KagiKagi vs Stillwind: Which is Better in 2026?
A comprehensive comparison of Kagi and Stillwind covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Kagi if:
- →You want more affordable paid plans (from $5/mo)
- →You need no ads or tracking or customizable rankings
Choose Stillwind if:
- →You want a free tier to get started without commitment
- →You need natural-language part search combining parametric and semantic constraints or millions of structured parameters queryable in one request
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Kagi vs Stillwind: At a Glance
Pricing Comparison: Kagi vs Stillwind
Understanding the pricing differences between Kagi and Stillwind is crucial for making the right choice. Here's how their plans compare side by side.
💡 Pricing takeaway: Stillwind has an edge with a free tier, letting you start without commitment. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from Kagi and Stillwind stacks up.
What Makes Each Tool Unique
🔵 Unique to Kagi
Features available in Kagi but not in Stillwind:
- ✓No ads or tracking
- ✓Customizable rankings
- ✓AI summarization
- ✓Universal Summarizer
- ✓FastGPT
- ✓Lenses
🟣 Unique to Stillwind
Features available in Stillwind but not in Kagi:
- ✓Natural-language part search combining parametric and semantic constraints
- ✓Millions of structured parameters queryable in one request
- ✓Page-level datasheet references on every returned spec
- ✓Footprint-matching alternative part discovery
- ✓Builder (beta): prompt to formalized constraints to validated BOM
- ✓Real-time cross-referencing to resolve incompatibilities between candidate parts
Use Case Recommendations
Best for: Kagi
Premium search engine focused on privacy and quality results. Kagi provides ad-free search, customizable rankings, and AI features like summarization without tracking users.
Ideal use cases:
- •Teams or individuals who need no ads or tracking
- •Teams or individuals who need customizable rankings
- •Teams or individuals who need ai summarization
- •Teams or individuals who need universal summarizer
- •Anyone focused on search workflows
- •Anyone focused on privacy workflows
Best for: Stillwind
Stillwind is a natural-language search engine and design copilot for electronic components, built by P2P Industries. It treats part selection as a constraint-solving problem rather than a keyword search. You describe electrical behavior, constraints, or a package in plain language — a buck converter stepping 12V to 3.3V at 3A or more with integrated FETs, or a shielded power inductor at 4.7uH with at least 5A saturation current — and its hybrid search resolves that against a proprietary component database, returning matching parts with source-backed specifications. Every spec traces to the datasheet with page-level references, so an engineer can validate a claim rather than trust an extracted number, which is the persistent problem with parametric search tools built on scraped distributor data. Parametric and semantic constraints go into the same query instead of being split across a filter sidebar and a text box. The Builder product, currently in beta, extends this from part search to board composition: describe a system — a BLE air-quality sensor board with charging, or a battery-powered GPS asset tracker — and Stillwind helps draft the spec, formalizes it into mathematical constraints, matches those constraints to exact parts via Stillwind Search, cross-references candidate parts in real time to resolve incompatibilities between them, and returns a semantically validated bill of materials. The company frames it as a first step toward autonomous electrical engineering.
Ideal use cases:
- •Teams or individuals who need natural-language part search combining parametric and semantic constraints
- •Teams or individuals who need millions of structured parameters queryable in one request
- •Teams or individuals who need page-level datasheet references on every returned spec
- •Teams or individuals who need footprint-matching alternative part discovery
- •Anyone focused on electronic components workflows
- •Anyone focused on pcb design workflows
🔍 Other Search & Knowledge Tools to Consider
Kagi and Stillwind aren't the only options. Here are other popular tools in the same space:
Perplexity
AI search engine with cited, real-time answers
Phind
AI search engine for developers
Genspark
AI search and agent platform with autonomous research and content creation
Fellou
Agentic AI browser that automates complex web tasks and deep research
Dia
AI-native browser from Browser Company with contextual intelligence
You.com
AI search engine with chat and multimodal features
Is one of these your tool?
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Frequently Asked Questions
Is Kagi better than Stillwind?
It depends on your needs. Kagi offers 6 key features including No ads or tracking and Customizable rankings, while Stillwind provides 6 features including Natural-language part search combining parametric and semantic constraints and Millions of structured parameters queryable in one request. Kagi uses a paid model, while Stillwind is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is Kagi cheaper than Stillwind?
Stillwind doesn't have standard paid plans, while Kagi starts at $5/month. Stillwind offers a free tier, making it easier to get started. Always check the official websites for the most current pricing.
Can I use Kagi and Stillwind together?
Yes, many users combine Kagi and Stillwind in their workflow. Kagi excels at no ads or tracking, while Stillwind shines with natural-language part search combining parametric and semantic constraints. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between Kagi and Stillwind?
While both are search & knowledge tools, Kagi emphasizes no ads or tracking, whereas Stillwind is known for natural-language part search combining parametric and semantic constraints. The best choice depends on your specific workflow and feature priorities.
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