{"id":257,"date":"2026-03-25T05:12:55","date_gmt":"2026-03-25T05:12:55","guid":{"rendered":"https:\/\/blog.listenlabs.ai\/best-ab-testing-tools-2026\/"},"modified":"2026-07-04T05:31:29","modified_gmt":"2026-07-04T05:31:29","slug":"best-ab-testing-tools-2026","status":"publish","type":"post","link":"https:\/\/listenlabs.ai\/articles\/best-ab-testing-tools-2026\/","title":{"rendered":"Best A\/B Testing Tools for Product Experiments in 2026"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs | Last updated: June 15, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for 2026 Experimentation Stacks<\/h2>\n<ul>\n<li>Warehouse-native platforms like GrowthBook and Eppo keep analysis inside existing data warehouses, eliminating duplication costs and maintaining PII governance.<\/li>\n<li>All-in-one SaaS tools reduce initial setup effort but create separate data pipelines and higher long-term costs for Series B+ teams.<\/li>\n<li>Open-source options provide maximum infrastructure control but require ongoing engineering maintenance for upgrades and flag lifecycle management.<\/li>\n<li>A\/B testing tools identify what changed in metrics but cannot explain why users behaved differently or surface the reasoning behind results.<\/li>\n<li>Listen Labs pairs with any A\/B testing platform to deliver verified participants, AI-moderated interviews, and consultant-quality analysis in under 24 hours, so you close the qualitative insight gap <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">in a live demo<\/a>.<\/li>\n<\/ul>\n<h2>Evaluation Criteria for 2026 Product Experiments<\/h2>\n<p>Eight criteria determine fit at Series B+ scale and work together as a single decision lens. Speed to first insight measures how quickly a team moves from hypothesis to actionable data, but speed only helps when the data has enough depth to guide decisions. Depth of qualitative signal determines whether the tool surfaces the reasoning behind metric changes or only the changes themselves, which shapes how confidently teams act on results. That confidence also depends on participant or user quality, including fraud prevention, panel diversity, and behavioral targeting that keeps samples close to your real market.<\/p>\n<p>Integration effort with warehouses or CDPs captures the real engineering cost of connecting the tool to Snowflake, BigQuery, Redshift, or Databricks. Developer workload covers SDK size, flag lifecycle management, and ongoing maintenance, which all affect how many experiments engineering can support. Mobile, web, and full-stack coverage shows whether the tool handles every surface a modern product team ships to instead of forcing separate systems. Pricing transparency at Series B+ scale matters because <a href=\"https:\/\/growthlist.co\/startup-funding-stages\" target=\"_blank\" rel=\"noindex nofollow\">procurement at this stage involves security reviews, RFPs, and sales cycles of three to six months<\/a>, so sticker price rarely matches total cost. Long-term operational burden includes flag sprawl, data duplication, and the analyst overhead required to keep experiments running cleanly over time.<\/p>\n<h2>Warehouse-Native Platforms for Data-Mature Teams<\/h2>\n<p>Warehouse-native platforms align best with teams that already treat the warehouse as the source of truth. <strong>GrowthBook<\/strong> queries data directly inside existing warehouses, including Snowflake, BigQuery, Redshift, and Databricks, instead of copying events into a proprietary system. <a href=\"https:\/\/growthbook.io\/insights\/best-ab-testing-tools-product-analytics\" target=\"_blank\" rel=\"noindex nofollow\">A true warehouse-native setup keeps underlying SQL visible, retains all data inside the customer&#039;s infrastructure, and performs statistical analysis against the existing warehouse as the primary layer.<\/a> PII stays inside the customer&#039;s own environment. GrowthBook&#039;s open-source MIT license supports both fully managed cloud and self-hosted deployments, including air-gapped environments. <a href=\"https:\/\/newreleases.io\/project\/github\/growthbook\/growthbook\/release\/v4.4.0\" target=\"_blank\" rel=\"noindex nofollow\">GrowthBook 4.4 adds feature flag ramp schedules for targeted time-based rollouts, an improved stale feature algorithm and UX, and a major REST API expansion with many new endpoints.<\/a> Developer workload is moderate, since teams must maintain warehouse metric definitions and manage the flag lifecycle, but avoid per-event vendor fees. Mobile, web, and full-stack coverage remains strong through SDKs and edge middleware.<\/p>\n<p>This pure warehouse-native approach sets the baseline for the category. <strong>Eppo<\/strong> takes the same architectural path but focuses on statistical rigor for data-mature teams. It <a href=\"https:\/\/guideflow.com\/blog\/ab-testing-tools\" target=\"_blank\" rel=\"noindex nofollow\">connects to Snowflake, BigQuery, Databricks, or Redshift, performs analysis in-place, and includes Sequential testing, CUPED variance reduction, and experiment diagnostics for sample ratio mismatches.<\/a> Eppo fits organizations that already maintain clean metric definitions in their warehouse and want advanced experimentation without rebuilding their data layer.<\/p>\n<p><strong>Statsig in warehouse mode<\/strong> extends the warehouse-native pattern with a different emphasis. It offers <a href=\"https:\/\/lokker.com\/compare\/ab-testing-tools\" target=\"_blank\" rel=\"noindex nofollow\">warehouse-native gates, experiments, and analytics with CUPED, sequential testing, and Pulse analytics<\/a>, yet <a href=\"https:\/\/growthbook.io\/insights\/best-ab-testing-tools-product-analytics\" target=\"_blank\" rel=\"noindex nofollow\">treats warehouse connectivity as an optional add-on deployment mode rather than the default core architecture.<\/a> Teams that want warehouse-native analysis must configure it explicitly, which suits companies easing into warehouse-native workflows. <a href=\"https:\/\/contentsquare.com\/guides\/ab-testing\/tools\" target=\"_blank\" rel=\"noindex nofollow\">Statsig processes over one trillion events daily and claims 99.99% uptime<\/a>, so it supports high-traffic production environments. <a href=\"https:\/\/www.statsig.com\/pricing\" target=\"_blank\" rel=\"noindex nofollow\">Statsig&#039;s free tier covers up to 2 million events per month<\/a>, which helps growth-stage teams scale without heavy upfront spend.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>See how Listen Labs integrates with your warehouse workflow<\/strong> to answer the qualitative questions your A\/B tests cannot, and request a walkthrough.<\/a><\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098461736-796a7724447a.png\" alt=\"Screenshot of researcher creating a study by simply typing &quot;I want to interview Gen Z on how they use ChatGPT&quot;\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Our AI helps you go from idea to implemented discussion guide in seconds.<\/em><\/figcaption><\/figure>\n<h2>All-in-One SaaS Solutions for Faster Initial Setup<\/h2>\n<p>All-in-one SaaS platforms trade warehouse alignment for speed of deployment and bundled features. <strong>Optimizely<\/strong> scales to support hundreds of experiments across multiple teams and targets large organizations with dedicated experimentation functions. <a href=\"https:\/\/growthbook.io\/insights\/best-ab-testing-tools-product-analytics\" target=\"_blank\" rel=\"noindex nofollow\">Setup is measured in weeks to months and often requires a dedicated team of developers, analysts, and specialists.<\/a> <a href=\"https:\/\/lokker.com\/compare\/ab-testing-tools\" target=\"_blank\" rel=\"noindex nofollow\">Its stats engine supports sequential and fixed-horizon methods, and warehouse integration relies on exports and partner CDP connectors rather than native in-warehouse querying.<\/a> Teams already invested in a warehouse face data duplication and parallel pipelines. Custom enterprise pricing for Optimizely typically starts around $50,000 per year, with most companies paying between $50,000 and $200,000 annually. <a href=\"https:\/\/growthbook.io\/insights\/best-ab-testing-tools-product-analytics\" target=\"_blank\" rel=\"noindex nofollow\">Expanding into additional experimentation, analytics, or personalization modules compounds costs as teams scale.<\/a><\/p>\n<p><strong>Amplitude Feature Experimentation<\/strong> integrates experimentation directly with Amplitude&#039;s behavioral analytics layer, which shortens the analysis loop for teams already on the platform. Warehouse connectivity follows the same export-and-sync pattern as other all-in-one tools, so teams on Snowflake or BigQuery still maintain a separate data pipeline. The advantage lies in faster insight for teams whose metrics already live in Amplitude. The trade-off is vendor lock-in as the experimentation program grows and more decisions depend on Amplitude&#039;s data model.<\/p>\n<p><strong>VWO<\/strong> focuses on a visual editor suited to marketing and landing page tests. <a href=\"https:\/\/growthbook.io\/insights\/best-ab-testing-tools-product-analytics\" target=\"_blank\" rel=\"noindex nofollow\">Full-stack, server-side, or mobile experimentation is significantly harder to operationalize on VWO<\/a>, so it rarely fits engineering-owned product experimentation at Series B+ scale. <a href=\"https:\/\/coursera.org\/articles\/ab-testing-tools\" target=\"_blank\" rel=\"noindex nofollow\">VWO does support mobile apps and server-side testing with generative AI optimization suggestions<\/a>, yet the platform&#039;s core strength remains client-side web testing. Warehouse integration stays non-native, which reinforces its role as a marketing experimentation tool rather than a warehouse-first solution.<\/p>\n<h2>Open-Source Options for Maximum Control<\/h2>\n<p>Open-source platforms suit teams that want full control and have the engineering capacity to support it. <strong>GrowthBook self-hosted<\/strong> gives teams complete infrastructure control, including air-gapped deployments for strict compliance environments. <a href=\"https:\/\/guideflow.com\/blog\/ab-testing-tools\" target=\"_blank\" rel=\"noindex nofollow\">It avoids data duplication by using trusted metrics already defined by the data team and eliminates the need to send event data to a separate testing system.<\/a> The trade-off is maintenance, since engineering bandwidth must cover upgrades, security patches, and flag lifecycle management. For teams with strong DevOps capacity and a mature warehouse, self-hosted GrowthBook often becomes the lowest total-cost option at scale.<\/p>\n<p><strong>PostHog<\/strong> is an open-source product analytics platform with built-in A\/B testing and feature flags that consolidates several tools into one stack. <a href=\"https:\/\/contentsquare.com\/guides\/ab-testing\/tools\" target=\"_blank\" rel=\"noindex nofollow\">PostHog supports self-hosting, event autocapture, and session recordings, making it suitable for developer-focused teams that want full data control without separate SaaS data pipelines.<\/a> However, <a href=\"https:\/\/growthbook.io\/insights\/best-ab-testing-tools-product-analytics\" target=\"_blank\" rel=\"noindex nofollow\">PostHog calculates experiment metrics inside its own infrastructure and is not warehouse-native, which can require teams already using Snowflake, BigQuery, or Redshift to duplicate event data.<\/a> PostHog fits early-stage teams consolidating analytics and experimentation in one self-hosted environment before a dedicated warehouse strategy exists.<\/p>\n<h2>Scenario-Based Best-Fit Guidance for Common Teams<\/h2>\n<p>Enterprise insights teams with mature Snowflake or BigQuery environments and dedicated data engineers should evaluate GrowthBook or Eppo first. The warehouse-native architecture eliminates duplication costs and keeps PII inside existing governance boundaries, which aligns with strict compliance needs. UX research leads at mid-to-large product companies who need faster feedback loops beyond what A\/B tests provide should layer Listen Labs on top of any of these platforms. A\/B tests identify what changed, while AI-moderated interviews at scale explain why it changed and what to build next.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098685817-eaceb6089d9a.png\" alt=\"Listen Labs finds participants and helps build screener questions\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs finds participants and helps build screener questions<\/em><\/figcaption><\/figure>\n<p>Product teams without dedicated researchers at Series B companies benefit most from all-in-one SaaS options like Statsig, which offer generous free tiers and lower initial integration effort. These teams can later migrate to warehouse-native platforms as data maturity grows and warehouse investments deepen. Agencies and consultancies running bespoke experiments for clients on tight timelines need tools with fast setup and minimal infrastructure dependency. PostHog or Statsig&#039;s cloud tier fits this profile, and Listen Labs covers the qualitative research layer that client deliverables require.<\/p>\n<h2>Operational Considerations for Long-Running Programs<\/h2>\n<p>Stakeholder alignment often becomes the most underestimated cost in experimentation programs. <a href=\"https:\/\/entlify.com\/blog\/b2b-marketing-tech-stack\" target=\"_blank\" rel=\"noindex nofollow\">One fully integrated platform outperforms five platforms with only partial syncs<\/a>, and this pattern applies directly to experimentation stacks. Change management for warehouse-native migrations requires data team buy-in before product teams can run experiments independently, which affects timelines and ownership. Compliance considerations differ by architecture, since <a href=\"https:\/\/growthbook.io\/insights\/best-ab-testing-tools-product-analytics\" target=\"_blank\" rel=\"noindex nofollow\">all-in-one SaaS platforms generally process event data on the vendor&#039;s servers<\/a>, creating GDPR and data residency obligations that warehouse-native tools avoid by design.<\/p>\n<p><a href=\"https:\/\/developer.harness.io\/release-notes\/feature-management-experimentation\" target=\"_blank\" rel=\"noindex nofollow\">Harness FME&#039;s Warehouse Native Experimentation, generally available as of April 2026, executes all experiment queries inside the customer&#039;s warehouse and only retrieves aggregated results<\/a>, which signals where the market is heading on data privacy. Repeatability across global programs requires consistent metric definitions, flag lifecycle governance, and cross-study knowledge management. Most A\/B testing tools do not provide these capabilities natively, so teams must design processes and supporting systems around the experimentation layer.<\/p>\n<h2>Risks and Limitations of Metrics-Only Experimentation<\/h2>\n<p>Shallow data from rigid methods creates the primary risk in metrics-only experimentation. A\/B tests confirm or deny a hypothesis but cannot surface the reasoning behind user behavior, which limits what teams learn from each release. <a href=\"https:\/\/plane.so\/blog\/what-is-a-feature-flag-definition-best-practices-and-use-cases\" target=\"_blank\" rel=\"noindex nofollow\">The most effective teams track both quantitative indicators and qualitative signals from early-access users to determine whether rollout expansion is safe<\/a>, so they rely on a separate research layer to complete the picture. This dual-signal workflow introduces hidden costs, since recruitment or integration complexity often inflates total cost beyond subscription fees.<\/p>\n<p><a href=\"https:\/\/growthlist.co\/startup-funding-stages\" target=\"_blank\" rel=\"noindex nofollow\">Series B to C procurement involves implementation, integration, support, RFPs, and security reviews<\/a> that subscription pricing does not reflect, which makes vendor comparisons harder. <a href=\"https:\/\/datadoghq.com\/knowledge-center\/feature-flags\" target=\"_blank\" rel=\"noindex nofollow\">Feature flag sprawl and technical debt, where unused or permanent flags accumulate and increase code complexity, is a scaling risk<\/a> that engineering teams must budget maintenance time to address. Faster tools do not automatically produce better experiments, since speed to assignment differs from speed to insight. Teams that chase launch velocity without qualitative validation often ship features that move metrics in the wrong direction.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>Learn how Listen Labs delivers the qualitative layer your experimentation program is missing<\/strong> in under 24 hours through a personalized demo.<\/a><\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098910279-d16bc544a32e.png\" alt=\"Listen Labs auto-generates research reports in under a minute\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs auto-generates research reports in under a minute<\/em><\/figcaption><\/figure>\n<h2>Criteria-Based Decision Framework for Tool Selection<\/h2>\n<p>This checklist turns the earlier criteria into a practical decision path. Teams that already have a production data warehouse with clean metric definitions should start with GrowthBook or Eppo, since both consume existing metrics directly. Organizations whose engineering teams lack bandwidth to maintain a self-hosted deployment should favor GrowthBook Cloud or Statsig, which reduce operational overhead. Companies with strict PII governance or data residency requirements should prioritize warehouse-native or self-hosted open-source options that avoid vendor data processing.<\/p>\n<p>Marketing teams that need visual editing for landing page tests without engineering involvement fit best with all-in-one SaaS tools. Product groups running mobile, web, and backend experiments simultaneously must verify full-stack SDK coverage before signing any contract. Experimentation programs that produce metric changes leadership cannot explain should add AI-moderated qualitative research at scale to close the insight gap. Teams that need results in hours rather than weeks can use Listen Labs, which compresses the full research cycle, including recruitment, moderation, analysis, and deliverables, to under 24 hours across 45+ countries and 100+ languages.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is the difference between warehouse-native and all-in-one A\/B testing platforms?<\/h3>\n<p>Warehouse-native platforms run statistical analysis directly inside your existing data infrastructure, such as Snowflake, BigQuery, Redshift, or Databricks, without copying event data to a vendor&#039;s servers. This approach eliminates duplicate storage costs, keeps PII inside your governance boundary, and lets you use metric definitions your data team already maintains. All-in-one SaaS platforms collect event data in their own infrastructure, which simplifies initial setup but creates a separate data pipeline alongside your warehouse. The right choice depends on your data maturity, compliance requirements, and whether your data team has defined clean metrics that an experimentation tool can consume directly.<\/p>\n<h3>How does Listen Labs complement A\/B testing tools rather than replace them?<\/h3>\n<p>A\/B testing tools measure whether a change moved a metric, but they do not explain why users behaved differently, what emotional or functional need drove the result, or what the winning variant should evolve into next. Listen Labs fills that gap by conducting hundreds or thousands of AI-moderated qualitative interviews simultaneously, drawing on a verified network of 30 million respondents across 45+ countries. The platform compresses the full research lifecycle, including study design, recruitment, moderation, analysis, and deliverables, into that same 24-hour window. Teams use Listen Labs to generate the hypotheses that feed their A\/B testing roadmap and to explain the results that come out of it, replacing the fragmented workflow of separate recruitment vendors, moderators, and analysis tools.<\/p>\n<h3>What should Series B+ teams prioritize when evaluating experimentation tool pricing?<\/h3>\n<p>Subscription price is a poor proxy for total cost at this stage. The sales and onboarding cycle mentioned earlier, often three to six months, includes not just contract negotiation but implementation work and integration engineering that subscription pricing does not reflect. All-in-one enterprise platforms like Optimizely typically start around $50,000 per year before module expansions. Open-source self-hosted options like GrowthBook eliminate licensing costs but require engineering maintenance. Warehouse-native cloud options like Eppo and Statsig price on usage rather than flat enterprise contracts, which suits teams scaling experimentation volume. Teams should also factor in the cost of data duplication, analyst overhead for maintaining metric definitions, and the engineering time required to manage flag lifecycle and technical debt when comparing options.<\/p>\n<h3>How do feature flags connect to metrics-driven experimentation in 2026?<\/h3>\n<p>Feature flags act as the delivery mechanism, while metrics form the evaluation layer. Modern workflows use staged rollouts, often 1%, 5%, 25%, then 100% of traffic, with each phase assessed against both technical metrics like error rates and latency and business metrics like conversion and retention before proceeding. Platforms like GrowthBook 4.4 now support automated, time-based rollouts that attach guardrail metrics to hold, advance, or roll back releases without manual intervention. The risk at scale is flag sprawl, where unused or permanent flags accumulate and increase code complexity. Teams that treat flag creation as a lifecycle, with defined ownership, expiry dates, and cleanup processes, maintain experimentation discipline as their programs grow.<\/p>\n<h3>What qualitative research capabilities does Listen Labs offer that A\/B testing tools do not?<\/h3>\n<p>Listen Labs conducts adaptive, AI-moderated video interviews at scale, with dynamic follow-up questions that probe deeper on interesting or short answers the same way a trained human interviewer would. Its Emotional Intelligence feature analyzes tone of voice, word choice, and facial micro-expressions to surface emotions that transcripts alone miss, built on Ekman&#039;s universal emotions framework and available across 50+ languages. The Research Agent generates consultant-quality slide decks, memos, highlight reels, and statistical charts from interview data in under a minute. Mission Control stores all findings in a cross-study knowledge base so teams can query past research in seconds rather than re-running studies on questions they have already answered. None of these capabilities exist in any A\/B testing platform.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773099063654-7132de546a42.png\" alt=\"Listen Labs&apos; Research Agent quickly generates consultant-quality PowerPoint slide decks\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs&#039; Research Agent quickly generates consultant-quality PowerPoint slide decks<\/em><\/figcaption><\/figure>\n<h2>Conclusion: Matching Tools to Your Stack and Insight Needs<\/h2>\n<p>The best A\/B testing tool for product experiments in 2026 fits your data stack, engineering bandwidth, and compliance requirements instead of simply offering the longest feature list. Warehouse-native platforms serve data-mature teams that want analysis to run where their metrics already live. All-in-one SaaS solutions reduce initial integration effort at the cost of a separate data pipeline. Open-source options offer maximum control for teams willing to own the maintenance burden.<\/p>\n<p>No A\/B testing tool resolves the depth-versus-scale trade-off on its own, since each focuses on what changed rather than why it changed. Listen Labs replaces the slow, fragmented research workflow of separate recruitment vendors, human moderators, manual analysis, and delayed reports with a single end-to-end platform that closes the research cycle in under a day, from recruitment through final deliverables. Enterprises including Microsoft, Google, Anthropic, and Procter &amp; Gamble run their customer research on Listen Labs. <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>Explore a Listen Labs demo<\/strong> to see how it closes the insight gap your experimentation program leaves open.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Compare warehouse-native, all-in-one &amp; open-source A\/B testing tools. Listen Labs adds the qualitative &#8220;why&#8221; to your experiment results. Explore now.<\/p>\n","protected":false},"author":52,"featured_media":200,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-257","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/257","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/comments?post=257"}],"version-history":[{"count":5,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/257\/revisions"}],"predecessor-version":[{"id":1063,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/257\/revisions\/1063"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media\/200"}],"wp:attachment":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media?parent=257"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/categories?post=257"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/tags?post=257"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}