It's especially tuned for use in LLM agents, ensuring context-aware, hallucination-resistant responses. Designed for ease and speed, you can go from zero to search-enabled app in minutes. The platform includes a generous free tier and clear, tiered pricing as you scale. Whether you’re building customer support tools, knowledge agents, or AI-enhanced search features, Ducky handles the infrastructure so developers can build smarter.
- Developers & ML Engineers: Add powerful, managed search infrastructure to your app with minimal setup.
- Data Scientists & AI Teams: Build RAG-powered knowledge systems without managing the backend vector stack.
- Product Managers & Founders: Prototype and launch AI search features quickly using SDKs and built-in pipelines.
- Enterprise Teams & Agencies: Scale production-grade retrieval systems with performance and developer efficiency in mind.
- LLM-Agent Builders: Create context-aware agents with accurate, low-hallucination retrieval at core.
How to Use It?
- Sign Up & Create Project: Access the dashboard, create a new project, and generate your API key.
- Install SDK: Use the Python or TypeScript SDK to connect to Ducky’s service.
- Index Documents: Upload documents or content to your named index for retrieval.
- Perform Retrievals: Send natural language queries to retrieve relevant content chunks.
- Integrate into Agents: Embed Ducky into LLM workflows for enhanced response accuracy via search.
- Fully Managed RAG Infrastructure: No need for DIY vector databases or complex embedding systems.
- Multi-Stage Pipeline: Includes chunking, query rewriting, hybrid search, reranking, and more for relevance.
- Developer-Friendly SDK: Quick setup via intuitive Python and TypeScript libraries.
- Low-Latency, High Relevance: Optimized for fast retrieval and precision, not just similarity-based results.
- LLM Agent Compatible: Designed to support downstream usage in agents with context-aware retrieval.
- Clear, Tiered Pricing & Free Tier: Start for free, scale predictably with transparent pricing.
- Allows developers to build powerful search features without managing infrastructure.
- Multi-stage retrieval improves result quality and reduces hallucinations.
- Quick loading and indexing means faster go-to-market timelines.
- Thoughtful support for LLM-based systems makes it ideal for modern AI workflows.
- Generous free tier encourages experimentation before scaling up.
- Limited control over internal pipeline logic and fine-tuning options.
- Potential dependency on third-party infrastructure—less flexibility for custom scaling.
- Usage beyond free tier may become cost-prohibitive at very large volumes.
- Invite-only or access gating may limit immediate adoption.
Build
Free
- 100k index tokens
- 100k retrieval tokens
Launch
$ 12.00
- 300k index tokens each month
- 300k retrieval tokens each month
- $0.029 per additional 1k index tokens
- $0.166 per additional 1k retrieval tokens
Grow
$ 290.00
- 3M index tokens each month
- 3M retrieval tokens each month
- $0.014 per additional 1k index tokens
- $0.079 per additional 1k retrieval tokens
- Support via Slack
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Reviews
Rating Distribution
Average score
Popular Mention
FAQs
It manages the backend for semantic search and RAG, letting developers index and retrieve data without custom infrastructure.
Very fast—signup, SDK install, indexing, and querying can be completed within minutes.
It abstracts away complex backend tasks like embedding, vector databases, and reranking into a managed service.
Yes—there’s a free forever tier, with paid plans offering larger token quotas and scaling
Absolutely—it excels at powering agents with context-aware retrieval to reduce hallucinations.
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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