Elasticsearch and Skryx solve overlapping problems with different trade-offs. Elasticsearch is a general-purpose search and analytics engine you operate yourself; Skryx is a hosted search SaaS with strong defaults for product / content search.
# When Skryx wins
- You're tired of capacity-planning Elasticsearch nodes.
- You want typo tolerance, synonyms, ranking and AI features included, not configured.
- You want sub-50 ms response on commodity payloads without tuning shards / replicas.
- You don't need Elasticsearch's analytics-engine features (aggregations across billions of docs, parent-child, geo-shape queries, etc.).
# When Elasticsearch is the right answer
- You're already running it well and your team owns the cluster.
- You need log/metrics analytics — that's Kibana territory.
- You need Painless scripting at query time for arbitrary scoring.
# Mapping the concepts
| Elasticsearch | Skryx |
|---|---|
| Cluster | (managed by Skryx) |
| Index | Index |
| Mapping | Schema |
Document _id |
id |
_source |
document |
match / multi_match |
q + query_by |
bool.filter |
filter_by |
aggs (terms) |
facet_by |
function_score |
Ranking rules |
| Analyzers + filters | typo_config, stop_words, synonyms |
# Migration path
- Export each ES index to JSON.
elasticdumpor a custom scroll script. - Provision the Skryx index with the create-index endpoint.
- Use batch upsert to load. 1,000 docs per request, parallelised.
- Translate one representative query at a time — start with your top three.