Eventual consistency lets replicas converge "eventually," but it makes no promises about order. If Alice posts "I lost my cat" and then comments "Found him!", a replica might show the comment before the post. The reply makes no sense without the cause. Causal consistency guarantees that if event A causally precedes event B, every replica observing B has already observed A.

The key word is causally. Concurrent events — writes with no happens-before relationship — can still be seen in any order on different replicas. You're not paying for total ordering; you're only paying to preserve the dependencies that actually matter.

How it works under the hood: each write carries a dependency set — a summary of the writes its author had seen at the time of writing. A replica receiving a write checks its local state against that dependency set. If anything is missing, the write is buffered until the missing dependencies arrive. Vector clocks or version vectors are the usual encoding.

Real-world example: Facebook's TAO and many comment systems use causal consistency. When you reply to a comment, your reply carries metadata saying "I saw comment #4823." A read replica that hasn't yet received comment #4823 will delay showing your reply (or fetch the parent) rather than display an orphaned response. The same principle drives collaborative editors like Google Docs — your keystrokes carry causal metadata so peers never apply your edits before the prior context they depended on.

What it does NOT give you:

• Concurrent writes from independent users may be ordered differently on different replicas — that's fine, they had no causal link.
• It doesn't prevent lost updates on concurrent writes to the same key; you still need conflict resolution (CRDTs, last-write-wins, application merge).
• It doesn't give you linearizability — a read might miss a write that finished a microsecond ago on another replica, as long as you didn't depend on it.

Rule of thumb: if your application has conversations — replies, threads, dependent edits, "after I did X, do Y" workflows — you need at least causal consistency. If every write is independent (telemetry, sensor data, like-counts), eventual is enough. The cost gap is roughly 2-4x metadata overhead per write for vector clocks versus plain timestamps, but it's still cheaper and more available than strong consistency.

Causal is the sweet spot for social, collaborative, and messaging systems: strong enough to preserve meaning, weak enough to stay fast and partition-tolerant.