5 Ways to Reduce Observability Costs

Ram Kumar OSP
Published On :
May 3, 2023

Observability is a critical aspect of any modern software development and operation. It helps teams identify and troubleshoot issues quickly, improve application performance, and ensure their systems are reliable. However, observability can also be expensive, especially when you scale up your operations. In this blog, we'll explore five ways to reduce observability costs and how SnappyFlow can help.

Choose the Right Tool

The first step to reducing observability costs is choosing the right tool. There are many observability tools available, each with its own set of features and pricing models. To avoid overspending, it's important to understand your observability goals and choose a tool that fits your needs. Additionally, try to combine multiple requirements into a single tool to avoid paying for unnecessary features. For teams requiring an on-premise or self-hosted setup, look for low-footprint solutions that offer the right feature set to avoid heavy infrastructure costs.

Leverage Primary/Secondary Storage Features

Many tools provide something called hot and cold storage for ingested data. Some can even compress logs and store them in cold storage for longer durations. It is important to note that the logs stored in Secondary storage logs are available anytime and on-demand. SnappyFlow provides secondary storage logs on-demand with millisecond-level search performance, allowing teams to access logs quickly and efficiently.

Ingest Only What Matters

You don't need to monitor everything and ingest all data. Identify critical applications and dependencies and decide what data you need to bring into the system. Many observability tools charge for what you monitor or ingest, so look for solutions that offer a flat licensing fee or open-source solutions where you are in control of your license costs.

Set the Right Retention Policy

Retention policy is one of the most significant cost impacting elements when it comes to observability. Setting the right retention policy for logs, metrics, and traces will help reduce costs. A good starting point is logs for 7 days, metrics for 30 days, and traces for 1 day. However, the retention policy should be adjusted based on the level of historical data needed for observability.

Not all Users are Power Users

As the number of users for your observability tool increases, costs can escalate quickly. Look at your user's individual requirements and bucketize them into categories. Use role-based access controls (RBAC) to segregate users and provide access only as per their needs. This will help reduce costs and ensure that only authorized users have access to the observability tool.

How can SnappyFlow Help in Reducing Observability Costs?

SnappyFlow provides multiple product options, including SnappyFlow Cloud, SnappyFlow Self-Hosted Lite, and SnappyFlow Self-Hosted Turbo. The Cloud version is ideal for startups and enterprises alike. The Self-Hosted Lite solution is a low-footprint version that's perfect for small ingest sizes (up to 500 GB/day). The Turbo version is a full-scale version of the tool that can handle terabyte-scale ingest.

SnappyFlow also provides advanced RBAC controls to limit what each user can access, ensuring that costs don't escalate due to unnecessary user access. Additionally, SnappyFlow provides the ability to selectively ingest logs and store them in primary, high-performance storage and secondary, low-cost storages. Logs are compressed by up to 40% before they are stored in secondary storage, providing a much higher cost optimization.

In conclusion, reducing observability costsis critical for organizations to remain profitable and competitive. By choosing the right tool, leveraging primary/secondary storage features, ingesting only what matters, setting the right retention policy, and limiting user access, organizations can reduce observability costs significantly. SnappyFlow provides multiple product options and features that can help organizations reduce observability costs without compromising on performance and functionality.

What is trace retention

Tracing is an indispensable tool for application performance management (APM) providing insights into how a certain transaction or a request performed – the services involved, the relationships between the services and the duration of each service. This is especially useful in a multi-cloud, distributed microservices environment with complex interdependent services. These data points in conjunction with logs and metrics from the entire stack provide crucial insights into the overall application performance and help debug applications and deliver a consistent end-user experience.
Amongst all observability ingest data, trace data is typically stored for an hour or two. This is because trace data by itself is humongous. For just one transaction, there will be multiple services or APIs involved and imagine an organization running thousands of business transactions an hour which translates to hundreds of millions of API calls an hour. Storing traces for all these transactions would need Tera Bytes of storage and extremely powerful compute engines for index, visualization, and search.

Why is it required

To strike a balance between storage/compute costs and troubleshooting ease, most organizations choose to retain only a couple of hours of trace data. What if we need historical traces? Today, modern APM tools like SnappyFlow have the advantage of intelligently and selectively retaining certain traces beyond this limit of a couple of hours. This is enabled for important API calls and certain calls which are deemed anomalous by the tool. In most troubleshooting scenarios, we do not need all the trace data. For example, a SaaS-based payment solutions provider would want to monitor more important APIs/services related to payments rather than say customer support services.

Intelligent trace retention with SnappyFlow

SnappyFlow by default retains traces for
SnappyFlow by default retains traces for
HTTP requests with durations > 90th percentile (anomalous incidents)
In addition to these rules, users can specify additional rules to filter out services, transaction types, request methods, response codes and transaction duration. These rules are run every 30 minutes and all traces that satisfy these conditions are retained for future use.
With the built-in trace history retention and custom filters enabled, SREs and DevOps practitioners can look further to understand historical API performance, troubleshoot effectively and provide end-users with a consistent and delightful user experience.
Get in touch
Or fill the form below, we will get back!
Is SnappyFlow right for you ?
Sign up for a 14-day trial