At a glance
Tavily vs askFinz
Row by row — what a call buys on each side.
| Compared on | Tavily | askFinz |
|---|---|---|
| What you buy | A search call to wire into your own agent | Workspaces, research agents, a browser extension and a desktop app, on one sign-in |
| What comes back | Results trimmed for feeding to a model | The matched passage and an answer, together |
| Rate per 1,000 searches | $5.00 at their largest published plan, $8.00 pay-as-you-go; advanced search costs double | $0.75 top-up · $1.00 with the extension or a paid plan · $3.50 standalone |
| Who assembles the answer | Tavily can return page content and a short answer; your loop decides what to do with them | Already assembled, with the matched passage and the address it was read from |
| Drops into LangChain / LlamaIndex | Yes, in a few lines | No — there is nothing to import |
| Underlying index | Tavily's own, refreshed on its schedule | Its own, read continuously |
Swipe the table sideways to see both columns.
Tavily credit rates as published in its own documentation, verified 7 September 2026: $0.005 per credit at their largest published plan up to $0.008 pay-as-you-go, one credit per basic search and two per advanced. askFinz rates as published on pricing. Check Tavily's current page before relying on the figure.
What Tavily is
Tavily is a search API built specifically for LLM agents. You send a query, it runs the search, and it returns results already trimmed and shaped for feeding straight back into a model rather than rendered for a person. It is priced per credit, pay-as-you-go, and it has deep integrations with LangChain, LlamaIndex and most of the popular agent frameworks.
What askFinz is
askFinz runs its own crawled web index — pages read continuously rather than fetched in response to a query. A question returns the matched passage alongside the answer, with the address it was read from and at no separate rate, and what the index holds is filed as the kind of document it actually is: a judgment with its jurisdiction, a standard with its revision, a filing under the reference number the issuing body published it with. Documents are read as documents too — PDFs, Word files and slide decks read page by page rather than linked to. On top of the index sits a set of workspaces — Search, Research, Chat, Code, News and more — plus a browser extension and a desktop app, all on one sign-in. It is a product you use, not an API you build against; askFinz does not sell index access as a standalone feed.
How many calls does one answer take?
This is the difference that does not show up in a rate card. A search API returns pointers; something still has to fetch what is behind them, decide which ones matter, and turn the pile into a sentence. With Tavily the search call is priced, the reasoning is your model's tokens, and the glue is code you write and then own for as long as the product lives.
What it costs
At the published rates the gap is real but narrower than a single figure suggests — $5.00 to $8.00 against $0.75 to $3.50 per 1,000, depending which plan each side is on. That said, a rate card is only half a cost: Tavily's integrations can genuinely be cheaper in engineering time if you are already standing up an agent framework, and that is a real cost too.
A component, or a place to work
One of these is a call you build against. The other is somewhere you open and use.
Choose Tavily if
- You are building your own agent and want a search tool that drops into LangChain or LlamaIndex in a few lines. askFinz is not a library; there is nothing to import.
- Your code needs to call search directly. askFinz has no such call to sell you.
- You want control over the retrieval loop — how many results come back, what gets re-queried, how it is re-ranked before the model sees it.
- Your agent runs server-side with no browser and no person in the loop.
- The framework you have already committed to documents Tavily as its default, and the switching cost outweighs the rate difference.
Choose askFinz if
- You want the passage back with the answer and do not want to build extraction, ranking and assembly yourself.
- Cost per search matters at volume: $0.75–$3.50 against $5.00–$8.00 per 1,000 (rates as of 7 September 2026).
- You want the answer to come from an index read continuously, rather than a fresh outbound call each time your agent runs.
- The work continues after the answer — into Research with sources attached, into Storage, into a project someone else can pick up, or out to any other workspace, the browser extension or the desktop app on the same sign-in.
- You want results filed by what a document is — a judgment with its jurisdiction, a standard with its revision, a filing with its reference number kept — rather than returned as an undifferentiated list.
FAQ
Questions people ask
The things people actually ask before choosing between Tavily and askFinz.
5 questions · open one to read the answer
No. Tavily is an API you call from your own code; askFinz is a workspace, a browser extension and a set of research agents. If your architecture needs a search function to call, Tavily fits that shape and askFinz does not.
askFinz reads and holds its own index. That is the substantive difference from most products in this category, though not all of them — Exa, Brave, Parallel, Amazon and a few others operate their own. Tavily is not among them: its documentation describes aggregating sources at the moment you ask rather than holding an index.
Partly because the reading has already happened: the index is read continuously rather than assembled per request, so a question is answered against something already held. The published rates are $0.75 as a top-up, $1.00 with the extension or a paid plan and $3.50 standalone, against Tavily's $8.00 per 1,000 as of 12 August 2026.
Not as a framework tool. askFinz has its own research agents that work against the index directly, but it is not a library you import into someone else's loop.
Both are current in practice, by different routes. Tavily searches when your agent asks. askFinz reads continuously and answers from what it holds, so freshness is a property of the index rather than of the timing of your call — and because the reading is already done, an answer is a lookup rather than a live fetch, including for the PDFs and slide decks that are slowest to read at the moment somebody asks.
See how the index works, or read the fuller case for owning an index. Join the beta and try it against a real question.