LogdyPro Cost Savings Calculator - Compare Log Management Solutions

What is your use case for storing logs?

Short-term retention

Long-term retention

Short-term retention is targeted for storing logs in a "hot" layer starting from few days up to 30 days. It heavily depends on the number of days you want to have logs available in the "hot" layer. The cost is the main driver here as the data can be queried with low latency and high throughput.

How many logs are you producing per day? 50 GB (1500 GB per month, 18000 GB per year)

Do you want to use your existing infrastructure to store and query logs?

Yes

Storage cost in $ per GB:

Object storage cost: AWS S3 is 0.023$ and Cloudflare R2 is 0.015$

Cloud solution cost in $ of GB of logs ingested:
Starting Up to
Typically depending on the provider and the exact solution, we took major cloud providers (GCP, AWS, Azure) and current logging market leaders (Datadog, Logz.io, Logit, Betterstack) into account

Savings simulation

LogdyPro

1 $/month 😃

Low cost
Fast ad-hoc queries
Data secure on premises

Self-hosted/open-source

760 $/month 😞

High operational complexity
Requires dedicated infrastructure

Cloud solution

300 - 1200 $/month 😞

Usually between 7-14 days of retention
Vendor lock-in
Additional cost for extended retention

Are you interested in how we calculated the amounts? Reach out to us.

Interested in using LogdyPro? Let's get in contact!

F.A.Q.

What is a hot and a cold storage layer?

In log management, a "hot layer" refers to recent, frequently accessed logs that require quick retrieval and analysis. These logs are typically stored on high-performance storage for fast access. A "cold layer" contains older, less frequently accessed logs that are moved to more cost-effective storage solutions like object storage. LogdyPro optimizes both layers, providing fast access to recent logs while maintaining cost-effective storage for historical data.

Why does LogdyPro have such a competitive price?

There are mostly two factors at play here: size reduction and runtime requirements.

The former is very straightforward: you generate 10GB of logs and LogdyPro reduces it's size to 50-100MB depending on the data. If you multiply that by months of retention, the numbers add up and the difference becomes noticeable.

Another aspect is that LogdyPro doesn't require large RAM memory to work, it mostly reads the data straight of the disk for every query. In some instances only an index file will be needed (which is usually between 0.75-1% of the original data size). In other instance LogdyPro is able to partially read the data from object storage like S3 thus it's possible to not store the data on a local disk and still be able to query it in a performant way! What the above means is that you don't have to maintain a fleet of servers that will be ready for queries.