Google Cloud uses a consumption-based pricing model that allows organizations to pay only for the resources they use. This flexibility is paired with multiple pricing options designed to support different workload types and usage patterns. With the pay-as-you-go model, users are charged per unit of resource—such as CPU time, memory, or storage—with billing typically calculated by the second or minute. This model suits dynamic workloads that vary in usage.
For more predictable workloads, Google Cloud offers Committed Use Discounts (CUDs). These allow users to commit to using specific resources (e.g., vCPUs, RAM) over a one- or three-year term in exchange for reduced rates—often yielding significant savings. Another built-in savings mechanism is Sustained Use Discounts (SUDs). These apply automatically when eligible services, like Compute Engine, run for a large portion of a billing cycle, providing up to 30% off without a commitment.
To support cost-sensitive or fault-tolerant applications, Google Cloud provides Spot VM Instances—heavily discounted virtual machines using surplus capacity. While cost-effective, these instances can be preempted at any time. For enterprises with large or unique workloads, custom discounts and pricing agreements are available through sales negotiation. This can include volume-based discounts or tailored billing structures.
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Google Cloud has a simple offer to get you started: a $300 spend-it-as-you-like credit. This offer expands if you share and verify a business email address.
This approach seems less generous than AWS' and Azure's, which offer free tier services for up to 12 months to new customers. However, Google Cloud combines its new customer treats with an "always free" usage threshold for certain services (as detailed here). Google Cloud also offers over 20 products that are free up to designated monthly thresholds.
Google Cloud offers a flexible, pay-as-you-go pricing model, which allows organizations to scale usage up or down based on demand. There are several pricing options available:
It is not the intention of this article to give you an overview of every service offered by Google Cloud or the Google Cloud marketplace; rather, we will discuss the more popular service offerings and how they are priced.
Google Cloud’s Compute Engine is part of its IaaS (Infrastructure as a Service), equivalent to AWS’ EC2 service. Google Cloud takes its pay-for-what-you-use very seriously, with all vCPUs, GPUs, and GB of memory charged in per-second increments, with a 60-second minimum.
You can opt for either of the models discussed above: pay-as-you-go or committed use. The Google Cloud even rewards the pay-as-you-go user with sustained use discounts. Even if you don’t commit and apply the committed use model, there are discounts of up to 30% for running specific Compute Engine resources for a significant portion of a billing cycle.
Pricing example:
If you run an e2-standard-2 virtual machine (2 vCPUs, 8 GB memory) in the Iowa (us-central1) region:
These prices exclude any additional costs like network egress, premium images, or attached disks.
Google Kubernetes (GKE) is the managed Kubernetes solution within Google Cloud. Kubernetes provides automated container orchestration and efficient machine management and also improves reliability and decreases the time and resources attributed to DevOps. Google is deeply connected with Kubernetes, having donated Kubernetes as the founding project of the Cloud Native Computing Foundation (CNCF). Google continues to support the CNCF.
Kubernetes Engine autoscaling handles increased user demand for your services, keeping them available when it matters most. GKE is a managed service, and you can easily install and configure High Available Kubernetes Cluster with that service. It is a robust and stable service, and makes Kubernetes upgrades simple, reducing overhead for DevOps teams.
Pricing example:
If you deploy a GKE Standard cluster with one e2-standard-4 node (4 vCPUs, 16 GB memory) in the Iowa (us-central1) region:
Prices exclude additional services like persistent disks, networking, or load balancers.
You can find detailed info for pricing on Google Kubernetes Engine pricing.
Cloud Storage is Google Cloud’s version of AWS S3. Buckets are charged per GB, and prices vary according to both the region they are in and the storage type, which may consist of standard, nearline, coldline, or archival storage.
Each storage type has a minimum charge, i.e., the expectation is that the data is stored for a minimum time, and that period raises a charge:
So, while you can delete, replace, or move an object before it has been stored for the minimum duration, you are charged as if the object was stored for that duration.
Of course, when applications rely on such data to function, then the data must be accessed. Such data will most typically be held in the “standard” or “nearline” tiers. The cost of accessing stored data varies by operation, with delete operations being free and other operations belonging to A- or B-grade pricing levels.
Note that the cloud bucket storage described is not the same service as a Google persistent disk. A persistent disk is a component that may be added to a virtual machine, i.e., a Compute Engine instance, as discussed above. A persistent disk can be a cheaper solution: if a virtual machine needs local data access or needs to share local data with other virtual machines. Also, a virtual disk provides lower latency for read/write operations.
Pricing example:
Storing 1 TB of data in a Standard storage bucket in the Iowa (us-central1) region for one month:
Switching to Nearline storage for the same 1 TB:
This highlights the trade-off between lower storage costs and retrieval or access fees in colder storage classes.
Google Cloud offers a VPC function that allows you to place Compute Engine instances in an isolated, secure network within its data center. In a VPC, ingress is free. However, if the ingress triggers an operation, for example, load balancing, then that operation may be charged.
When creating a VPC, you are connecting virtual machines, so ingress from one VM (virtual machine) equates to egress from another. The egress may be charged and charged per GB delivered. If you use the internal IP address of the devices, you are connecting within the same network, and no charges will be raised. Cross Google Cloud zones or regions, and charges will ensue, even if you are on the same subnet.
There are some egress scenarios that are free of charge, such as when a VM calls upon a Google service such as the oracle Google Maps, or the Google streaming platform, YouTube.
Other services that raise charges include:
These network services come in two tiers:
Not only are some services priced differently, those “always free” threshold limits do not apply to Standard Tier. There are some shared pricing strategies between the two tiers, for example, the price of IP addresses, instances, and forwarding rules.
Pricing example:
Suppose a Compute Engine VM in us-central1 sends 100 GB of data per month to another VM in us-east1:
Now add an external static IP:
If you enable packet mirroring or HTTP load balancing, those services incur additional charges depending on the traffic volume and configuration.
The Google Cloud's CDN pulls content from any HTTP-capable origin, including Compute Engine, Cloud Storage, and Google Kubernetes Engine backends. The origin point does not have to be within Google Cloud; it can access sources such as storage buckets in other clouds.
If you are serving up cacheable content, you pay for the cache lookup and cache egress in terms of bandwidth provided for HTTP/S requests. The egress charges will vary based on both the destination and usage. Usage is charged monthly per project per destination. The destination is determined by the client's IP address.
If the content is non-cacheable, then the charges are determined by the use case as it applies to (Cloud Storage or Compute Engine, as discussed above).
Most teams leveraging the CDN need to consider what the charges will be for:
If your content requirements are large (over 1 PB per month), you can discuss commitment-based reductions on rates with the Google Cloud sales team.
Pricing example:
Suppose you serve 500 GB of cached content per month from Cloud CDN to users in North America:
Add cache lookup charges:
If 100 GB of data is fetched from your origin (cache fill):
Total estimated monthly cost: $59.50
(assuming no other services like load balancing or additional backend operations)
Cloud SQL is a managed relational storage provided by Google Cloud that is used to store relational data. Cloud SQL will let you have a relational database in the cloud that is capable of handling gigabytes of data. There are other options that you should consider if you will be handling terabytes and petabytes of data.
Alongside the cost of the SQL instance itself, the following raise charges:
Pricing example:
Suppose you run a db-f1-micro instance (1 vCPU, 0.6 GB RAM) of MySQL in the us-central1 region for one month:
Total estimated monthly cost: ~$9.25
For a production setup with a db-n2-standard-2 instance (2 vCPUs, 8 GB RAM):
Total monthly cost: ~$312.22 (excluding backups and replica instances)
Cloud Spanner is a relational database that provides transactional consistency at scale.
Alongside the cost of the amount of compute capacity in your instance, the following raise charges:
Note that you are charged by instance, not by replica. Again, it is worth considering committed use to benefit from the discounts.
Pricing example:
Suppose you run a Cloud Spanner instance in the Iowa (us-central) region with 100 GB of database storage and 50 GB of backup storage. If your instance uses 1 node (equivalent to 1000 processing units) for an entire month and has no significant network egress, the monthly charges would break down approximately as:
For a small application, Cloud Datastore could function as a free option, thanks to the daily allocation of free read, writes, and deletes; egress; operations; and the 1 GB of storage. Once those thresholds are crossed, then charges will be raised, with the exception of “small” operations, which maintain the free tier:
Such charges are very sensitive to the region in which they occur.
Pricing example:
Assume your application is hosted in the Iowa (us-central) region and exceeds the daily free tier. In one month, it performs 300,000 document reads, 100,000 writes, and stores 10 GB of data. The monthly charges would look like:
Firestore can act like a front end to Datastore or use its own database. Again you benefit from a free allocation, and ingress is free after that, expect daily charges for:
Pricing example:
Suppose your app is running in the multi-region us-central location and, over a month, performs the following operations:
After subtracting the free tier, the monthly charges would be:
This shows how Firestore remains low-cost for moderate usage, especially when staying close to free tier limits.
Bigtable supports clusters that may be spread across regions/zones and duplicate nodes. Expect to be charged for:
Pricing example:
Suppose you run a production instance in the us-central1 region with a single cluster and 3 nodes, using SSD storage and 500 GB of stored data. You also replicate data to another region and incur 100 GiB of cross-region egress. Monthly charges would include:
Total: $1,520.50/month
BigQuery can automatically allocate computing resources on an as-needed basis. Alternatively, you can reserve compute capacity, i.e., virtual CPUs. The pricing structure of BigQuery reflects the design you apply. Adopting the reservation approach means that you will be billed a flat rate per month or per annum for that designated service.
Pricing example:
Assume you’re using the on-demand model in us-central1 and run 10 SQL queries per day, each scanning 5 GB of data, for a total of 300 queries per month. You also store 1 TB of active data.
Managing cloud costs effectively is essential to avoid unexpected charges and ensure long-term sustainability. Google Cloud provides a variety of tools and features that help teams monitor, control, and optimize their spending. The following best practices can help you make the most of your budget while maintaining performance and flexibility.