Loki
Grafana's log aggregator (Prometheus-like)
Elasticsearch
Search and log analytics engine
Loki indexes only metadata (labels) and stores log lines compressed in object storage — cheap, fast for label-based queries, weaker for full-text search. Elasticsearch indexes every token — rich full-text search, heavier infra, much more expensive to run at scale.
Pick Loki when you mostly filter by service/host/level and cost matters.
Pick Elasticsearch when you need full-text search across log bodies at query time.
| Feature | 📁Loki | 🔎Elasticsearch | Winner |
|---|---|---|---|
| Index strategy | Labels only | Every field tokenized | Tie |
| Storage cost (S3) | Very cheap (compressed chunks) | High (SSDs, replica) | A |
| Full-text search | Grep-like per chunk | Lucene, instant | B |
| Query language | LogQL (PromQL-like) | Lucene / ES Query DSL | Tie |
| Ingest cost | Low | High | A |
| Operational complexity | Lower | Higher | A |
| Ecosystem / visualisations | Grafana | Kibana + Grafana | B |
| Security / audit use-case | Possible, not ideal | Native (SIEM) | B |
Index strategy
TieLoki
Labels only
Elasticsearch
Every field tokenized
Storage cost (S3)
ALoki
Very cheap (compressed chunks)
Elasticsearch
High (SSDs, replica)
Full-text search
BLoki
Grep-like per chunk
Elasticsearch
Lucene, instant
Query language
TieLoki
LogQL (PromQL-like)
Elasticsearch
Lucene / ES Query DSL
Ingest cost
ALoki
Low
Elasticsearch
High
Operational complexity
ALoki
Lower
Elasticsearch
Higher
Ecosystem / visualisations
BLoki
Grafana
Elasticsearch
Kibana + Grafana
Security / audit use-case
BLoki
Possible, not ideal
Elasticsearch
Native (SIEM)
Best for
Best for
ES → Loki: swap Logstash/Fluentd output to Loki's HTTP endpoint, add labels (service, level, host), drop high-cardinality fields (user_id, trace_id as labels is a trap). Accept you lose Lucene-style free-text indexes. Loki → ES: point shippers at ES, define index templates, budget 5-10× the infra.
Loki indexes only metadata (labels) and stores log lines compressed in object storage — cheap, fast for label-based queries, weaker for full-text search. Elasticsearch indexes every token — rich full-text search, heavier infra, much more expensive to run at scale. In short: Loki — Grafana's log aggregator (Prometheus-like). Elasticsearch — Search and log analytics engine.
Pick Loki when you mostly filter by service/host/level and cost matters.
Pick Elasticsearch when you need full-text search across log bodies at query time.
ES → Loki: swap Logstash/Fluentd output to Loki's HTTP endpoint, add labels (service, level, host), drop high-cardinality fields (user_id, trace_id as labels is a trap). Accept you lose Lucene-style free-text indexes. Loki → ES: point shippers at ES, define index templates, budget 5-10× the infra.
Yes. Both have MCP servers installable via MCPizy (mcpizy install loki and mcpizy install elasticsearch). They work identically across Claude Code, Claude Desktop, Cursor, Windsurf, and any other MCP-compatible client. You can install both side by side and route queries in your agent's prompt.
Sentry is a developer-first error + performance tracker — excellent for 'why did this deploy break?'. Datadog is a full-stack observability platform — infra metrics, APM, logs, RUM, security, and ~30 more products. Most teams use Sentry for app errors and Datadog (or competitors) for infra.
Grafana is the open-source dashboard king, paired with Prometheus/Loki/Tempo (the LGTM stack). Datadog is the polished managed alternative — faster to deploy, easier to use, much more expensive. Grafana LGTM wins on cost and flexibility; Datadog wins on time-to-value and enterprise support.
Not competitors — they're paired. Prometheus is the time-series database and scraper. Grafana is the dashboarding UI. You run both: Prometheus collects and stores metrics, Grafana visualizes them. Grafana also supports Loki (logs), Tempo (traces), Elasticsearch, and 100+ other data sources.
Not sure? Run both side by side — swap between them in your AI agent with a single config line.