The Most Common Mistakes Teams Make With Datadog Logs (and How to Avoid Them)

I’ve worked with teams running Datadog Logs across some of the largest enterprises in the world. One thing I’ve noticed: most logging mistakes aren’t about Datadog itself — they’re about habits carried over from older log platforms.

The problem? What worked when you had a single ELK cluster or a couple of apps doesn’t scale in Datadog. Costs explode, searches slow down, and suddenly logging becomes a liability instead of a superpower.

Here are the six most common mistakes I see — and what you can do instead.

1. Using Only One Retention Period

Not all logs are equal. Yet I regularly see teams keep everything for 30, 60, or 90 days. That’s one of the fastest ways to waste budget.

• Why it hurts: Debug logs and ephemeral container logs don’t need 90 days of retention. Security audit logs often do. By treating all logs the same, you either keep too little (losing data) or too much (paying for noise).
• What to do instead: Assign retention by log type. Keep your compliance or security logs longer, but let ephemeral or high-volume logs expire quickly.

👉 I cover retention strategies in detail on our YouTube channel — check it out if you want a step-by-step guide.

2. Indexing Everything

Old-school logging platforms taught us: “ship all logs, index all logs.” In Datadog, indexing is optional.

• Why it hurts: If you index everything, you’ll quickly drown in costs and noise. Teams end up with slow queries and dashboards cluttered with irrelevant logs.
• What to do instead: Use exclusion filters to control what gets indexed. Keep only the logs that help you troubleshoot, audit, or meet compliance.

3. Not Setting Up an Archive

I’m still surprised how many teams skip archives.

• Why it hurts: Without an archive, you can’t recover excluded logs if you filter too aggressively. That’s dangerous when debugging incidents or investigating security events. It also keep you safe and make your logging strategy more aggressive thus saving costs.
• What to do instead: Set up an archive (S3, GCS, etc.) as a safety net. With archives, you can aggressively filter and still rehydrate logs if needed.

4. Not Parsing Logs Up Front

Another common mistake: treating Datadog like a place to dump raw text logs and “figure it out later.”

• Why it hurts: Complex queries on unparsed logs are slow, expensive, and only understandable by log experts. Check those with datadog.pipelines:false
• What to do instead: Parse logs as they come in. Once logs are structured into fields, anyone can query them — not just SREs or DevOps. Parsing unlocks the real power of Datadog Logs.

5. Only Using Exclusion Filters at 100%

Exclusion filters are great for removing noise. But too many teams treat them as an on/off switch.

• Why it hurts: Sometimes you don’t need all of a noisy log type, but you also don’t want to throw away 100%. That’s how visibility gaps creep in.
• What to do instead: Use sampling. Keep 5–20% of those noisy logs to maintain trend visibility. This way, if an issue starts spiking, you’ll still see it in the sample set.

6. Thinking of Logs in Isolation

Logs are just one piece of your observability stack. But I often see teams treat them as the source of truth.

• Why it hurts: If you duplicate data across logs and traces, you’re paying twice. If you only look at logs, you miss the full picture.
• What to do instead: Connect your logs to metrics and traces. Don’t log data you’ve already enriched in spans. Use logs where they add context, not as a dumping ground.

Wrapping Up

Logging in Datadog is powerful — but only if you avoid these common mistakes. Think of it as a checklist:

• Tune retention.
• Be selective about indexing.
• Always have an archive.
• Parse early.
• Sample instead of dropping 100%.
• Correlate logs with metrics and traces.

Avoiding these mistakes will save you money, improve performance, and make logging a tool your whole team can use — not just a cost center.

👉 Want to see these fixes in action? Check out our YouTube channel where we walk through real-world Datadog setups.