Perigon vs Tavily: full comparison for 2026
Quick verdict
Perigon (4.5/5) edges ahead of Tavily (4.1/5) overall. Perigon is the better choice for indie developers and startups wanting enriched data at a known price. Tavily is the stronger option for agent builders adding a news tool in a few lines. The right pick depends on your budget, how much history you need and whether you need full text.
Perigon vs Tavily: head-to-head summary
| Criterion | Perigon | Tavily |
|---|---|---|
| Founded | 2022 | Not disclosed |
| HQ | Austin, Texas, USA | New York and Tel Aviv; part of Nebius |
| Team size | 11–50 | Part of Nebius |
| Rating | 4.5 / 5 | 4.1 / 5 |
| Primary differentiator | Entity, topic and cluster data from the $25 plan up, with all tiers priced publicly | Built-in LangChain and LlamaIndex integrations that return prompt-ready text |
| Pricing model | Monthly plans; add-ons for real-time and volume | Credits; free monthly allowance; pay-as-you-go |
| Entry price | $25/mo | $30/mo |
| Formats & SDKs | REST API, JSON, Python SDK | REST API, JSON, Python SDK |
| Typical uses | AI & LLM apps, Media monitoring, Risk & compliance, Marketing & PR | AI & LLM apps, Research & academia |
Perigon vs Tavily: overview
Perigon
Perigon is an Austin, Texas startup founded in 2022 by Joshua Dziabiak and Josh Rickel, backed by a $5 million seed round that LiveOak Ventures led in February 2024. For developers its main draw is that the pricing page tells you what you will pay: Individual at $25 a month for personal use, Startup at $250 with limited commercial rights, and Commercial at $1,250 with real-time data, an SLA and SSO. Every tier returns enriched articles with entities, topics, summaries and story clusters from 230,000+ sources (per company website; independently unverifiable), and Monitors let you save a query and get matches on a schedule. Plan for the delay: below Commercial, data lags by an hour unless you pay for the real-time add-on.
Tavily
Tavily, founded by CEO Rotem Weiss, is a search API designed to be called by LLM agents, and its topic=news mode limits results to recent coverage with extracted text ready for a prompt. It is the quickest way on this list to give a LangChain or LlamaIndex agent a news tool, since both frameworks ship Tavily integrations. Nebius, the Nasdaq-listed AI cloud company, agreed to buy Tavily in February 2026 (Bloomberg put the price at $275 million) and later said the deal had closed, keeping the brand and pricing. Credits cost about $0.008 each on pay-as-you-go, a basic search uses one and an advanced search two, and 1,000 credits a month are free. Because it is a search tool, there is no archive, source list or clustering to work with.
Features: Perigon vs Tavily
| Capability | Perigon | Tavily |
|---|---|---|
| Full article text | ✓ | ✓ |
| Historical archive | ✓ | ✗ |
| NLP enrichment | ✓ | ✗ |
| Story/event clustering | ✓ | ✗ |
| Official SDK | ✓ | ✓ |
| Streaming or webhooks | ✓ | ✗ |
| Free access | ✓ | ✓ |
Formats and SDKs: Perigon vs Tavily
| Format / SDK | Perigon | Tavily |
|---|---|---|
| REST API | ✓ | ✓ |
| Python SDK | ✓ | ✓ |
| Node.js SDK | ✓ | ✓ |
| Webhooks | ✓ | N/A |
| WebSocket | N/A | N/A |
| MCP server | N/A | ✓ |
| S3/bulk delivery | N/A | N/A |
| BigQuery | N/A | N/A |
Pricing comparison: Perigon vs Tavily
| Criterion | Perigon | Tavily |
|---|---|---|
| Entry price | $25/mo | $30/mo |
| Pricing model | Monthly plans; add-ons for real-time and volume | Credits; free monthly allowance; pay-as-you-go |
| Access models | Free trial, Monthly plans, Enterprise contract | Free tier, Monthly plans, Pay-as-you-go, Enterprise contract |
| Price transparency | Public price | Public price |
| Price tier | Budget | Budget |
Who each API suits: Perigon vs Tavily
| Dimension | Perigon | Tavily |
|---|---|---|
| Buyer type | Individuals to enterprise | Individuals to enterprise |
| Typical uses | AI & LLM apps, Media monitoring, Risk & compliance | AI & LLM apps, Research & academia |
| Example use cases | A startup MVP that needs commercial rights from the first month. Alerting on a brand or topic without building a scheduler. | Letting an assistant answer questions about today's news. Agents that summarise what was published this week. |
| How you get access | Free trial | Free tier |
Perigon vs Tavily: pros and cons
| Perigon | |
|---|---|
| + | The cheapest plan already includes enrichment, which most rivals reserve for higher tiers |
| + | Monitors replace a cron job and a diffing script with a saved query |
| + | Python and Node SDKs, plus webhooks for pushed results |
| + | The license explains what you may do with the structured data, which saves a round of legal questions |
| - | Data lags by an hour below the $1,250 plan unless you buy real-time |
| - | The Startup tier caps you at 10,000 requests a month |
| - | Republishing full article text is outside the license |
| Tavily | |
|---|---|
| + | Agent frameworks already support it, so setup is mostly an API key |
| + | 1,000 free credits a month cover a prototype |
| + | Extracted content arrives with results, with no separate scraper |
| + | Nebius ownership adds funding and compute behind the service |
| - | No archive, so historical questions go unanswered |
| - | No control over which sources are searched |
| - | Roadmap may change under new ownership |
Who should choose Perigon?
A typical fit: a startup MVP that needs commercial rights from the first month.
Entity, topic and cluster data from the $25 plan up, with all tiers priced publicly. Paid access starts at $25/mo. Typical uses: AI & LLM apps, Media monitoring, Risk & compliance, Marketing & PR.
Who should choose Tavily?
A typical fit: letting an assistant answer questions about today's news.
Built-in LangChain and LlamaIndex integrations that return prompt-ready text. Paid access starts at $30/mo. Typical uses: AI & LLM apps, Research & academia.
Decision matrix: Perigon vs Tavily
| Your situation | Recommended choice |
|---|---|
| You want to start free before paying | Both |
| Your budget is at the lower end | Perigon |
| You need full article text in the response | Both |
| You need years of historical news | Perigon |
| You want an official SDK | Both |
| You are wiring news into an LLM agent | Both |
Use case fit: Perigon vs Tavily
| Use case | Perigon fit | Tavily fit | Winner |
|---|---|---|---|
| AI & LLM apps | Strong | Strong | Both equally |
| Media monitoring | Strong | Limited | Perigon |
| Risk & compliance | Strong | Limited | Perigon |
| Marketing & PR | Strong | Limited | Perigon |
| Research & academia | Limited | Strong | Tavily |
Verdict: Perigon vs Tavily
Perigon (4.5/5) is the stronger overall choice as a news data source. Entity, topic and cluster data from the $25 plan up, with all tiers priced publicly.
Tavily (4.1/5) is worth a look if you need agents that summarise what was published this week. If your situation matches that, Tavily is a competitive option.
Related comparisons
Perigon vs Tavily FAQ
Is Perigon better than Tavily?
Perigon (4.5/5) scores higher overall, but "better" depends on your use case. Perigon's strongest advantage: the cheapest plan already includes enrichment, which most rivals reserve for higher tiers. Tavily's strongest advantage: agent frameworks already support it, so setup is mostly an API key.
How do Perigon and Tavily differ in pricing?
Perigon: monthly plans; add-ons for real-time and volume, with paid access from $25/mo. Tavily: credits; free monthly allowance; pay-as-you-go, with paid access from $30/mo. Both offer some free access for testing.
Which is better for enterprise: Perigon or Tavily?
Both sell enterprise contracts. Neither carries licensed publisher content, so check copyright terms before republishing anything.
What are the main differences between Perigon and Tavily?
What sets Perigon apart: Entity, topic and cluster data from the $25 plan up, with all tiers priced publicly. What sets Tavily apart: built-in LangChain and LlamaIndex integrations that return prompt-ready text. They also differ on entry price ($25/mo vs $30/mo) and typical uses (AI & LLM apps, Media monitoring vs AI & LLM apps, Research & academia).
Verify all details directly with each API before making a decision.