Sonic vs OpenSearch
Both are alternatives to Algolia. Here's how they stack up — verified facts, no spin.
Also searched as OpenSearch vs Sonic — same comparison, one verdict.
Sonic
A search index in about 30 MB of RAM, when you need less than you think.
Sonic is a deliberately minimal Rust search index: it stores identifiers rather than documents, so you query Sonic for matching IDs and fetch the actual records from your own database. That design keeps memory in the tens of megabytes where a conventional engine wants gigabytes, and it makes Sonic a serious option for small services, edge deployments and anything running on hardware you would not consider adequate for search. MPL-2.0, around 21.3k stars. It is not trying to be Algolia — it has no faceting and no relevance tuning — and being clear about that is the point of listing it.
OpenSearch
The Apache-2.0 Elasticsearch fork — when search is only half the job.
OpenSearch is the fork AWS created after Elastic changed Elasticsearch's licence in 2021, kept under Apache-2.0 and now governed by a foundation. It is far more than a search box: aggregations, analytics, log ingestion, dashboards and vector search in one platform, which is exactly right if search and observability are the same infrastructure problem for you and exactly wrong if you only wanted a fast product search. Around 13.4k stars. It is the heaviest option here and it earns that weight only when you need what it does.
Side by side
| Sonic | OpenSearch | |
|---|---|---|
| Sovereignty Score | 94 | 91 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MPL-2.0 | Apache-2.0 |
| Pricing | Free and open source. Runs in around 30 MB of RAM. | Free and open source. Managed OpenSearch is available from AWS and others. |
Sonic edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Sonic
Strengths
- +Extraordinarily light — tens of megabytes rather than gigabytes
- +Fast enough for most real workloads despite the footprint
- +MPL-2.0, simple to deploy, almost nothing to operate
- +Ideal for edge, embedded and small-service deployments
Trade-offs
- −Returns identifiers only — you fetch the documents yourself
- −No faceting, no aggregations, no relevance tuning
- −Minimal ecosystem and few integrations
- −Wrong tool if search is a core product surface
OpenSearch
Strengths
- +Apache-2.0 under foundation governance — the licence question is settled
- +Handles search, analytics, logs and vectors in one platform
- +Enormous ecosystem inherited from Elasticsearch
- +Managed offerings exist if you would rather not operate a cluster
Trade-offs
- −Substantially heavier than the purpose-built search engines
- −JVM tuning and cluster operation are a real skill set
- −Relevance needs meaningful configuration to match Algolia
- −Overkill if all you need is a fast search box
Related alternative guides
Facts verified 2026-07-31. Licenses and pricing change — spotted something out of date? That's a correction we want.