Choosing a Log Aggregation Stack: Loki vs Elasticsearch vs CloudWatch Logs vs Vector+ClickHouse

Choosing a Log Aggregation Stack#

Logs are the most fundamental observability signal. Every application produces them, every incident investigation starts with them, and every compliance framework requires retaining them. The challenge is not collecting logs – it is storing, indexing, querying, and retaining them at scale without spending a fortune.

The choice of log aggregation stack determines your query speed, operational burden, storage costs, and how effectively you can correlate logs with metrics and traces during incident response.

Choosing a Monitoring Stack: Prometheus vs Datadog vs Cloud-Native vs VictoriaMetrics

Choosing a Monitoring Stack#

Monitoring is not optional. Without metrics, you are guessing. The question is not whether to monitor but which stack to use. The right choice depends on your cost tolerance, operational capacity, retention requirements, and how much you value control versus convenience.

Decision Criteria#

Before comparing tools, clarify what matters to your organization:

  • Cost model: Are you optimizing for infrastructure spend or engineering time? Self-managed tools cost less in licensing but more in operational hours. SaaS tools cost more in subscription fees but less in engineering effort.
  • Operational burden: Who manages the monitoring system? Do you have an infrastructure team, or are developers responsible for everything?
  • Data retention: Do you need metrics for 15 days, 90 days, or years? Long retention changes the equation significantly.
  • Query capability: Does your team know PromQL? Do they need ad-hoc analysis or mostly pre-built dashboards?
  • Alerting requirements: Simple threshold alerts, or complex multi-signal alerts with routing and escalation?
  • Team expertise: An organization fluent in Prometheus wastes that investment by switching to Datadog. An organization with no Prometheus experience faces a learning curve.

Options at a Glance#

Capability Prometheus + Grafana Prometheus + Thanos/Mimir VictoriaMetrics Datadog Cloud-Native Grafana Cloud
Cost model Infrastructure only Infrastructure only Infrastructure only Per host ($15-23/mo) Per metric/API call Per series/GB
Operational burden High Very high Medium None Low Low
Query language PromQL PromQL MetricsQL (PromQL-compatible) Datadog query language Vendor-specific PromQL, LogQL
Default retention 15 days (local disk) Unlimited (object storage) Unlimited (configurable) 15 months Varies (15 days - 15 months) Plan-dependent
HA built-in No (requires federation) Yes Yes (cluster mode) Yes Yes Yes
Multi-cluster Federation (limited) Yes (global view) Yes (cluster mode) Yes Per-account Yes
APM/Tracing No (separate tools) No (separate tools) No (separate tools) Yes (integrated) Varies Yes (Tempo)
Vendor lock-in None None Low High High Low-Medium

Prometheus + Grafana (Self-Managed)#

Prometheus is the de facto standard for Kubernetes metrics. It uses a pull-based model, scraping metrics from endpoints at configurable intervals, and stores time series data on local disk. Grafana provides visualization. Alertmanager handles alert routing.

Choosing a Secret Management Strategy: K8s Secrets vs Vault vs Sealed Secrets vs External Secrets

Choosing a Secret Management Strategy#

Secrets – database credentials, API keys, TLS certificates, encryption keys – must be available to pods at runtime. At the same time, they must not be stored in plain text in git, should be rotatable without downtime, and should produce an audit trail showing who accessed what and when. No single tool satisfies every requirement, and the right choice depends on your security maturity, operational capacity, and compliance obligations.