TinyFish vs Tavily: APIs, Features, and Pricing Compared

TinyFish and Tavily provide APIs that help AI agents find and use information from the web. Tavily offers web search, content extraction, website crawling, and research tools. TinyFish provides search through TinySearch and page retrieval through TinyFetch, alongside TinyBrowser and TinyAgent for tasks that require interacting with websites.

If your application uses Tavily primarily for search and extraction, TinyFish offers an opportunity to lower retrieval costs: TinySearch and TinyFetch are free within their published standard limits. Your application can keep its existing agent logic while you update the API calls and response handling. Workflows that depend on Tavily’s crawling, generated answers, or research features require additional evaluation.

This guide compares search and retrieval results, explains the API differences, and walks through the changes needed to move an existing Tavily workload to TinyFish.

TinyFish vs Tavily at a glance

TinyFish and Tavily overlap across search and content retrieval, but they differ in how those capabilities are packaged and what your application needs to handle itself. Here is a quick look at where each platform fits.

Capability details come from the TinyFish API catalog and Tavily API catalog. Availability and integration differences are covered below.

Tavily Search API vs TinySearch

Tavily Search and TinySearch both help applications discover relevant web sources, but they return different kinds of output and expose different controls. The key differences are in generated answers, result structure, search depth, filtering, and freshness.

TinySearch vs Tavily Search Results and Generated Answers

The Tavily Search API can return source content, relevance scores, an optional generated answer, and optional raw_content. TinySearch returns source metadata with title, url, snippet, and position fields. Its standard response is source discovery rather than a generated answer.

For an illustrative query such as “PostgreSQL logical replication limitations,” the application would consume these responses differently:

If your existing agent already selects sources and writes the answer, TinySearch can supply discovery without requiring you to change that reasoning. If you currently consume Tavily’s generated answer, you must preserve that step explicitly when changing providers. See the TinySearch response reference.

Search depth, filters, and freshness

Both APIs let you narrow source discovery, but their controls differ in ways that affect integration and the content your application receives.

These controls are documented in the TinySearch reference and Tavily Search reference.

Tavily’s depth settings also change the response: basic, fast, and advanced return query-ranked chunks, while ultra-fast returns page summaries. TinySearch suits applications that already select sources and generate answers themselves; Tavily exposes more choices within the search request.

For changing information such as prices, publication dates alone are insufficient. Tavily can request live content with max_age_hours: 0; TinyFetch uses ‘ttl: 0’ when retrieving a selected URL. Keep freshness settings consistent during evaluation, because cached and live retrieval can produce different content and latency.

Published search quality and latency

TinyFish’s published Tavily comparison reports advantages in both answer results and request latency on its evaluated samples.

For an application that searches before responding, the 553 ms median difference could reduce time spent waiting for retrieval. The lower p99 is also relevant when a slow search call can delay an otherwise fast workflow. Neither figure measures the application’s complete response time, which also includes model processing and any subsequent page retrieval.

The six-percentage-point difference in end-to-end answer rate supports evaluating TinyFish for workloads where retrieved evidence determines whether an answer can be completed. It does not establish fewer retries, lower model bills, or better task completion in your application; those outcomes require testing with your queries and agent.

These are historical, vendor-published results. The original search run date and full provider configuration are not disclosed publicly, so the table cannot establish superiority over every current Tavily depth setting. Evaluate the settings you would deploy, including content freshness, response size, latency, and cost.

TinyFetch vs Tavily Extract: Content quality and API behavior

TinyFetch and Tavily Extract both retrieve content from known URLs, but they differ in how that content is returned and how much control the application has over the result. Those differences matter for full-page retrieval, focused passages, batching, and failure handling.

TinyFetch vs Tavily Extract for Full Pages and Focused Passages

Tavily Extract returns content in raw_content. Adding a query reranks passages by relevance; chunks_per_source controls how many are returned. This is useful when an application needs a specific policy clause rather than an entire document.

TinyFetch offers Markdown, semantic HTML, and a JSON document tree. Its JSON format represents document structure; it is not a guarantee of application-specific fields such as invoice number or contract value. Those require a separate extraction step.

TinyFetch also documents a Highlights beta for ranked passages. It requires account enablement, and text is null by default unless full-page text is requested alongside highlights. A migration from Tavily’s query-ranked chunks therefore needs both an availability check and response mapping. The Fetch examples show that distinction.

For a contract-summary workflow, test whether the returned text preserves the clause and its qualifications. A shorter response only helps if it retains the evidence needed for the answer.

Batching, timeouts, and partial failures

Tavily accepts up to 20 URLs per Extract request; TinyFetch accepts up to 10. Tavily separates results from failed_results, while TinyFetch uses results and errors. A 20-URL batch therefore becomes at least two TinyFetch requests. See the Tavily batching guidance and TinyFetch reference.

Check each URL’s outcome. Tavily explicitly documents that HTTP 200 can contain an empty results array when all valid URLs fail. TinyFetch can return a timeout for one URL while others complete. Match outcomes by URL, validate the content, and retry the failed subset rather than repeating every successful extraction.

Published extraction-quality results

TinyFish’s fetch-quality evaluation tested 45 URLs on June 2, 2026, using default configurations and no retries.

“Usable” required at least 1,000 returned characters and more than 30% content signal. Claude Sonnet 4.6 judged content versus surrounding noise; outputs above 30,000 characters were truncated for judging.

The results favor TinyFish on that set, but the evaluation did not test current tuned configurations or downstream answer accuracy. Its minimum-length rule also suits page extraction rather than intentional short passages. Use it as evidence for evaluating coverage and cleanup, not as a universal token-savings estimate.

Tavily Crawl and Research vs TinyFish Browser and Agent Workflows

The platforms diverge more clearly beyond search and extraction. Tavily extends into site mapping, crawling, and research, while TinyFish adds browser control and goal-based website execution alongside its research capabilities.

Website mapping and crawling

Tavily Map discovers URLs across a site. Tavily Crawl traverses links and extracts content, with depth, breadth, path, and page-count controls.

For a documentation-ingestion job, those controls specify which parts of a site to explore and how far to go. TinySearch and TinyFetch can participate in a custom discovery-and-extraction pipeline, but replacing individual fetches does not replace crawl traversal, URL deduplication, or job orchestration. Tavily is a more direct fit when a dedicated site crawl is central to the application.

Research reports

Both platforms can turn a research question into a cited report. Compare their input and output contracts separately from their basic search APIs.

See the TinyFish Research reference, Tavily Research reference, and Tavily task-retrieval reference.

For an application that writes research directly into database fields, Tavily’s explicit output schema is a material integration feature. With TinyFish, verify how the report and citations will map into your application’s required structure. Neither provider’s mode names establish equivalent research depth or quality.

Research pricing

• TinyFish: Its Research reference lists two free completed runs per UTC day, followed by $2 per completed deep run or $3 per completed max run. Failed, timed-out, and cancelled runs are not charged.
• Tavily: Research consumes 4–110 credits for mini and 15–250 for pro. At the published $0.008 pay-as-you-go rate, those ranges equal $0.032–$0.88 and $0.12–$2.00 respectively, before allowances or plan discounts. Tavily billing documentation.

TinyFish’s completed-run rates make the API charge easier to forecast; Tavily’s variable credit ranges can start lower. Compare the evidence and completeness of the resulting reports before treating either pricing model as better value.

Authenticated browser tasks

TinyFish’s browser products address work beyond collecting source material. TinyBrowser gives your code direct browser control. TinyAgent accepts a website and goal and carries out browser actions.

Consider a report-retrieval workflow. Finding a public report and reading its contents is retrieval. Signing into a supplier portal, selecting a reporting period, and navigating to an export is an interactive task. TinyFish offers that execution layer within the same platform; Tavily’s compared endpoints would need a separate browser controller.

TinyAgent’s Browser Context Profiles preserve saved login state. When paired with a matching Vault credential, they can support signing in again after the session expires. Validate authentication and the actual downloaded output for your target portal. This example illustrates the architecture; it is not a reported test run.

Integration and Tavily API rate limits

Replacing Tavily involves more than changing an API key. Keep provider API keys server-side and avoid exposing them directly in client applications:

Keep an internal result format in your application, then map each provider’s fields into it. Review score thresholds, answer dependencies, and focused-passage behavior separately; different fields do not necessarily represent equivalent information.

Tavily API rate limits list 100 requests per minute for development keys and 1,000 for production keys, with separate Crawl and Research limits. A migration should be sized against peak throughput as well as monthly credit use, because TinyFish’s standard free limits and Tavily’s production-key limits are structured differently. These differ substantially from TinyFish’s standard free limits, so check peak traffic as well as monthly totals.

Both platforms also offer Model Context Protocol (MCP) integration: Tavily’s MCP server exposes its web tools, while TinyFish’s integrations provide access from compatible clients. Tool names, arguments, and enabled capabilities still need checking; a connector does not make the APIs interchangeable.

TinyFish and Tavily API pricing

The two platforms use different pricing models, so the useful question is not just the price of one request, but how those costs scale across the workload your application actually runs.

Published rates checked November 5, 2026. Contract pricing may differ.

Tavily pricing includes 1,000 free monthly credits and pay-as-you-go at $0.008 per credit. Its Project plan lists 4,000 credits for $30 per month. Credit usage depends on the operation; failed URL extractions are not charged.

TinyFish pricing lists these standard free retrieval limits:

• TinySearch: 30 requests per minute and 500 per hour.
• TinyFetch: 150 URLs per minute and 1,000 per day.

TinyBrowser and TinyAgent draw from a prepaid Wallet, without a recurring subscription or monthly minimum. Confirm higher-capacity terms before planning a workload above the standard limits. Research runs have separate billing and are excluded from the retrieval example below; confirm account access and current Research terms before budgeting them.

Example: 10,000 searches and 10,000 extracted pages

Assume 10,000 basic searches and 10,000 successful basic URL extractions:

• Tavily: Under Tavily’s current basic extraction credit rate, 10,000 searches consume 10,000 credits and 10,000 successful URL extractions consume 2,000 credits, for 12,000 credits total.
• TinyFish: $0 in Search and Fetch API charges when traffic stays within the published limits. Fetching 10,000 URLs requires at least 10 daily allowance periods, with requests also paced to the per-minute cap.

This illustrates the retrieval-cost difference; it does not guarantee the same reduction in your final bill. Add model processing, browser usage, and integration work when comparing the full application cost.

Which is better: TinyFish or Tavily?

TinyFish is a strong fit when your application needs search and page retrieval within its free limits, especially if the next step involves interacting with a website. It gives you a path from finding a source to operating a browser without adding another execution provider.

Tavily fits applications that depend on its search-depth controls, generated answers, or dedicated mapping and crawling. Its production-key throughput may also suit traffic that exceeds TinyFish’s standard retrieval limits, subject to each account’s terms.

For existing Tavily users, start with Search and Extract calls whose outputs you can verify. Compare the content your application receives, the answers it produces, and the capacity you need. That will show whether TinyFish’s retrieval pricing and operating limits are a better fit for your workload. That establishes whether TinyFish’s lower retrieval charges translate into a better fit for your workload.

Related reading:

• Best Web Search APIs for AI Agents
• 8 Best Tavily Alternatives
• What is TinySearch?
• What is TinyFetch?