ZincSearch Has a Free API: The Lightweight Elasticsearch Alternative Written in Go That Runs on 50MB of RAM

Your side project needs search. You looked at Elasticsearch — minimum 2GB RAM, Java dependency, complex cluster setup. You looked at Algolia — $1/1000 records/month adds up fast. ZincSearch is a single Go binary that gives you Elasticsearch-compatible search on 50MB of RAM.

What ZincSearch Actually Does

ZincSearch (now called OpenObserve for the observability use case) is an open-source search engine written in Go. It provides full-text search with an Elasticsearch-compatible API, a built-in web UI for data exploration, and S3-compatible storage for cost-effective data retention.

The design philosophy: make search accessible to everyone, not just teams with DevOps engineers. One binary, one command to start, built-in UI for exploring data. No JVM, no cluster coordination, no YAML configuration files.

ZincSearch supports Elasticsearch-compatible ingest and search APIs, so existing tools and libraries work with minimal changes. Open-source under Apache 2.0.

Quick Start

# Single binary — download and run
mkdir -p data
ZINC_FIRST_ADMIN_USER=admin ZINC_FIRST_ADMIN_PASSWORD=Complexpass#123 \
  zinc server --data ./data

# Or Docker
docker run -d -p 4080:4080 \
  -e ZINC_FIRST_ADMIN_USER=admin \
  -e ZINC_FIRST_ADMIN_PASSWORD=Complexpass#123 \
  public.ecr.aws/zinclabs/zinc:latest

Index a document:

curl -u admin:Complexpass#123 \
  -X POST http://localhost:4080/api/default/articles/_json \
  -d '[
    {
      "title": "Getting Started with Go",
      "body": "Go is a statically typed language designed at Google...",
      "tags": ["go", "tutorial"],
      "published": "2026-03-15"
    },
    {
      "title": "Building REST APIs in Go",
      "body": "Learn how to build production-ready REST APIs using Go...",
      "tags": ["go", "api", "rest"],
      "published": "2026-03-20"
    }
  ]'

Search (Elasticsearch-compatible):

curl -u admin:Complexpass#123 \
  http://localhost:4080/es/articles/_search -d '{
  "query": {
    "match": { "title": "REST API" }
  },
  "highlight": {
    "fields": { "title": {} }
  }
}'

3 Practical Use Cases

1. Application Log Search

# Ship logs with Fluent Bit (Elasticsearch output works directly)
[OUTPUT]
    Name  es
    Host  localhost
    Port  4080
    Index app-logs
    HTTP_User admin
    HTTP_Passwd Complexpass#123

Then search logs via the built-in UI at http://localhost:4080 or API.

2. Product Search for E-Commerce

curl -u admin:Complexpass#123 \
  http://localhost:4080/es/products/_search -d '{
  "query": {
    "bool": {
      "must": { "match": { "name": "wireless headphones" } },
      "filter": {
        "range": { "price": { "lte": 100 } }
      }
    }
  },
  "sort": [{ "rating": "desc" }],
  "size": 10
}'

3. Full-Text Search for Documentation Sites

// Index your docs on build
const docs = glob.sync('docs/**/*.md').map(file => ({
  path: file,
  title: extractTitle(file),
  content: fs.readFileSync(file, 'utf8'),
  section: file.split('/')[1]
}));

await fetch('http://localhost:4080/api/default/docs/_json', {
  method: 'POST',
  headers: {
    'Authorization': 'Basic ' + btoa('admin:Complexpass#123'),
    'Content-Type': 'application/json'
  },
  body: JSON.stringify(docs)
});

Why This Matters

Search shouldn't require a dedicated infrastructure team. ZincSearch brings full-text search to the 99% of projects that don't need Elasticsearch's scale but do need its query capabilities. One binary, 50MB RAM, Elasticsearch-compatible API, built-in UI. That's the entire ops story.

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