AWS Knowledge
Understanding Google Cloud Monitoring Pricing & Costs
Piyush Kalra
Dec 11, 2024
When using the Google Cloud Platform together with Cloud Monitoring to manage your operations, knowing how much to pay for everything is, at times, difficult to figure out. The lack of a proper scope can lead to paying out more than expected over time.
If you are a cloud connoisseur, if you work in a DevOps team, or if you are a professional FinOps consultant, then this will be helpful to you as this guide unravels Google Cloud Monitoring's pricing scheme, explaining exactly how it works. You'll learn cost-cutting and effective strategies for a better overall performance. In most cases, with minimal cost optimization techniques, Google Cloud can be cheaper by 20-30% while still remaining as efficient as before.
What You’ll Learn
A breakdown of Google Cloud Monitoring pricing models and components
Cloud Cost optimization strategies to save money without sacrificing performance
What is Google Cloud Monitoring?
Google Cloud Monitoring is among the many products under Google Cloud’s suite of services, with the distinct feature of providing a real-time overview of the operations of your system by tracking, analyzing and recording any queries or other customized metrics through in-depth performance monitoring. You enable teams to keep track of their infrastructure and detect any potential problems almost instantly. It aids in helping an organization identify an issue via alerts and insights, allowing for a more engaging approach in combating the problem that may affect the end users or, more importantly, the end users.
Key Features & Benefits of Cloud Monitoring
Real-Time Metrics & Alerts: Monitoring and setting alerts for performance metrics such as the health of a system and the uptime of an API is necessary.
Custom Dashboards: Make use of these dashboards that can be customized to obtain a specific view of the environment you are working in.
Integrations: Integrates well into Google Cloud services, including Google Kubernetes Engine and Big Query.
Incident Management: Save time and resolve issues quicker while using related incident tracking tools.
Scalability: Effective reams of any shape and size easily grow along with the structure of the infrastructure.
How Does it Compare to Other Monitoring Tools?
AWS CloudWatch: A monitoring service provided by AWS with functionality similar to that of Google Cloud Monitoring, such as custom metrics, logs, alarms, etc. However, it is designed to work only in AWS environments due to its different pricing and integration.
Datadog: An effective multi-cloud capable monitoring tool that is not restricted to a single cloud provider. On the contrary, the tool is not well integrated with the Google Cloud Platform, within which it performs excellent monitoring capabilities such as seamless native integration.
Prometheus.io: A robust open-source monitoring solution that is highly adaptable, which means that it is great when it comes to scaling and customization of monitoring.
How Does Google Cloud Monitoring Work?
Google Cloud Monitoring aggregates data from various regions and incorporates it into real-time dashboards and metrics. Let us take a look at its architecture and working mechanism.
Key Components:
Metric Collection: Includes minimal hardware requirements and uses agents such as Ops Agent or built-in support for Prometheus to gather metrics for both system and applications.
Dashboards: Data is presented for the purposes of reconnaissance evaluation of infrastructure and application monitoring performance.
Alerts & Incidents – Set rules for conditions (e.g., CPU usage above 95%) to automatically notify the team.
API for Extensibility – Apply programming to automate monitoring activities and carry out integrations with third-party tools.
Deep Dive into Google Cloud Monitoring Pricing Structure
GCP Cloud’s pay-as-you-go pricing model allows users flexibility and convenience. However, knowing how the pricing structure is distributed and what enables it allows you to make cost allocations better.
Pricing Models
Pay-As-You-Go: Cost is incurred per MiB data ingested into a stream, stored and the number of API calls made. This pricing strategy suits firms that have ad hoc requirements.
Committed Use Discounts: Eases the payments by providing discount rates in return for being subscribed for 1 or 3 years. This is better for short-term businesses with predictable workloads.
Free Tier – A monthly free tier to cater for metrics ingested (150 MiB) and API requests (1 million requests)
Cost Breakdown for Data and Features
1. Data Ingestion Costs
General Metrics: If data is received within the limits of 150 MiB and up to 100,000 MiB a month, then there is a payment of $0.2580 per MiB. Additionally, as one moves past those levels, the cost incurred reduces.
Prometheus Metrics: Their cost starts at $0.06 per million samples. They are charged according to the number of samples which were ingested.
2. Storage Costs
Logs are stored for free for up to 30 days. If you need to retain logs for longer, you’ll pay $0.01 per GiB per month for extended storage.
3. Additional Features
Uptime Checks: The first 1 million uptime check executions are free. After that, each 1,000 executions costs $0.30.
API Calls: The first 1 million read calls are free. After that, every 1,000 read API calls are charged at $0.01.
Synthetic Monitors: You get 100 executions per billing account for free. After that, it costs $1.20 for every 1,000 executions.
Example Scenario: Cost for 10 Virtual Machines
Let’s say you’re monitoring 10 VMs, each generating 20 MiB of performance data monthly:
Total Metrics Data: 20 MiB * 10 VMs = 200 MiB/month.
Cost Calculation:
The first 50 MiB is part of the free tier.
You’ll pay $0.2580 per MiB for the remaining 150 MiB.
Total Cost: 150 MiB * $0.2580 = $38.70.
Case Study: Cleartrip's Move to Google Cloud
With the help of migrating to Google Cloud, Cleartrip was able to enhance its user experience and lower its expenses simultaneously as it used cloud monitoring for enhanced efficiency. Operating as a travel platform that enables the booking of flights, hotels, and trains, Cleartrip has two main requirements: high reliability and the capability to cater to traffic spikes.
Challenges: Having inadequate visibility of their data, Cleartrip encountered system bottlenecks and ineffective resource usage, which led to delays and increased expenses. There were low rates for scaling infrastructure during traffic bursts as well as transformation to the cloud facing internal organizational inertia.
Results with Cloud Monitoring:
Cost savings: Reduced operational costs by 10%.
Improved reliability: Around 90% of the production problems were solved through cloud monitoring.
Scalability: Seamlessly handled traffic surges.
Team efficiency: Full data visibility made deployments quicker.
Enhanced performance: End customer experience improved with lower latencies.
Tools and Tips to Cut Cloud Costs
Recommended Tools for Cloud Cost Management
Google Cloud Pricing Calculator: Consider the cost implications prior to making a decision regarding the functionalities of the configuration unit.
Cloud Billing Reports: Review patterns and configure the drill-down to the level of individual projects here.
Budget Alerting: Retrain any potential excess spending by incorporating alerts on budget overruns.
Metrics Diagnostics: Assess API consumption trends as well as key cost-affecting parameters using Cloud Monitoring.
Best Practices for Reducing Costs
Right-Sizing Metrics Collection:
Ensure maximal and minimal ingestion frequency is only performed where necessary (e.g. once every 120 seconds).
Do metric gathering in a way that saves more resources.
Use Prometheus for Kubernetes: Use the Managed Service for Prometheus for Kubernetes workloads and curb ingestion while scaling up massively.
Streamline Alerts: Data alerting should be avoided for raw data sets; instead, it should be a metric alert which can be useful and solve a purpose.
Leverage Free Tiers: Maximize usage of the 150 MiB free tier and free system metrics.
Conclusion
With Google Cloud Monitoring, high value is provided to corporations; however, its price mode, if not understood, can lead to expenses rising rapidly. In situations that require strategies that reduce unnecessary spending, such as reducing data reporting frequency, removing metrics not needed and using free tiers, cloud spending can be kept efficient and predictable.
For cloud enthusiasts, DevOps teams, and FinOps professionals, the primary question is not how to reduce the costs but how to enable the optimal allocation of resources needed for scalability, profitability and competitiveness.
Join Pump for Free
If you found this post interesting, consider checking out Pump, which can save you up to 60% off AWS for early-stage startups, and it’s completely free (yes, that's right!). Pump has tailor-made solutions to take you in control of your AWS and GCP spend in an effective way. So, are you ready to take charge of cloud expenses and maximize the most from your investment in AWS? Learn more here.
