I was a big Elasticsearch user during several years, I wasn't convinced by Grafana Loki which is far less expensive because the data are stored in object storage, but have poor read performances because it's not a real search engine.
Then I discovered Quickwit which is a combination of the advantages of both world. With Quickwit you can ingest the logs the way you're already used to : through OTLP/grpc, with a log collector like fluentbit or Vector which can ingest the stdout of your pods and forward to Quickwit using the http API, etc.
And you can then use Grafana with pretty much the same features available for the Elasticsearch datasource.
The read performance of Quickwit are incredible because of their amazing indexing engine (which is kind of like Lucene rewritten in Rust) and the storage is very cheap and without limitation other than your cloud provider capabilities.
I was a big Elasticsearch user during several years, I wasn't convinced by Grafana Loki which is far less expensive because the data are stored in object storage, but have poor read performances because it's not a real search engine.
Then I discovered Quickwit which is a combination of the advantages of both world. With Quickwit you can ingest the logs the way you're already used to : through OTLP/grpc, with a log collector like fluentbit or Vector which can ingest the stdout of your pods and forward to Quickwit using the http API, etc.
And you can then use Grafana with pretty much the same features available for the Elasticsearch datasource.
https://quickwit.io/docs/log-management/send-logs/using-flue...
The read performance of Quickwit are incredible because of their amazing indexing engine (which is kind of like Lucene rewritten in Rust) and the storage is very cheap and without limitation other than your cloud provider capabilities.
https://quickwit.io/blog/quickwit-binance-story