB expenses.

Identifying the cost variance

Cost Analysis enables the comparison of Azure Cosmos DB resource spending between different time ranges, revealing variances that help in identifying the anomalies and providing insight into the spending trends.

Pay for Actual Usage through Optimization

Manually scaling down throughput for Cosmos containers, tables, and databases across subscriptions is time-consuming, particularly during non-working hours. The Optimization feature in Cost Analyzer streamlines this process, listing each container, table, and database available in the cosmos account individually.

By creating a schedule to associate and scale down throughput during non-business hours, users ensure resources are appropriately scaled, aligning with actual usage, and preventing budget overruns.

Rightsizing Throughput and Identify Idle resource

The Rightsizing features of the Cost Analyzer offer recommendations based on resource usage for each container, table, and database in the Cosmos account. It suggests downgrading or upgrading throughput and identifies idle resources for potential cost reduction.

Conclusion

Azure Cosmos DB cost optimization is a strategic necessity that goes beyond immediate financial benefits, influencing an organization’s competitiveness, adaptability, and ability to innovate in the long run. Utilizing Turbo360’s Cost Analyzer provides organizations with capabilities like Cost Analysis, Cost Monitoring, and Cost Optimization. These features enable effective cost management, identification of areas for savings, and optimization of Azure Cosmos DB expenses.