As infrastructures and software grow in complexity the need to keep track of things becomes important. It is the job of log aggregation solutions to condense log data into a form that is easier to search, visualize, and analyze. There are many log aggregation solutions out there today with various pros and cons to fit the various types of data and architectures. This makes the choice of selecting a log aggregation solution an important one. This thesis analyzes two full-stack log aggregation solutions, Elastic stack and SigNoz, with the goal of evaluating how the ingestion and storage components of the two stacks perform with smaller and larger amounts of data. The evaluation of these solutions was done by ingesting log files of varying sizes into them while tracking their performance. These performance metrics were then analyzed to find similarities and differences. The thesis found that SigNoz featured a higher CPU usage on average, faster processing times, and lower memory usage. Elastic stack was found to do more processing and indexing on the data, requiring more memory and storage space to allow for more detailed searchability of the ingested data. This also meant that there was a larger storage space requirement for Elastic stack than SigNoz to store the ingested logs. The hope of this thesis is that these findings can be used to provide insight into the area and aid those choosing between the two solutions in making a more informed decision.