Best Lusha alternatives for B2B contact data.

Lusha is often evaluated for good reasons: it has a recognizable extension workflow, reveal contacts, company data, CRM handoff, API access, and MCP support. The reason to look for alternatives is not that Lusha is weak. It is that the buyer may need a different workflow or a different data boundary.

If the team mainly wants browser lookup inside LinkedIn or a CRM, Lusha may remain a strong fit. If the team wants account research, email-format pages, org-chart pages, verified exports, API access, and AI-agent controls, a focused data layer like Argorant becomes more relevant.

Use one controlled account sample across every provider: 25 companies, three buyer titles, two countries, and one export destination. Measure relevant contacts found, verified emails, phone fields, title freshness, duplicate records, export fields, API behavior, MCP behavior, extension speed, and credit burn.

Then compare cost per usable exported contact. Include email reveal, phone reveal, API request costs, company-data costs, signal costs, duplicate behavior, rollover, exports, CRM sync, and cleanup time. Lusha uses one balance across the platform, extension, AI Assistant, and API, but the unit differs by action: one email costs one credit and one phone costs five.

Lusha's public monthly prices are $49.90 for Starter, $69.90 for Pro, and $399.90 for Premium. Annual billing lowers the displayed monthly equivalents to $37.45, $48.95, and $259.95, with 4,800, 7,200, and 40,800 credits granted for the year. Free includes 40 credits per month, while Scale remains custom.

At full use, Starter works out to about 12.48 cents per credit month-to-month or 9.36 cents on annual billing. Pro is about 11.65 cents monthly or 8.16 cents annually; Premium is about 11.76 cents monthly or 7.65 cents annually. Those annual figures are monthly equivalents of an annual purchase, not cancellable monthly prices.

One verified email therefore uses one of those credits, while one phone uses five, before considering unused allowance or workflow-specific charges. Argorant's agent wallet is $0.035 per contact without a subscription, nominally below Lusha's self-serve per-credit rates. The units are not identical, so compare the same fields and count only usable output.

Lusha supports OAuth connections for Claude, ChatGPT, and Codex, while custom MCP clients can use an API key. MCP does not introduce a separate public wallet: agent actions use Lusha's normal API and credit mechanics.

The published pricing table says API and CSV result batches of one to 25 rows can cost one credit, while contact data points cost one credit for an email and five for a phone. Buyers should test the exact tool call rather than infer costs from the browser product.

Lusha's current documentation contains a point worth resolving before production: its API pricing material describes a one-credit minimum per request even when no result is returned, while MCP troubleshooting material says failed lookups are not charged. Because those statements can produce different budgets, run a no-result test and get the governing rule confirmed for your plan.

Choose Argorant when the team wants company research before signing in. Public company pages, email-format pages, org-chart pages, comparison pages, alternatives pages, and free tools can show useful aggregate coverage without publishing real contact details.

Argorant is also the stronger fit when AI-agent governance matters. Search and fetch can return masked previews, while real contact details, export lists, high-volume usage, and owner and admin controls stay behind account controls.

Lusha may still be better when the team wants an already-mature extension workflow. Its official extension materials describe prospecting on LinkedIn, Sales Navigator, CRMs, Gmail, and company websites, plus saving contacts and sending data into GTM systems.

Lusha may also fit teams that are comfortable with its credit model and want a documented MCP/API path today. The diligence is to confirm exact plan requirements, channel-specific credit rules, API-key limits, and what an AI client can reveal or spend.

A useful public research page does not need to expose real people. It can show aggregate coverage, sample contact examples, masked contact teasers, role coverage, department coverage, company facts, and clear sign-in prompts.

That is the safer Argorant path. Public pages can help buyers understand the account graph, while named people, full email addresses, direct phones, profile URLs, exports, and automated reveal stay private.