# Parallel vs. Exa
Parallel is faster, more affordable, and more accurate than Exa on everything from quick lookups and grounding to deep, cited web research.
## What does Parallel do?
Parallel is one developer platform covering search, extraction, research, entity discovery, and monitoring. On the agentic endpoints, every field comes back with a citation and a confidence score.
## Parallel vs. Exa at a glance
Published list prices and documented capabilities as of August 2026.
| Dimension | Parallel | Exa |
|---|---|---|
| Search list priceper 1,000 requests, per 1,000 requests | Leads.$1 (Turbo) (Leads) | $7, raised from $5 in March 2026 |
| Agentic multi-hop retrieval (BrowseComp) | Leads.51% (Turbo); 58% at the deepest Task tier (Leads) | 33.7% (Instant) |
| Field-level citations on agentic runs | Leads.Research Basis on Task, FindAll, and Monitor: URL, excerpt, reasoning, confidence (Leads) | Grounding when a run emits it |
| Batch research orchestration | Leads.Task Groups over reusable Task Specs (Leads) | No documented equivalent |
| Change monitoringper 1,000 executions, per 1,000 executions | Leads.$3–$10, event stream plus snapshot value diffs (Leads) | $15, recurring deduplicated search |
| People and company search | Entity Search for ranked results; FindAll for verified sets | Dedicated people and company indexes |
| Framework ecosystem | Vercel AI SDK, n8n, LangChain, LlamaIndex, and more | Leads.LangChain, LlamaIndex, CrewAI, and 25+ more (Leads) |
## Better at finding critical information
Simple lookups are already answered from a model’s weights. The queries that reach a search API are the ones that need a chain of pages followed: find the announcement, open the filing, pull the figure. BrowseComp measures exactly that, and Parallel Turbo scores 51% where Exa Instant scores 33.7%.
The gap holds when you spend more compute. Parallel’s deepest Task tier reaches 58%, above the 57.7% that OpenAI Web Search scored in the same run. On single-hop SimpleQA the two platforms sit close together, which is the point: the harder the query, the wider the margin.
That margin is why Harvey runs its international legal research on Parallel, and why Kepler built its finance research on it: work that professionals have to be able to trust.
BrowseComp, agentic multi-hop
Higher is better
SimpleQA, single-hop factual
Higher is better
Parallel-run evaluation, July 2026: all 1,266 BrowseComp questions, a GPT-5.4 agent and grader, and up to 20 search and fetch calls per question. Parallel Turbo against Exa Instant in the same harness. Exa publishes its own runs with different configurations and results.
## A seventh of the price on search, and it compounds
Parallel Turbo lists at $1 per 1,000 requests against Exa’s $7. The spread widens on the endpoints that run continuously: monitoring costs $3 to $10 per 1,000 executions against $15, and a 100-result entity query costs $5 per 1,000 against roughly $97 once Exa’s per-extra-result charge is applied.
Agents amplify that difference, because a single user request fans out into dozens of retrieval calls. Parallel prices each tier separately, so you can pin cheap retrieval to the broad sweep and reserve deep tiers for the queries that need them. Nooks cut its web search costs 70.5% by switching.
Search, per 1,000 requests
Lower is better
Monitoring, per 1,000 executions
Lower is better
100 entity results, per 1,000 requests
Lower is better
Published list prices, July 2026. Exa charges $7 per 1,000 requests for the first 10 results and $1 per additional result, so 100 results reach about $97. Enterprise pricing differs on both platforms.
## Feature by feature
Pricing, capabilities, performance, integrations, and enterprise readiness, side by side.
### Pricing and total cost
Parallel bills per request at the tier you pick. Exa mixes pay-as-you-go rates with Websets subscriptions, and charges per content type on extraction.
| Feature | Parallel | Exa |
|---|---|---|
| Cheapest searchper 1,000, per 1,000 | Leads.$1 Turbo; $5 Basic or Advanced (Leads) | $7; Deep $12; Deep-Reasoning $15 |
| Results included | Leads.10, then $1 per 1,000 per extra 10 results (Leads) | 10, then $1 per extra result |
| Extractionper 1,000, per 1,000 | $1 per URL, excerpts and full markdown included | $1 per page, per content type |
| Deep researchper 1,000 runs, per 1,000 runs | $5–$2,400 across 8 fixed-price tiers | $12–$1,000, plus search tool calls and enrichment |
| Monitoringper 1,000 executions, per 1,000 executions | Leads.$3–$10 (Leads) | $15, up to 10 results |
| Pricing model | Pay per request per 1,000 | Pay-as-you-go per 1,000 plus Websets plans at $49–$449 per month |
| Free tier | Leads.Up to $80 at signup, plus $5 every month (Leads) | $20 on signup plus $10 every month |
### Core capabilities
Both platforms cover search, extraction, research, and monitoring. They diverge on how much task context retrieval receives and how evidence comes back.
| Feature | Parallel | Exa |
|---|---|---|
| Search input | Leads.An objective plus up to five keyword queries (Leads) | One query; deep tiers accept additional queries |
| Citations on agentic runs | Leads.Research Basis per field on Task, FindAll, and Monitor, with calibrated confidence (Leads) | Grounding on text or fields when a run emits it |
| Sources on search and extract | Ranked pages and excerpts with source URLs, no Basis | Results and contents with source URLs and highlights |
| AI summaries in search | Not offered; Search returns ranked excerpts | Leads.LLM summaries available at extra cost (Leads) |
| Research tiers | 8 fixed-price Task processors, lite through ultra8x | 5 effort levels plus auto |
| Batch orchestration | Leads.Task Groups with group status and bulk retrieval (Leads) | Not documented |
| Monitoring model | Event stream, plus snapshot diffs on structured values | Recurring search, deduplicated, delivered by webhook |
| Entity discovery | Entity Search for candidates; FindAll for verified sets | Company and People Search; Websets workspace |
| Profile shape | The fields you ask for, to your own schema | Fixed profile records from its own indexes |
| Content type filters | Entity Search API for specialized people and company search; additional types via natural language | Company, people, news, papers, financial reports at query time |
| Site crawl and map | Crawler is internal to the index, not an endpoint | Leads.Subpage crawling within Contents (Leads) |
### Performance and scale
Exa advertises sub-200ms on Instant, but across the five suites in Parallel’s July 2026 run it measured 335–361ms p50 against Turbo’s 216–240ms. Both figures are client-side p50, best across runs, measured from us-central. Parallel also allows far more concurrent task throughput.
| Feature | Parallel | Exa |
|---|---|---|
| Search latency, advertised | 200ms p50 (Turbo) | Sub-200ms (Instant); Deep-Reasoning 12–50s |
| Search latency, measured p50 | Leads.216–240ms (Turbo) across five suites (Leads) | 335–361ms (Instant) across the same five suites |
| BrowseComp, Parallel’s July 2026 run | Leads.51% Turbo; 58% Task Ultra8x (Leads) | 33.7% Instant |
| SimpleQA, Parallel’s July 2026 run | 91% Turbo | 89.3% |
| Own web index | Yes | Yes |
| Search rate limit | 600 RPM | 600 RPM |
| Research rate limit | 2,000 RPM | Not documented for Agent |
### Integrations and ecosystem
Exa has the broader framework footprint: prebuilt wrappers that trade tuning for zero setup, the path of least effort if you don’t want to write anything. Parallel leans on OpenAI-compatible interfaces, a keyless MCP server, and warehouse connectors.
| Feature | Parallel | Exa |
|---|---|---|
| SDKs | Python and TypeScript | Python and TypeScript |
| CLI | Leads.Search, extraction, research, enrichment, and monitoring from the terminal (Leads) | Not documented |
| Framework coverage | Vercel AI SDK, n8n, LangChain, LlamaIndex, and more | Leads.LangChain, LlamaIndex, CrewAI, Vercel AI SDK, 25+ more (Leads) |
| MCP | Leads.Free keyless hosted server, plus Parallel Skills (Leads) | MCP and Websets MCP; Agent and Connect in MCP |
| OpenAI-compatible route | Synchronous Responses API with streaming and follow-ups | /responses maps to the asynchronous Agent API |
| Warehouse connectors | Snowflake, BigQuery, DuckDB | Exa Connect premium data partners |
| Agent-native payments | x402 | x402 |
### Security, compliance, and support
The two platforms line up closely here. Neither publishes SLA numbers outside an enterprise contract.
| Feature | Parallel | Exa |
|---|---|---|
| SOC 2 Type II | Yes | Yes |
| Zero data retention | Enterprise | Enterprise |
| HIPAA compliance | Yes | Yes |
| SSO and DPA | Yes | Yes |
| Published SLA numbers | Enterprise contract only | Enterprise contract only |
## Which one should you pick?
Both platforms are credible. Parallel is built for production agent workloads where accuracy, evidence, and unit cost decide. Exa is a good fit for independent apps and side projects.
### Pick Parallel when
- You want maximum accuracy at every price and latency tier.
- You want a single provider that covers basic web grounding, batch deep research, and web monitoring.
- You want the most intuitive experience for developers and engineers.
- You need verifiable source attribution on agentic outputs.
- You need consistent outputs at scale.
- You want more control over cost-to-research depth.
### Pick Exa when
- You’re building an independent app or side project where Exa’s broader framework coverage means less initial wiring.
- You want LLM summaries inline in search results and accept the extra cost per result.
- You want to crawl a site’s subpages through the API rather than query an index.
## Switching from Exa
Exa’s four core endpoints have direct Parallel counterparts. Answer and Websets land close by. Port the four first, then validate the results on your own query mix.
- 01
Map the calls you already make
Exa Search becomes Parallel Search, Contents becomes Extract, Agent becomes Task, and Monitors becomes Monitor. Add your objective alongside the keyword query to give ranking the task context it was missing.
- 02
Keep your existing client
Python and TypeScript SDKs, a free keyless MCP server, and an OpenAI Responses-compatible endpoint mean the switch is usually a client and parameter change rather than a rewrite.
- 03
Validate before you commit
New accounts earn up to $80 at signup plus $5 in credits every month, which covers a real evaluation on your own query mix before anything moves to production.
FAQ
Is Parallel better than Exa?
For scaled, enterprise-grade products that depend on web search and deep research, yes: Parallel is more accurate, faster, more cost-effective, and more intuitive to build with. Exa remains a solid pick for independent apps and side projects.
Parallel vs. Exa: which is cheaper?
Parallel, at published list prices. Search is $1 per 1,000 requests against Exa’s $7, monitoring is $3–$10 against $15, and a 100-result entity query is $5 against roughly $97. Deep research tiers overlap, so price the specific tier your workload needs.
Can I migrate from Exa to Parallel?
Yes. Search maps to Search, Contents to Extract, Agent to Task, and Monitors to Monitor, with Python and TypeScript SDKs and an OpenAI Responses-compatible endpoint on the Parallel side. Answer and Websets map approximately, to Responses and FindAll, so budget validation time there.
Does Parallel have citations that Exa lacks?
On agentic runs, yes. Task, FindAll, and Monitor return a Research Basis per field: source URL, excerpt, reasoning, and calibrated confidence. Search and Extract return source URLs without Basis. Exa Agent emits grounding when a run produces it, covering the answer rather than guaranteeing provenance per value.
Is Parallel a good Exa alternative for agentic research?
Yes. Task Specs fix the input and output contract, Task Groups fan thousands of runs out across a dataset, and eight fixed-price processors let you match compute to query difficulty. Parallel accepts 2,000 task requests per minute by default.
Where does Exa still win?
Exa suits independent apps and side projects: its framework ecosystem spans LangChain, LlamaIndex, CrewAI, and 25+ others, search can add per-result AI summaries at extra cost, and subpage crawling is documented. Not latency, and not the index: Exa advertises sub-200ms but measured 335–361ms in our run.
How current is this comparison?
Parallel reviewed both providers’ live documentation and published price lists on July 27, 2026, and last updated this page on August 8, 2026. Benchmark figures come from Parallel-run evaluations with the methodology linked. Both platforms ship often, so check the linked sources before you buy.
FAQ
For scaled, enterprise-grade products that depend on web search and deep research, yes: Parallel is more accurate, faster, more cost-effective, and more intuitive to build with. Exa remains a solid pick for independent apps and side projects.
Parallel, at published list prices. Search is $1 per 1,000 requests against Exa’s $7, monitoring is $3–$10 against $15, and a 100-result entity query is $5 against roughly $97. Deep research tiers overlap, so price the specific tier your workload needs.
Yes. Search maps to Search, Contents to Extract, Agent to Task, and Monitors to Monitor, with Python and TypeScript SDKs and an OpenAI Responses-compatible endpoint on the Parallel side. Answer and Websets map approximately, to Responses and FindAll, so budget validation time there.
On agentic runs, yes. Task, FindAll, and Monitor return a Research Basis per field: source URL, excerpt, reasoning, and calibrated confidence. Search and Extract return source URLs without Basis. Exa Agent emits grounding when a run produces it, covering the answer rather than guaranteeing provenance per value.
Yes. Task Specs fix the input and output contract, Task Groups fan thousands of runs out across a dataset, and eight fixed-price processors let you match compute to query difficulty. Parallel accepts 2,000 task requests per minute by default.
Exa suits independent apps and side projects: its framework ecosystem spans LangChain, LlamaIndex, CrewAI, and 25+ others, search can add per-result AI summaries at extra cost, and subpage crawling is documented. Not latency, and not the index: Exa advertises sub-200ms but measured 335–361ms in our run.
Parallel reviewed both providers’ live documentation and published price lists on July 27, 2026, and last updated this page on August 8, 2026. Benchmark figures come from Parallel-run evaluations with the methodology linked. Both platforms ship often, so check the linked sources before you buy.
Trusted by
- Harvey
- Kepler
- Nooks
- Modal
- Starbridge
- Attio
- Profound
- Actively
