What Good Logging Actually Requires
Most developers think about logging when something breaks in production and they cannot figure out why. At that point, the quality of your logging setup determines whether the investigation takes minutes or days. The requirements for useful production logging are straightforward: structured log output (JSON, not free-form text), correlation IDs that tie logs from a single request across service boundaries, appropriate log levels that let you filter signal from noise, and a way to query logs quickly when you need to. Most logging tools support all of these. The differences lie in cost, scale, and how well the query interface fits your workflow.
Datadog Logs
Datadog is the full-stack observability platform, and its logging product is genuinely excellent if you can justify the cost. The query language is powerful, the correlation between logs, metrics, and traces is seamless, and the anomaly detection features surface issues before your users report them. The downside is price — Datadog's logging costs scale with ingestion volume and retention period, and it is easy to generate more logs than you budget for. For teams already using Datadog for metrics or APM, adding logs to the same platform makes the correlation story compelling.
Grafana Loki
Loki is the open-source log aggregation system from Grafana Labs, designed to be cost-efficient at scale by indexing only metadata (labels) rather than full log content. Query performance for label-based filtering is excellent; full-text search is slower than Elasticsearch-based solutions. If you are already running Grafana and Prometheus for metrics, adding Loki gives you a unified dashboard for logs, metrics, and traces without adding a new vendor. The self-hosted option keeps costs predictable; Grafana Cloud offers a managed version.
Better Stack (formerly Logtail)
Better Stack has become the go-to choice for smaller teams that want a managed logging solution without enterprise pricing. Ingestion is fast, the SQL-based query interface is intuitive for developers who think in queries, and the alerting integrations cover the common cases. The retention options are more limited than enterprise tools, but for most startups and mid-size teams, the cost-to-capability ratio is excellent. The free tier is generous enough to evaluate properly.
Self-Hosted ELK Stack
The Elasticsearch, Logstash, Kibana stack remains the most powerful self-hosted option, particularly for teams with complex search requirements or large log volumes where per-GB pricing would be prohibitive. The operational overhead is real — running Elasticsearch well requires attention to heap sizing, index lifecycle management, and shard allocation. But for teams with the infrastructure expertise, the flexibility and cost control are unmatched. OpenSearch, the Amazon-maintained Elasticsearch fork, is a viable alternative with similar capabilities.
The Practical Decision
Small team or startup: start with Better Stack or Grafana Cloud Loki. The managed setup removes operational burden and the pricing is accessible. Mid-size team already on Datadog: consolidate logging there for the correlation benefits. Large team with cost constraints and infrastructure expertise: evaluate self-hosted Loki or OpenSearch. The worst outcome is over-investing in a complex logging setup before you know what your actual query patterns are. Start simple, upgrade when you hit the limits.