unlob vs Exa
Exa is the best-funded neural search API in the category at a $2.2B valuation. unlob is 17.5× cheaper and adds graph traversal and coverage auditing.
Verified 5 August 2026·Figures from vendors' own pages
On the headline rate that is 18× — $7 per 1,000 against $0.40. Both figures are computed from the published plan tables rather than typed into this page, so they follow the vendors rather than drifting from them.
Price
- unlob$0.40
- Exa$7
| Provider | Own index? | $ / 1,000 queries | Source |
|---|---|---|---|
Base search is $7 per 1,000 requests. Results beyond the first 10 add $1 per 1,000, contents add $1 per 1,000 pages, and AI summaries another $1 per 1,000 — so a realistic RAG call costs more than the headline. | Yes Proprietary AI-native semantic index | $7 | Exa pricing |
$29 per month for 50,000 requests. | Yes Our own crawl and index | $0.58 | This page |
$199 per month for 500,000 requests. | Yes Our own crawl and index | $0.40 | This page |
When Exa is the better choice
A comparison page that cannot name a scenario where the competitor wins is an advertisement. Here are the real ones.
Choose Exa
- Websets — building and enriching structured entity sets is a genuinely different product and we have no equivalent.
- Contact enrichment, at $0.02 per email and $0.07 per phone number, for go-to-market workloads.
- A larger index and a longer operating history, which for broad research queries can simply mean better recall.
- Exa Agent, where you want managed research effort levels rather than composing the loop yourself.
Choose unlob
- Price: $0.40 versus $7.00 per 1,000 — and Exa’s effective rate rises with results beyond ten, contents and summaries.
- Graph traversal over the open web, which Exa does not offer.
- Coverage transparency through why_not.
- Corroboration counts that survive deduplication, for agents that must judge whether a claim is independently reported.
The substantive differences
| Exa | unlob | |
|---|---|---|
| Headline price | $7 per 1,000 for base search, plus $1 per 1,000 for results beyond ten, $1 per 1,000 pages of contents, and $1 per 1,000 for summaries. | $0.40 per 1,000 on Scale, with no surcharges — filters, facets and graph calls all bill as ordinary requests. |
| Retrieval model | Neural, trained on link prediction. Strong at "find me things like this". | Hybrid by default — BM25 fused with vectors — with a term filter for exact identifiers that semantic search smooths away. |
| Beyond search | Websets, Answer, Monitors, Agent — a product suite. | A coverage graph — six traversal tools over relationships already computed to build the index. |
| Company | $335M+ raised, $2.2B valuation, backed by a16z, Benchmark, Lightspeed and NVIDIA’s venture arm. | Self-funded and smaller. |
Sources
Every figure on this page was read from the vendor's own pricing page or funding announcement on the date shown.
- Exa: Exa pricingread 5 August 2026
- Exa: Exa Labs raises $250M at $2.2B valuationread 5 August 2026
- Exa: Announcing our Series Bread 5 August 2026
Frequently asked questions
Is unlob a drop-in replacement for Exa?
For plain web search, largely yes — both return ranked results with metadata from their own index, and the filter surfaces overlap. For Websets or contact enrichment there is no equivalent here; those are separate products rather than search parameters.
Why is Exa 17.5× more expensive?
Different cost structures rather than different margins. Exa’s pricing has to support a $2.2 billion valuation and a broad product suite; ours has to support a self-funded company with no valuation to grow into. Both of us own our index, so neither is paying a per-query fee upstream — the difference is what each business needs the price to cover.
How much has Exa raised?
$85 million in a Series B led by Benchmark in September 2025 at a $700M valuation, then $250 million led by Andreessen Horowitz in May 2026 at a $2.2 billion valuation.
Check the claims yourself
The free tier is 10,000 requests a month with no card. That is enough to test coverage, latency and result quality against your own queries — which is a better comparison than this page.