Get Full Observability Without the DevOps Overhead

Fluentd is free to download — but it's not free to run. The real cost is everything around it: an Elasticsearch or Splunk cluster to store and search logs, Kibana or Grafana for dashboards, Prometheus for infrastructure metrics, a separate APM tool for tracing, and ongoing SRE time to deploy, configure, tune, and upgrade all of these components. A typical team running Fluentd+EFK+Prometheus+APM spends $500-$1,000/month in infrastructure and 10-20 SRE hours/month in operational maintenance. Atatus replaces all of that with a single subscription that includes managed log storage, search, dashboards, alerting, APM, infrastructure monitoring, and distributed tracing — at a cost that's frequently less than the sum of the components it replaces, once you account for the engineering time you recover.

Yes. Atatus accepts logs via its own lightweight agent as well as via HTTP endpoints, Syslog, and direct integration with Fluent Bit output plugins. If you have existing Fluentd or Fluent Bit instances collecting logs from edge devices, Kubernetes nodes, or legacy infrastructure, you can forward those logs to Atatus as an output destination while deploying the Atatus agent on your hosts. This allows you to run both in parallel during validation before cutting over fully. Our onboarding team provides direct migration support for common Fluentd+Elasticsearch migrations.

Fluentd's 500+ plugin ecosystem is genuinely impressive — its breadth is one of its strongest advantages. The right question to ask is: what are those plugins actually doing? In most teams, the core plugin usage falls into a manageable set of patterns: collecting from files, syslog, HTTP, Docker, and Kubernetes; parsing JSON, NGINX, and Apache formats; enriching with metadata; and forwarding to Elasticsearch or S3. Atatus handles all of those patterns natively without any plugin configuration. For more unusual integrations — IoT device log collection, legacy on-premise systems, highly custom routing logic — you may still benefit from keeping Fluent Bit as a forwarder that feeds Atatus as the managed backend. Our onboarding engineers review your specific configuration and identify the optimal migration path.

Yes. Atatus supports log pipeline enrichment including field redaction, masking of PII (email addresses, IP addresses, credit card patterns), and field-level exclusion before storage. You can define redaction rules that apply at ingestion time so sensitive data never reaches long-term storage. Atatus is GDPR compliant with EU data residency available on Business plans, and our data processing agreements (DPAs) are available for enterprise customers. For teams that use Fluentd specifically to mask PII before it reaches Elasticsearch, Atatus replicates that behavior natively without requiring Ruby plugin code.

Fluentd's scale and reliability record is impressive — it's one of the reasons switching from it feels daunting. Atatus processes billions of log entries monthly with a 99.9% uptime SLA and sub-2-second search across any volume. The key distinction is who bears the operational burden of that scale: with Fluentd, your team is responsible for scaling Elasticsearch, managing shard allocation, handling hot nodes, and tuning GC — Amazon and Microsoft have dedicated infra teams for this. With Atatus, that scale is managed for you. For teams without dedicated infra engineering resources, Atatus's managed reliability is more accessible than replicating what hyperscalers do with Fluentd internally.

Yes. Atatus deploys as a DaemonSet on Kubernetes and automatically collects logs from all containers across your cluster without any per-pod configuration. Beyond just log collection, the same DaemonSet also collects pod health metrics, container restart counts, resource utilization (CPU/memory requests vs limits), and node-level infrastructure data — giving you complete Kubernetes observability from a single deployment. When a pod crashes and generates an error log, Atatus automatically correlates that log with the pod's memory usage leading up to the crash and any related APM traces — in a single view, not across three separate dashboards.

The recommended approach is a parallel-run migration over 5-7 days. Install the Atatus agent on your infrastructure — it begins collecting logs, APM traces, and infrastructure metrics immediately (first logs appear in under 15 minutes). Keep Fluentd running in parallel during this period to validate Atatus log collection matches your expectations before decommissioning. You can even configure Fluentd to forward a copy of its log stream to Atatus as an additional output during validation, so both tools receive the same log data simultaneously. Our onboarding engineers provide free migration support including review of your Fluentd configuration, help recreating saved views and alert configurations in Atatus, and a migration checklist. Historical logs stored in your Elasticsearch cluster cannot be migrated, but all new log data is captured immediately from Atatus deployment.

Atatus stores and manages your log data in its own managed backend — it doesn't function as a multi-destination router the way Fluentd does. If you need logs delivered to multiple destinations simultaneously (e.g., Atatus + S3 archive + a security SIEM), the recommended approach is to use Fluent Bit as a lightweight forwarder that fans out to multiple outputs including Atatus. This is actually a common architecture: Fluent Bit handles the multi-destination routing at the edge, while Atatus provides the full managed observability backend for search, dashboards, alerting, APM, and infrastructure monitoring. Our team can help you design this architecture based on your specific forwarding requirements.