WONDERSEARCH VS. DENSE EMBEDDINGS
You want answers.
Skip the extra setup.
Dense embeddings are one way to build semantic search. WonderSearch gives you semantic search as a service: serverless, pay per query, and no upfront ingestion or embedding fees.
Follow a search into production.
Select a moment in the journey.
Build the path to your first result.
- Prepare documents
- Generate embeddings
- Build a vector index
- Query and evaluate
A typical dense setup represents documents as vectors, stores them in an index, and searches that index. Managed services can handle some of this work.
Get to the question.
- Connect your data
- Ask in your own words
- Use relevant results
WonderSearch gives you a hosted search service with no upfront ingestion or embedding fees. You do not manage a separate embedding pipeline.
Illustrative workflows, not a latency comparison. Dense retrieval is an approach; WonderSearch is a hosted product. Features and costs of dense services vary.
Keep your focus
on your product.
Dense embeddings represent content as lists of numbers that a retrieval system compares to find related passages. They are a building block for semantic search. Read the technical background.
Your customers want to find an answer, finish a task, or give their AI the right context. WonderSearch lets your team focus on those experiences, without taking on a separate embedding pipeline.
Connect through the API, SDK, or MCP. Choose the search effort for your use case. Put relevant passages and source references to work in your product. You build the experience; WonderSearch handles the search service.
Compare the commitment.
| Your decision | Dense embedding stack | WonderSearch |
|---|---|---|
| Getting data ready | Generate and store document embeddings, directly or through a managed service. | No upfront ingestion or embedding fees. |
| Operating search | Choose and maintain the components, or delegate them to a provider. | Serverless search through an API, SDK, or MCP. |
| Search compute | Infrastructure, token, or usage charges vary by deployment. | A published price per query for each search size. |
| At rest | Depends on capacity, minimums, and storage pricing. | No search compute charge when there are no queries. Storage remains billable. |
| Control | Direct control over models and index configuration in a self-managed stack. | A hosted product with selectable search effort. |
| Quality | Depends on the model, data, and retrieval setup. | Evaluate the published results, then test your own questions. |
Make your next search
a WonderSearch.
Give your team a shorter setup checklist and a clear usage model. WonderSearch combines semantic search, serverless operation, and per-query compute pricing in one service.
Already using dense embeddings? Bring a representative set of questions and compare the results, the operating work, and the bill. If direct model or index control is essential, keep that requirement in your evaluation. Managed dense services vary; compare the service you would actually run.
Start with our benchmark results, then try the questions your customers ask. We can help you plan an evaluation around your product, so the decision rests on useful answers.