Kafka is used as distributed message queue. I.e. a set of apps produce opaque messages and send them to Kafka queues, while another set of apps consume these messages from Kafka queues.
Time series databases are used for storing and querying time series data. There are no Kafka-like queues in time series databases. Every time series has a key, which uniquely identifies this time series, and a set of (timestamp, value) tuples usually ordered by timestamp. Time series key is usually composed of a name plus a set of ("key"=>"value") labels. These labels are used for filtering and grouping of time series data during queries. There are various time series databases optimized for various workloads. For example, VictoriaMetrics [1] is optimized for monitoring and observability of IT infrastructure, app-level metrics, industrial telemetry and other IoT cases.
As you can see, there is little sense in comparing Kafka with time series databases :)
