Parallel gives AI agents web research APIs, and you write the criteria. Ocean uses contextual intelligence to find your next deal, with the reason on every account.
Used by teams at
ParallelWeb research infrastructure for AI agents, by API. A fit for developers building agents that need live data.
Parallel sells web APIs that agents call directly: Search, Extract, Task, Responses, Monitor and FindAll. FindAll turns a plain-English query into a structured list of companies or people, checks each against your match conditions and returns citations and confidence.
A maintained market model: which companies fit, ranked from your deals, with the reason on each, without writing the criteria each run.
Ocean is contextual intelligence for go-to-market. It reads what every company actually does from its own website, learns what good looks like from your closed-won and closed-lost deals, and ranks your whole market against it, with the reason behind each score. The ranked accounts arrive in your CRM, your rep queue or your AI agents.
What buyers weigh, grouped by the job each part does. A light blue background marks the stronger side.
| Topic | Ocean | Parallel |
|---|---|---|
| Finding the right accounts | ||
| Learns your ICP from won and lost deals | Ocean is stronger here. Core of the product. Segments and models learned from your won and lost CRM deals. | You write the match conditions for each FindAll run. It doesn’t describe a model learned from your CRM deals. |
| Ranking the whole market | Ocean is stronger here. Keeps every company in the market ranked, with an exact count of the companies that match your filters. | Builds a set per query: candidates are generated from the web, evaluated, and returned as matched or unmatched. |
| The reason behind each account | Segment, score, tier and matched signals on every account and every export. | Citations, reasoning and a confidence level for each condition you set. |
| Lookalike relevance in a third-party test | Ocean is stronger here. 74.3% relevant in the top 10 results and 59.5% in the top 100, 4th of 7 on the top 100. | 69.2% relevant in the top 10 results and 56.9% in the top 100, in the same test. |
| Exact market count | Ocean is stronger here. Counts every company in your market that fits, exactly and by segment, before you buy a single contact. | Returns the records your query matches. We found no exact market count by segment documented. |
| Tiers and exclusions | Ocean is stronger here. Sorts accounts into tiers and excludes by keyword, so reps start with the best fits. | We found no account tiers or keyword exclusions documented, so you build them downstream. |
| Learns from open deals | Ocean is stronger here. Learns from won, lost and open deals, so the model keeps pace with your pipeline. | We found no model that learns from your won, lost and open deals documented. |
| Contacts and data | ||
| Contacts | Verified emails and direct phones from a waterfall across 16+ data sources. Charged only when a contact is found, and exports are free. | Finds people through FindAll and Entity Search, as web-sourced records rather than a verified contact waterfall. |
| Freshness | Up-to-date signals built in-house from each company’s own teams, leaders, hiring and website. | Reads the live web at query time, and a Monitor API watches for changes you define. |
| Buying committee | Ocean is stronger here. Maps the roles to reach at each ranked account. | Returns people by role and title. We found no buying-committee mapping documented. |
| Workflow and pricing | ||
| Who runs it | Ocean is stronger here. A revenue team, with no engineer to write or maintain it. | Developers and agents call the APIs, with SDKs, a CLI and an MCP server. |
| AI agents and MCP | MCP server, public API and a chat agent on the same data and lookalike engine. | An MCP server, plus Search, Extract, Task and Responses APIs built for agents. |
| Inbound scoring | Ocean is stronger here. Scores inbound leads and website visitors against the same learned fit. | We found no inbound lead or visitor scoring documented. |
| Pricing | Ocean is stronger here. Two ways to buy: a flat-rate plan with credits included, or a custom plan built around your deals and credit volume. | Pay as you go from $0.001 a request. FindAll is $0.03 to $1 per match, plus a fixed cost per run. |
Parallel publishes pay-as-you-go prices per request, with 5,000 free requests a month and a custom Enterprise plan.
FindAll is in public beta and capped at 25 runs an hour. Per-match prices rise sharply for rare matches, and someone still has to write and tune the criteria and score the results.
A flat-rate plan with credits included, or a custom plan built around your deals.
Contacts are charged only when one is found. Exports are free.
See Ocean pricingParallel is infrastructure to build with. Ocean is the market model you’d otherwise have to build.
Choose Parallel if Use both ifFive steps. Most teams keep Parallel for web research and use Ocean as the account model agents draw on.
Note which agents and workflows call FindAll, Search or Task, and what each run costs at your volume.
Keep the criteria and rubrics your team wrote, so you can compare Ocean’s ranking on the same accounts.
Connect HubSpot, Salesforce, Pipedrive or Attio. Ocean reads your closed-won and closed-lost deals to learn what a good account looks like.
Ocean proposes segments and scores, with the reason on each. Correct anything that doesn’t match how you sell.
Scores, segments and matched signals go back to your CRM, or into Apollo, Instantly or Lemlist sequences.
Expect your team to own it, because the tool supplies the pieces and not the finished model; budget the hours.
Expect results that shift between runs, because lists come from live search and prompts; run it twice and compare.
Expect a quote that grows with seats, credits and add-ons, because the entry price is the smallest line; price your real volume.
Expect rules, filters and signals you set, because a model of your own deals is not what it leads with; ask for a test on your accounts.
Expect roadmap and packaging answers to move, because ownership and plans changed recently; get commitments in writing.
Ocean is better because it tells it like it is: contextual intelligence to find your next deal, with the reason on every account.
Start using OceanThe right alternative depends on what you use Parallel for. Here’s what each is built for and where Ocean starts.
Exa
Clay
Landbase
DiscoLike
DataMoonShort answers on what Parallel costs, what FindAll does, how Ocean compares and how they work together.
Parallel is pay as you go, with 5,000 free requests a month. Search is $0.001 to $0.005 a request, Extract $0.001, and the Task API $0.005 to $2.40. FindAll costs $0.03 to $1 per match plus a fixed cost per run. Enterprise is custom.
An API that turns a plain-English query into a structured list of companies or people. It generates candidates from the web, checks each against your match conditions and returns citations, reasoning and confidence. It’s in public beta.
It doesn’t describe doing so. You write the match conditions for each run. Ocean learns the ICP from your won and lost deals and ranks the market with the reason on each account.
In the 2026 Q3 Openbenchmarks test, Parallel had 69.2% of the top 10 results and 56.9% of the top 100 judged relevant. Ocean had 74.3% and 59.5%. Other providers scored higher than both on the top 100.
Yes. Parallel offers an MCP server and Search, Extract, Task and Responses APIs for agents. Ocean has an MCP server and a public API on the same data and lookalike engine.
Its pricing page lists SOC 2, and Enterprise adds zero data retention, data protection agreements, single sign-on and custom rate limits. It’s a young infrastructure company, and FindAll is still in public beta with a 25 runs an hour limit.
Ocean reads what each company does from its own website, learns from your closed-won and closed-lost deals, and returns ranked matches with the reason on each one.
Building means writing and maintaining the criteria, scoring the results and keeping them current, and it needs an engineer. Ocean ships that as a maintained model learned from your deals, and your agents can call it through the API and MCP.
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