dockit vs opensearch dashboards
OpenSearch Dashboards is the standard web interface for OpenSearch. It's built for visualizations and monitoring, but it's often too heavy for daily query development. Running it requires a dedicated service, and the 10-30 second startup time and 500MB+ RAM overhead can get in the way when you just need to run a quick query.
DocKit is a native desktop app designed specifically for querying OpenSearch. It starts in under 2 seconds, connects directly to your cluster, and keeps your queries stored locally without any server-side dependencies.
head-to-head comparison
| DocKit | OpenSearch Dashboards | |
|---|---|---|
| Type | Native desktop app | Web app (requires server) |
| Startup time | < 2 s | 10–30 s |
| RAM usage | ~150 MB | 500 MB+ |
| Requires Dashboards server | ❌ | ✅ |
| Offline mode | ✅ | ❌ |
| Query editor | Monaco + JSON5 + autocomplete | Basic Dev Tools console |
| AI query assistant | ✅ (OpenAI, DeepSeek) | ❌ |
| Query persistence | ✅ Local files (Git-friendly) | ✅ Saved queries (server-side) |
| Index & cluster management | ✅ | ✅ |
| Visualizations / dashboards | ❌ | ✅ |
| Observability / tracing | ❌ | ✅ |
| Alerting | ❌ | ✅ |
| DynamoDB / Elasticsearch | ✅ | ❌ |
| AWS OpenSearch Service | ✅ | ✅ (hosted) |
| Open source | ✅ Apache 2.0 | ✅ Apache 2.0 |
| OS version support | 1.x – 3.x+ | Version-locked to cluster |
when dockit is the better choice
working with aws opensearch service
AWS OpenSearch Service gives you a managed Dashboards endpoint that can be frustrating to configure and is always version-locked to your cluster. DocKit skips the Dashboards setup by connecting directly to the OpenSearch REST API using your AWS credentials (access keys, IAM roles, or profiles).
faster query loops
Browser-based tools have inherent friction. The Dashboards Dev Tools tab has to load inside a Dashboards instance within your browser. DocKit is a native app that's ready to use in about 2 seconds. If you're running dozens of queries every day, that's a lot of waiting you don't have to do.
ai-assisted query generation
DocKit lets you describe a query in plain English and generates the OpenSearch DSL for you. It uses your actual index mapping as context so the results are relevant. It works with OpenAI or DeepSeek using your own API key. OpenSearch Dashboards has no equivalent for this.
queries in version control
DocKit stores your history and saved queries as local files. This makes it easy to commit them to Git or share them with teammates as code. Your query records stay with you, independent of whatever Dashboards instance you're currently using.
using elasticsearch or dynamodb
You can manage OpenSearch, Elasticsearch, and DynamoDB in the same app. You don't have to juggle multiple tools when switching between different cluster types.
when opensearch dashboards is the better choice
Stick with OpenSearch Dashboards when you need:
- Dashboards and visualizations: the built-in engines (Lens, TSVB) are better for charts intended for stakeholders.
- Alerting and monitors: rule-based alerting that's integrated directly into the cluster.
- Observability pipelines: features like Trace Analytics, Jaeger/Zipkin integrations, and specific log analytics views.
- Security plugin UI: managing FGAC, roles, and audit logs via the Security plugin.
- Shared access: when you need a browser URL that anyone on the team can access without installing software.
Most teams find a balance by using both: DocKit for the engineers writing queries, and Dashboards for shared monitoring and stakeholder reporting.
the query editor difference
The Dev Tools console in OpenSearch Dashboards is functional but limited. It's essentially the same tool inherited from Kibana:
- Strict JSON is required, which means no inline comments or trailing commas.
- Autocomplete for fields can be slow to update after mapping changes.
- There's no AI support.
- Saved queries are stored on the server and tied to a specific instance.
- You can't easily export queries as curl commands.
DocKit uses a Monaco-based editor that's much more flexible:
- JSON5 support: add comments to your queries and use trailing commas without breaking things.
- Live autocomplete: field suggestions are pulled from your mapping as soon as you connect.
- DSL validation: the editor checks your syntax against the OpenSearch Query DSL.
- One-click exports: auto-formats on save and lets you copy any query as a
curlcommand. - Multi-tabbing: keep several queries open and run them simultaneously.
- Local history: your entire execution history is saved locally and is fully searchable.
connecting dockit to opensearch
self-hosted opensearch
- Download DocKit for your OS.
- Go to New Connection and select OpenSearch.
- Enter your host, port, and credentials (Basic Auth, API Key, or none).
- Click Connect.
aws opensearch service
- Go to New Connection and select OpenSearch.
- Enter your domain endpoint (the
https://...URL). - Select AWS credentials authentication. DocKit will use your
~/.aws/credentialsor environment variables. - Click Connect.
For VPC clusters or fine-grained access control, check the connection guide.
opensearch version support
DocKit works with OpenSearch 1.x, 2.x, and 3.x because it uses the standard REST API. Unlike OpenSearch Dashboards, it isn't version-locked to the cluster it's talking to.
frequently asked questions
Is DocKit a full OpenSearch Dashboards replacement? For query development and index management, yes. For dashboards, observability, and alerting, no. Most teams use both.
Does it work with Amazon OpenSearch Service? Yes. DocKit connects to the AWS OpenSearch Service REST endpoint using your AWS credentials. Use Basic Auth for HTTP-level credentials or AWS SigV4 signing (roadmap — check GitHub for status).
Can I use DocKit with OpenSearch behind a VPN? Yes. As long as DocKit can reach the OpenSearch REST port (default 9200), it works. You can point DocKit at localhost if you have a VPN or SSH tunnel running.
Does it support OpenSearch Serverless? OpenSearch Serverless requires SigV4 signing for every request. Standard credential auth isn't enough. Check the GitHub issues for the current status.
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