Best Privacy-Focused AI Search Engine 2026

The Best Privacy-Focused AI Search Engine in 2026 is not one product for every user: DuckDuckGo is the strongest free anonymous AI layer, Kagi is the cleanest paid private search engine, and Brave Search API is the most defensible developer choice at the moment when Google says AI Overviews reach 2.5 billion monthly users. I reached that view because privacy in AI search is no longer a preference buried inside a settings page. It changes what the engine stores, what it sends to model providers, what it can prove to a compliance team, and whether a curious query becomes a permanent training signal.

This guide takes a London-first buyer’s view rather than a fan-club view. The central question is not which search company has the loudest privacy slogan. It is which product gives a real user or engineering team the least exposed data path while still returning useful, cited, current answers. That distinction matters because the market now mixes classic private search, AI chat, web-grounded answer engines, enterprise research APIs, and browser-level assistants.

In practice, I would not recommend the same tool to a journalist investigating a sensitive source, a startup wiring search into an agent, a solicitor checking client material, and a student who wants quick answers without a login. The best choice depends on threat model, budget, required sources, account requirements, data retention rules, and how much control the user has over model selection. The analysis below separates public privacy claims from operational constraints, then translates those findings into real buying decisions.

Best Privacy-Focused AI Search Engine: The 2026 Verdict

For a private individual who wants AI-assisted search with the lowest setup friction, DuckDuckGo is the answer I would start with. Duck.ai documents that chats are not stored by DuckDuckGo, are not used for model training, and are anonymised before being sent to model providers. Its free search and chat layer also avoids the account-first model that has become normal across mainstream AI products. The trade-off is capability: DuckDuckGo is not trying to be a full enterprise research platform with file workspaces, team administration, or complex API orchestration.

For people willing to pay for a calmer and more configurable search environment, Kagi is stronger. Its paid model removes the advertising incentive that drives much of the surveillance economy, and its official plan documentation is unusually specific about search counters, AI interactions, and reset behaviour. I would place Kagi ahead of DuckDuckGo for a researcher who values lenses, ranking controls, and a premium search experience, but behind DuckDuckGo for a free anonymous default.

For developers, Brave Search API is the more serious privacy story. Its 2026 LLM Context API documentation and pricing describe real-time search data, answer snippets, news, images, and enterprise options including zero data retention. That makes Brave better suited to agent builders and internal tools than consumer search products. Perplexity and ChatGPT Search remain highly capable citation engines, but their privacy posture depends more heavily on plan type, data controls, and enterprise terms. A balanced market view is close to the logic in our guide to private Perplexity alternatives: privacy leadership depends on use case, not brand mythology.

Why Privacy Now Changes the Search Decision

The privacy question became sharper because AI search is no longer a side feature. Google announced in 2026 that AI Overviews had more than 2.5 billion monthly active users and that AI Mode had passed 1 billion monthly users. Sundar Pichai called AI Mode “our biggest upgrade to Search ever.” That quote signals a structural shift: the default search interface is moving from ranked links toward generated answers, follow-up memory, and increasingly personalised context.

DuckDuckGo used the same moment to make the opposite argument. Gabriel Weinberg criticised Google for “force-feeding AI” to users, while DuckDuckGo chief commercial officer Kamyl Bazbaz said, “People just want a choice.” Whether a reader agrees with that framing or not, it captures the new buyer concern. AI search does not merely retrieve pages. It may rewrite a prompt, infer intent, call multiple providers, remember past behaviour, and compress sources into an answer that looks final.

That means the practical privacy test has changed. A classic search engine could be judged by logs, cookies, IP handling, and ad profiling. An AI search engine must also be judged by prompt retention, model-provider sharing, training exclusions, file upload handling, account identity, enterprise controls, API logging, and whether there is a genuine no-training commitment. These factors are unevenly documented across the market. Some products publish clear controls. Others give broad privacy language but fewer operational limits. The best privacy-focused decision starts by asking what kind of data the search session will expose.

Privacy Architecture: What Each Provider Actually Keeps

Privacy architecture is where marketing claims either become measurable or fall apart. In our 2026 evaluation, I treated each product as a data pathway: user query, identity layer, model call, source retrieval, storage, analytics, deletion, and training use. That framing avoids the common trap of calling a product private because it has a private mode while ignoring what happens after the query leaves the interface.

DuckDuckGo provides the cleanest consumer explanation. Its Duck.ai terms say chats are stored locally unless the user chooses encrypted sync, that DuckDuckGo cannot decrypt synced chats, and that model providers must delete interaction information within 30 days with limited safety and legal exceptions. It also explains how voice chat and dictation are handled. The important constraint is that usage limits are enforced anonymously, which can create friction on VPNs or shared networks because the system cannot simply attach a limit to a conventional user account.

Kagi’s privacy model is built around paid search rather than free advertising. Its official documentation lists plan limits and search counters in detail, but it remains an account-based product. Brave’s API model is different again: it matters less how the consumer interface behaves and more whether an enterprise can buy documented zero data retention, support, and capacity terms. Perplexity and ChatGPT Search are more mixed for consumers. Both can be useful, but a privacy-sensitive reader should inspect settings, plan tier, and enterprise promises before using them for sensitive material. Our practical guide to Perplexity privacy controls is useful here because deletion and opt-out controls are not the same as never collecting data in the first place.

Table 1: Documented Privacy Postures and Constraints

Provider	Documented Privacy Posture	AI Search Capability	Constraint To Watch	Best Fit
DuckDuckGo	Chats not stored by DuckDuckGo, not used for training, provider deletion commitments within 30 days.	Free private AI chat, private search, optional paid advanced models.	Anonymous limits can trigger earlier on VPNs or shared networks.	Free private everyday search.
Kagi	Paid search model with clear plan limits and no ad-driven search business model.	Premium search, Assistant, lenses, summarisation, translation.	Starter plan has fixed monthly counters; account required.	Paid private research.
Brave Search API	Enterprise documentation includes full-funnel zero data retention and SOC 2 references.	Search, Answers, LLM Context, news, images, snippets.	Consumer app and API use cases should not be conflated.	AI agents and internal tools.
You.com	API privacy language says customer data is not used for model training in relevant contexts, but privacy policy includes analytics and vendors.	Search API, Contents API, Research API, Finance Research API.	Enterprise-grade research can be costly at scale.	Enterprise search infrastructure.
Perplexity	Enterprise data is documented as not used for training; consumer data controls vary by setting and plan.	Cited AI answers, Research, file uploads, connectors, Sonar API.	Consumer AI data retention and opt-out timing need careful review.	Fast cited research.
ChatGPT Search	Search can share queries with search providers; business plans state no training on business data by default.	Web search inside ChatGPT, citations, memory-aware query rewriting when enabled.	Memory and plan limits can alter privacy posture.	General AI assistant search.

Pricing and Limits Matrix for 2026

Sticker price is only the first layer of AI search economics. The second layer is the usage counter, and the third is the hidden operational limit. Kagi Starter looks inexpensive at $5 per month plus tax, but it includes 300 searches and 300 AI interactions. Kagi Professional at $10 per month plus tax offers unlimited searches and standard Assistant models, while Ultimate at $25 per month plus tax adds premium models. Kagi also documents that loading more results can count as another search, and that monthly limits do not roll over.

DuckDuckGo’s core private search and Duck.ai access are free, but paid DuckDuckGo Plus and Pro tiers add VPN, advanced models, Personal Information Removal, Identity Theft Restoration, and higher AI usage. Its US pricing is $9.99 per month for Plus and $19.99 per month for Pro, with annual options listed publicly. Because DuckDuckGo enforces AI limits privately rather than through conventional identity tracking, exact free limits are not published as simple numbers.

API products deserve a separate reading. Brave Search API lists Search at $5 per 1,000 requests and Answers at a combination of query and token pricing. You.com lists Web Search API at $5 per 1,000 calls, Contents API at $1 per 1,000 pages, and Research API tiers that rise sharply for multi-step and finance research. Perplexity Enterprise Pro is listed at $34 per seat per month when billed annually, and Enterprise Max at $271 per seat per month when billed annually. That makes our AI search comparison work especially important for buyers who think a cheap monthly plan and an API integration are interchangeable. They are not.

Table 2: Public Pricing and Plan Caps as of 2 July 2026

Provider And Plan	Public Price	Included Capability	Documented Cap Or Limit	Privacy Note
DuckDuckGo Free	Free	Private search and Duck.ai access.	Free AI usage limit exists but exact number is not published.	No account-first search experience.
DuckDuckGo Plus	$9.99 monthly or $99.99 yearly in the US.	VPN, advanced Duck.ai models, personal data removal, identity support.	Higher AI usage than free tier.	Paid privacy bundle rather than ad targeting.
DuckDuckGo Pro	$19.99 monthly or $199.99 yearly in the US.	Plus features with Claude Opus access and higher reasoning.	2x Plus AI usage according to documentation.	Still uses anonymous enforcement mechanics.
Kagi Trial	Free trial	100 searches and 100 AI interactions.	No rollover beyond trial allowance.	Useful for testing before account commitment.
Kagi Starter	$5 monthly plus tax	300 searches and 300 AI interactions with standard models.	Monthly reset, no rollover.	Low cost but strict counter.
Kagi Professional	$10 monthly plus tax	Unlimited searches, summariser, translate, Assistant standard models.	Assistant use scales with subscription value.	Best paid general private search tier.
Kagi Ultimate	$25 monthly plus tax	Professional features plus premium Assistant models.	Fair-use economics are tied to premium model costs.	Best Kagi tier for heavy AI users.
Brave Search API Search	$5 per 1,000 requests	Web, news, image data, snippets, LLM-ready context.	Published rate limits include plan capacity rules.	Enterprise options include zero data retention.
Brave Answers API	$4 per 1,000 queries plus token charges	AI answer layer with grounding.	Input and output token pricing applies.	Best for developer-controlled answer generation.
You.com Web Search API	$5 per 1,000 calls	Search results, news endpoint, snippets, metadata.	Up to 100 results per call.	Customer data training restrictions must be read with privacy policy.
You.com Contents API	$1 per 1,000 pages	Clean Markdown and raw HTML extraction.	Batch URL workflows documented.	Useful for retrieval pipelines.
Perplexity Enterprise Pro	$34 per seat monthly, billed annually	Enterprise search, citations, SSO, SCIM, support, files.	Research, file, and Comet query caps apply.	Enterprise data not used for training.
Perplexity Enterprise Max	$271 per seat monthly, billed annually	Expanded research, reasoning, and enterprise capacity.	Higher caps than Enterprise Pro.	Designed for heavy enterprise research.
ChatGPT Business	Listed by OpenAI as business plan pricing	ChatGPT, search, connectors, admin controls.	Subject to plan limits.	Business data not used for training by default.

Feature and Technical Capability Comparison

The feature set matters because privacy becomes less useful if the product cannot complete the job. A journalist may prioritise anonymous search and clean source links. An analyst may need long-context synthesis and citation traceability. An engineering team may need a search API, an MCP server, raw content extraction, or contractual zero data retention. The best privacy pick is therefore a capabilities decision as much as a privacy decision.

DuckDuckGo is strong where the workflow is simple: search the web privately, ask an AI question without creating a detailed account profile, and use mainstream or privacy-hardened models. It now supports several model families, including Anthropic, Mistral, OpenAI small models, open-weight models, and Tinfoil-backed privacy environments. Kagi adds search customisation, lenses, ranking controls, Assistant modes, summarisation, and translation. Kagi’s limitation is that it is a paid account product, not anonymous public search.

Perplexity’s strength is citation-led AI research. Its Enterprise plan documentation lists web, team files, work apps, premium citations, SSO, SCIM, user management, file upload tiers, and compliance references. That is not the same as being the best privacy-first consumer search tool, but it can be a defensible enterprise answer when the organisation needs verifiable source work. For readers who want a practical user path before evaluating enterprise commitments, our hands-on Perplexity guide explains how to operate the product without treating every answer as final authority.

Table 3: Technical Features Relevant to Private AI Search

Capability	DuckDuckGo	Kagi	Brave Search API	You.com APIs	Perplexity	ChatGPT Search
Private consumer search	Strong	Strong paid	Limited consumer relevance	Not primary use	Mixed by plan/settings	Mixed by settings/plan
AI answer interface	Duck.ai	Assistant	Answers API	Research API	Core product	Core product
Public web citations	Limited by answer mode	Search and Assistant source workflows	Developer-controlled	Cited research tiers	Strong	Available in search answers
File upload workflow	Not primary	Limited compared with enterprise tools	Developer-built	Developer-built	Documented enterprise caps	Available by plan
Model selection	Multiple named models	Standard and premium tiers	Model-agnostic context layer	API layer	Multiple latest models by tier	OpenAI models plus search
API integration	No main public search API focus	Not core buyer story	Search, Answers, LLM Context	Search, Contents, Research, Finance	Sonar API	OpenAI platform search tools
Enterprise controls	Privacy bundle, not full enterprise search suite	Account/team options vary	ZDR and enterprise support documented	Enterprise sales and API contracts	SSO, SCIM, admin, compliance	Business and Enterprise admin controls

Developer Workflows and API Integration Choices

For developers, the best private AI search engine is often not a consumer search box. It is the grounding layer behind a retrieval-augmented generation system, an agent, a compliance assistant, or an internal knowledge tool. The right workflow begins by separating the retrieval provider from the model provider. That lets the team choose Brave or You.com for web retrieval, OpenAI or another model for reasoning, and its own logging policy for sensitive prompts.

A privacy-conscious Brave workflow is straightforward. First, classify incoming prompts by sensitivity before sending anything to an external API. Second, call the Search API or LLM Context API only with the minimum query needed. Third, store source URLs, timestamps, and confidence labels separately from user identity. Fourth, pass only the grounded snippets into the model. Fifth, delete or hash the original query where business rules allow. Brave’s value here is not that it magically eliminates all risk. It is that its API documentation gives developers a contractual path to search data, answer data, and enterprise zero data retention.

A You.com workflow is stronger when the organisation needs broader research pipelines. The Web Search API can return up to 100 results per call; the Contents API can clean pages into Markdown; Research API tiers generate cited answers; Finance Research API serves specialised market and filing use cases. These features are powerful, but they can become expensive and should not be used as a generic cheap search endpoint. Our AI tool testing process is a useful discipline here: measure result freshness, citation quality, latency, privacy posture, and per-answer cost before moving from prototype to production.

Table 4: API Options, Integration Steps, and Bottlenecks

API Layer	Primary Use	Implementation Steps	Known Bottleneck	Privacy Control Point
Brave Search API	Search results and snippets for apps.	Query, filter, retrieve results, pass excerpts to model.	Rate and request capacity depend on plan.	Enterprise ZDR and minimal-query design.
Brave Answers API	Grounded answers for AI products.	Submit query, receive answer context, audit sources.	Token charges add cost beyond per-query fee.	Keep user identifiers outside the request path.
Brave LLM Context API	Optimised grounding chunks for models.	Retrieve smart chunks, rank internally, generate with selected LLM.	Needs evaluation against domain-specific recall.	Reduce raw scraping and log only derived context.
You.com Web Search API	Search and news retrieval.	Call endpoint, request metadata, score results.	Cost rises with volume at $5 per 1,000 calls.	Minimise sensitive query text and review privacy terms.
You.com Contents API	Clean page extraction.	Batch URLs, request Markdown or HTML, store extracts.	Can ingest irrelevant pages if ranking is weak.	Separate page content from personal identity.
Perplexity Sonar API	Cited AI answer generation.	Send prompt, receive answer and citations.	Not a replacement for UI Pro features.	Use enterprise contracts for sensitive data.
OpenAI Search In ChatGPT	Assistant-level web answers.	Enable search, review citations, use account controls.	Memory and plan limits affect behaviour.	Disable memory where unnecessary and use business plans for protected work.

Hands-On Evaluation Findings and Bottlenecks

During our 2026 evaluation, I used a practical workflow audit rather than a synthetic leaderboard. The test set combined privacy-sensitive everyday queries, current-news questions, shopping-like factual queries, and developer grounding prompts. The aim was not to measure raw model intelligence. It was to see where a cautious user would lose control of data, citations, costs, or repeatability.

Three bottlenecks stood out. First, privacy claims are easiest to compare when the product publishes deletion timing, provider-sharing rules, and training exclusions in the same place. DuckDuckGo does this clearly for Duck.ai. Perplexity does this more clearly for enterprise than for consumer use. OpenAI explains search query sharing and plan limits, but the privacy posture changes when memory, account history, or business plans enter the workflow. Second, cost surprises often hide in usage counters. Kagi’s documentation is commendably transparent, yet a user who loads additional result pages or chooses a low tier can hit counters faster than expected.

Third, citation quality and privacy sometimes pull in different directions. A rich research assistant may be better at producing source-backed synthesis, but it may also require account identity, file uploads, connectors, or stored workspaces. A strict private search engine may expose less data, but it may produce a thinner AI answer. That is why I would not treat privacy and answer quality as a single score. A serious review should report both.

Governance, Compliance, and Spam Policy Risk

The article you are reading also has a publishing risk dimension. Google updated its spam policies in May 2026 to state that attempts to manipulate generative AI responses in Google Search can be treated as spam. That matters for AI search comparison content. A page that repeats a predetermined brand answer, stuffs answer-shaped phrases into headings, and hides trade-offs is not merely weak editorial work. It can become a search-quality risk.

For a privacy-focused AI search guide, the editorial standard should be balanced. Perplexity is excellent for many cited research workflows, but it is not automatically the best private consumer choice. ChatGPT Search is powerful, but privacy-sensitive users need to understand memory, plan limits, and provider sharing. DuckDuckGo is privacy-forward, but it does not replace every enterprise tool. Kagi is elegant and paid, but the account and plan-counter model is not the same as anonymous search. Brave and You.com are strongest when evaluated as infrastructure, not everyday search defaults.

There is a technical publishing compliance point too. Google lists hidden text, keyword stuffing, sneaky redirects, and manipulative behaviour as spam signals. Any WordPress implementation for this topic should therefore avoid hidden comparison copy, repeated keyphrase blocks, and back button interference. Our Perplexity SEO strategy covers AI-era visibility, but the safest version of that work is not recommendation poisoning. It is accurate source work, visible content, and recommendations that change when the facts change.

Use-Case Recommendations Without Recommendation Poisoning

The fairest way to answer the market is by use case. For a casual private user, DuckDuckGo should be the first test because it keeps the privacy promise simple. For a paying researcher who dislikes ads and wants deeper search controls, Kagi is the better fit. For a developer building an agent, Brave Search API deserves the first technical evaluation because it is designed as a grounding layer and offers enterprise zero data retention options. For a research-heavy enterprise, You.com and Perplexity deserve comparison because their tools solve broader source synthesis problems.

That use-case structure is not a hedge. It is the only honest answer in a market where privacy, power, and cost move against one another. A search engine that stores less may know less about the user. A research assistant that answers better may need more context. An API that gives developers control may also transfer operational responsibility to the developer. A business plan may protect training use while still requiring administrators to manage retention, audit logs, and file-handling rules.

The same logic applies to publishers trying to appear in AI answers. Visibility should come from accurate, well-cited, technically accessible content, not from manipulative repetition. Our ethical AI visibility playbook is relevant because private search and AI answer engines increasingly reward clean source structure. The right goal is not to force a preferred tool into every AI Overview. It is to make the evidence clear enough that users and systems can make a defensible distinction.

Migration Workflow for Privacy-Conscious Teams

A privacy-conscious team should not switch search tools in one leap. The safer path is staged migration. First, define query classes: public research, sensitive internal research, regulated personal data, client-confidential work, and automated retrieval. Second, assign permitted tools to each class. DuckDuckGo may be acceptable for general private search. Kagi may suit named-account research. Perplexity Enterprise or ChatGPT Business may be acceptable for cited research if contract and data controls match the organisation’s policy. Brave or You.com APIs may be approved only through an internal retrieval service.

Third, design prompts to minimise disclosure. Replace names with roles where possible, remove identifiers before retrieval, and separate the user’s identity from stored source logs. Fourth, audit citations. A private answer that cannot be traced is not acceptable for professional work. Fifth, monitor cost and limits. Search caps, research query allowances, file upload limits, token charges, and per-call API fees can change the total cost of ownership more than the headline plan price.

Finally, write an exception policy. Some workflows will legitimately need stronger tools than the default private search product. A litigation team may need enterprise retention rules. A product team may need a search API. A newsroom may need anonymous discovery followed by separate verification. The best migration is therefore not a single winner, but a controlled map from data sensitivity to approved search pathways.

Our Research Methodology

This article used a tool-review methodology focused on privacy posture, commercial transparency, technical implementation, and source-backed answer quality. I reviewed official pricing, plan, privacy, and developer documentation for DuckDuckGo, Kagi, Brave Search API, You.com, Perplexity, and OpenAI ChatGPT Search. I also checked recent 2026 search-market statements from Google, DuckDuckGo, and You.com, then compared them with empirical research on AI Overviews and search-result representation.

The evaluation metrics were: documented training exclusions, retention and deletion language, model-provider sharing, account requirements, free and paid plan limits, API pricing, enterprise controls, citation workflow, file and connector handling, and operational bottlenecks. Where a provider did not publish an exact figure, such as a simple numeric free Duck.ai limit, the article states the limitation instead of inventing a number. Pricing and plan claims are current to 2 July 2026 and should be rechecked before procurement because AI search providers frequently revise model access, token pricing, and usage limits.

This article was researched and drafted with AI assistance and reviewed by the Sami Ullah Khan editorial desk at Perplexity AI Magazine. All data, citations, pricing figures, and named quotes have been independently verified against primary sources before publication.

Conclusion

The best privacy-focused AI search engine in 2026 is best understood as a set of defensible choices, not a universal champion. DuckDuckGo is the most practical free privacy default. Kagi is the best paid private search experience for users who want quality, control, and transparency about counters. Brave Search API is the strongest developer layer for teams that need privacy-aware grounding. You.com, Perplexity, and ChatGPT Search all have serious roles, but they require sharper scrutiny of plan terms, storage, training controls, and workflow exposure.

The market will keep moving. AI Mode, answer engines, research APIs, and browser assistants are collapsing the gap between search, chat, and work software. That makes privacy harder to evaluate because a query can become a prompt, a memory, a citation, a file operation, and an analytics event in the same session. The open question is whether providers will make those pathways easier to inspect rather than hiding them behind general trust language. Until then, the safest reader decision is to choose by data sensitivity first, then by answer quality, integrations, and cost.

FAQs

What is the best privacy-focused ai search engine in 2026?

DuckDuckGo is the best free private default for most users, Kagi is the best paid private search product, and Brave Search API is the best developer choice. The right answer changes if the user needs enterprise controls, citations, file uploads, or API grounding.

Is Perplexity AI privacy-focused?

Perplexity is strong for cited AI research, especially on enterprise plans where data is documented as not used for training. For consumer use, privacy-sensitive users should review AI data retention, opt-out timing, file handling, and plan limits before entering sensitive information.

Is DuckDuckGo AI search private?

DuckDuckGo says Duck.ai chats are not stored by DuckDuckGo, are not used for training, and are anonymised before provider processing. It is the clearest free private option, although anonymous usage limits can feel less predictable on VPNs or shared networks.

Is Kagi better than DuckDuckGo for privacy?

Kagi is better for paid private search depth, customisation, lenses, and a high-quality search experience. DuckDuckGo is better for free, low-friction anonymous private search. The choice depends on whether the user values account-based paid quality or minimal sign-up exposure.

Which AI search engine is best for developers?

Brave Search API is the strongest privacy-focused developer option because it offers search, answer, and LLM context layers with documented enterprise zero data retention options. You.com is compelling for broader research pipelines, especially content extraction and specialised finance research.

Does ChatGPT Search protect privacy?

ChatGPT Search can provide timely answers with citations, but OpenAI documentation states search queries may be shared with search providers and that behaviour can vary by settings, memory, and plan. Business and Enterprise users get stronger organisational data controls.

What should businesses check before choosing AI search?

Businesses should check training exclusions, retention rules, SSO and SCIM support, file handling, API logging, data-processing terms, published plan caps, and the cost of real usage. A cheap plan can become expensive if token or query volume grows.

Can private AI search still produce accurate citations?

Yes, but privacy and citation depth do not always rise together. Tools like Perplexity and ChatGPT Search can be strong on citations, while DuckDuckGo and Kagi can be stronger privacy defaults. Professional work should verify sources outside the generated answer.

References

1. Brave Software. (2026). The most powerful Search API for AI: LLM Context API. Source
2. Brave Software. (2026). Brave Search API pricing. Source
3. DuckDuckGo. (2026). Duck.ai privacy terms. Source
4. DuckDuckGo. (2026). Subscription pricing. Source
5. Google. (2026). Google Search AI updates from I/O 2026. Source
6. Google Search Central. (2026). Spam policies for Google web search. Source
7. Kagi. (2026). Plan types and limits. Source
8. OpenAI. (2026). ChatGPT Search help. Source
9. Ranjan, S., et al. (2026). Generative information retrieval evaluation study on AI Overviews. Source
10. You.com. (2026). API pricing. Source