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Pricing sourced from Neeva · Last verified March 2026
Neeva differentiates on two axes: long-term memory and product intelligence. Most AI testing tools self-heal by retrying with new selectors on each run, but Neeva persistently remembers why a previous failure occurred and what fixed it, applying that knowledge to all future runs. It also goes beyond pass/fail reporting via AutoBoards that surface a Quality Score, Release Risk, and Coverage Delta — turning test runs into product-health signals. Compared to the other testing tools in our 870+ tool directory, Neeva targets teams that want QA to inform product decisions, not just gate deploys.
Self-healing with memory means that when Neeva encounters a failing step — for example, a click on "Submit Order" that no longer exists — it first checks its long-term memory for similar past failures. If the same button was previously renamed to "Place Order" in v2.3 (per the vendor's marketing example), Neeva applies that learned mapping automatically and updates its memory so future runs use the new label. This is fundamentally different from stateless selector-retry healing, because the system gets smarter with every failure rather than re-solving the same problem each run. The result is compounding test resilience over time. Note: these examples are sourced from the vendor's landing page and have not been independently verified.
Neeva lists SOC-2 compliance as an FAQ topic on its landing page, indicating they address this requirement for enterprise buyers, but full compliance documentation is not publicly published. Prospective customers should request the current SOC-2 report (Type I or Type II) and any related security questionnaires directly during the demo or procurement process. Given the enterprise positioning and fact that Neeva ingests test scenarios that may include sensitive flows, verifying compliance status before granting production access is recommended. Always confirm the latest attestation date with the Neeva sales team.
Credential handling is listed as an FAQ topic on the Neeva landing page but the public-facing answer is not exposed in the scraped content. Most enterprise-grade QA platforms support encrypted secret storage with role-based access, and Neeva's enterprise positioning suggests similar capabilities. Buyers evaluating Neeva for production workflows should ask specifically about encryption at rest, secret rotation, and whether credentials are stored in their environment or Neeva's. This is especially important for teams testing flows behind authentication walls or processing payment data.
Neeva references integration as an FAQ topic but does not publish a specific integration matrix on its public landing page. Based on its product model — which the vendor's demo shows auto-detecting new flows from pull requests and correlating regressions to specific PRs — Neeva likely integrates with source-control systems (GitHub and similar) for PR-driven test discovery. Prospective buyers should request an up-to-date list of supported CI/CD providers, issue trackers, and observability tools during the demo. The lack of a public integration list is a common pattern for enterprise-gated tools.
No. The original Neeva was an ad-free, AI-powered search engine founded by ex-Google executives, which shut down its consumer search product and was acquired by Snowflake in May 2023. The current neeva.ai domain hosts a separate AI-powered QA testing platform with no disclosed connection to the former search engine or Snowflake. Because the domain has changed hands, buyers should confirm the operating entity and corporate history directly with the current Neeva sales team during evaluation, and be aware that the URL may not persistently host this product.
Neeva does not publish pricing, but the competitive landscape provides useful benchmarks. Self-serve AI testing tools like Reflect start around $25–$49/month for small teams, and Testim (now part of Tricentis) offers tiers starting under $100/month. Mid-market platforms like Mabl typically range from $200–$500/month depending on test volume. Fully managed services like QA Wolf start around $3,000–$5,000/month. Given Neeva's enterprise-only positioning, demo-gated access, and feature set (AutoBoards, Digital Twin, memory engine), pricing likely falls in the $500–$2,000+/month range for mid-market buyers, with custom enterprise contracts for larger orgs. Prospective buyers should request a quote during the demo and ask about annual vs. monthly billing, per-seat vs. per-test pricing, and any available pilot or proof-of-concept programs.
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