Podchaser API vs Listen Notes API

Podchaser API and Listen Notes API are both podcast data APIs, but they’re built for different jobs. Listen Notes API is a podcast directory API — 3.77M podcasts and 189M episodes, REST-only, with 11 official SDKs and pricing that starts at $200/month for 5,000 requests. It works well for podcast apps, search features, and lightweight directory lookups. Podchaser API is a podcast intelligence API — 6M+ podcasts, 32M+ creator and guest credits, first-party audience demographics, sponsor and ad-spend data, ratings and reviews, transcripts, and both REST and GraphQL access. It’s built for ad-tech, brand intelligence, PR tooling, AI products, and any use case where the *depth* of metadata is the product — not just the directory. For most teams that need anything beyond basic search, Podchaser is the more direct fit.

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If you’re evaluating a podcast API in 2026, the choice usually comes down to Podchaser API or Listen Notes API. Both have been on the market for years, both publish public pricing, and both will return podcast and episode metadata in a usable format. Where they diverge — and where most teams make the wrong call — is in everything *beyond* the directory: credits, transcripts, sponsors, demographics, query architecture, and how the pricing scales.

This guide is a side-by-side comparison of both APIs in 2026, with current pricing, catalog sizes, and feature support — so you can pick the right one in one read.

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Podchaser API vs Listen Notes API: Feature Comparison

The two APIs are aimed at different sides of the same workflow. Listen Notes is a directory API with strong developer ergonomics. Podchaser is an intelligence API with the data depth required for ad-tech, PR, brand monitoring, and AI products that need more than RSS-level data.

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Database Coverage and Catalog Size

Podchaser API indexes 6M+ podcasts and 200M+ episodes. Listen Notes API indexes 3.77M podcasts and 189M episodes (per Listen Notes’ own pricing page, May 2026).

A ~60% catalog gap matters more than the raw number suggests. Podchaser combines multiple feed sources with an authenticated, crowdsourced editorial layer, so shows that have changed RSS hosts, dropped from Apple, or live outside the major directories are still indexed. Pure feed-aggregator APIs miss those shows entirely. For search, recommendations, charts, or any feature where completeness affects user experience, the gap compounds at scale.

*For background on how Podchaser handles listener-level data and audience depth, see: [How to See How Many Listeners a Podcast Has](/articles/how-many-listeners-a-podcast-has)*

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Data Depth: What You Actually Get Back

This is where the two APIs diverge most sharply. Both will return the basics — title, description, image, RSS, episode list, and language. Everything past that point is the difference.

Podchaser API exposes:

– **Creator and guest credits** — 32M+ structured credits across host, co-host, producer, and guest roles. This is the dataset behind “find every podcast a given guest has appeared on” — a query Listen Notes cannot answer.
– **Transcripts** — Native transcript access. Following the May 2026 expansion, Podchaser provides transcripts for 150K+ podcasts with a 5-year backfill. Listen Notes does not provide native transcripts; transcript access requires integrating a third-party partner.
– **Sponsor and ad-spend data** — Past sponsors for the top 5,000 podcasts, each brand’s sponsor history, and estimated ad spend. This is the data layer behind brand intelligence and ad-tech use cases.
– **Audience demographics and psychographics** — 20+ first-party datapoints per show: age, gender, location, ethnicity, income, occupation, brand affinity.
– **Ratings and reviews** — Native rating and review data per podcast and per episode, with full review text and reviewer history.
– **Brand safety scores** — Show-level flags for political content, profanity, adult, graphic, and drug content.
– **Charts** — Top podcasts by category, country, and across major directories including Apple Podcasts and Spotify.
– **Power Score™** — A proprietary popularity-and-influence metric combining 30+ signals.

Listen Notes returns what’s in the RSS feed plus its Listen Score ranking and the partner-driven AI extras (titles, descriptions, show notes). If your product can be built from that layer, Listen Notes will work fine. If you need credits, transcripts, sponsors, demographics, or charts, you’d be sourcing those elsewhere on top of Listen Notes — at which point you’re already partway to Podchaser.

*Also see the Podchaser API category announcements: [Transcripts API](/articles/announcements/transcripts-api) · [Sponsor & Ad Spend API](/articles/announcements/podcast-sponsor-api)*

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API Architecture: REST + GraphQL vs REST Only

Podchaser API supports both REST and GraphQL endpoints. Listen Notes API is REST only.

GraphQL matters when you’re querying related entities together — fetching a podcast plus its credits plus its recent episodes plus its sponsor history in one round-trip, returning only the fields you asked for. In REST, that’s four endpoints and a payload size you didn’t ask for. For high-volume, latency-sensitive products, this is a real cost line — both in compute and in API credits.

Podchaser also exposes an MCP server, which lets non-developers connect Claude, ChatGPT, and other LLM agents directly to podcast data without writing integration code. For teams building AI-first products on top of podcast metadata, this is a meaningful architectural advantage.

Listen Notes’ REST API ships with 11 official SDKs (Node, Python, Ruby, PHP, Swift, Go, Java, Kotlin, C#, Scala, Rust), which is a real developer experience win on day one. Podchaser’s REST and GraphQL endpoints work with any standard HTTP or GraphQL client (Apollo, urql, graphql-request, fetch) — most modern stacks already have one — but there isn’t a language-specific SDK with the Podchaser logo on it.

**Rule of thumb:** if your team strongly prefers official typed SDKs and you only need directory-level data, Listen Notes’ DX is friendlier on day one. If you need related-entity queries, deeper data, MCP access, or expect volume, Podchaser scales better past day one.

For full Podchaser API technical reference, see [api-docs.podchaser.com](https://api-docs.podchaser.com).

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Pricing: Podchaser API vs Listen Notes API

Listen Notes API pricing (2026)

Podchaser API pricing (2026)

Podchaser API uses a query-points model with tiered packaging:

– **Free** — 25,000 points/month. Includes directory, search, credits, ratings, reviews, lists, listen tracking, and Login with Podchaser.
– **Plus** — Adds similar-podcast relationship graphs, hi-res images, priority support, white labeling, scalable pricing.
– **Pro** — Adds audience demographics, sponsor data, ad-spend estimates, charts, and Podchaser’s full proprietary signals.

The free tier alone is **83× larger** than Listen Notes’ free tier (25,000 points vs 300 requests). For most teams in evaluation or prototyping mode, that’s the difference between “can validate the build” and “rate-limited on day one.”

For paid pricing on Plus and Pro, talk to a Podchaser API specialist — packaging is shaped to your projected volume and data scope rather than a flat-rate ladder.

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Reliability, Support, and SLAs of Podcast APIs

Both APIs offer customer support on paid plans. Listen Notes provides priority email support on Pro and phone support plus a 99.999% SLA target on Enterprise. Podchaser provides priority support on Plus and dedicated account teams plus custom SLAs on Pro and enterprise engagements.

If you’re building anything customer-facing where downtime affects revenue, get SLA terms in writing from either vendor before signing — published support pages don’t always reflect contractual commitments.

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Who Uses Each API

Listen Notes publicly lists customers including Deloitte, HubSpot, Audacy, Snipd, Headliner, Readwise, and Zencastr. The mix skews toward podcast apps, hosting platforms, and discovery tools that need directory-level data at scale.

Podchaser API powers ad-tech and brand intelligence platforms, B2B sales platforms identifying podcast advertisers (e.g., Winmo), PR and agency tooling, podcast apps that need rich metadata, AI products grounding LLM responses on podcast data, and research products that depend on credits, sponsors, or demographics. The customer profile skews toward teams where the depth of metadata is the product, not just the directory.

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Migrating from Listen Notes to Podchaser

If you’re already on Listen Notes and considering a switch, the lift is usually smaller than expected because both APIs return the standard podcast and episode entities. Migration typically involves:

1. **Mapping Listen Notes podcast IDs to Podchaser IDs** — usually via RSS URL or iTunes ID, both of which exist on both sides.
2. **Replacing REST calls with either Podchaser’s REST or GraphQL equivalents** — direct field-to-field mapping for the directory layer; query consolidation opportunities at the relationship layer.
3. **Adding queries for the new data layers** — credits, transcripts, sponsors, demographics, charts — none of which are available on Listen Notes today.

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Choosing the Right Podcast API for Your Use Case

| If you’re building… | Recommendation |
|———————-|—————-|
| A consumer podcast app (search + play) | Either works; Listen Notes is faster to ship if you only need directory data; Podchaser if you want ratings, reviews, and credits in-product |
| Podcast advertising or brand intelligence | **Podchaser** — sponsor data, demographics, ad spend not available on Listen Notes |
| PR / earned media / guest booking | **Podchaser** — credits and contact graph not available on Listen Notes |
| Content research, listicles, recommendations | **Podchaser** — editorial layer plus credits plus ratings |
| Transcript search or LLM grounding | **Podchaser** — native transcripts, 5-year backfill |
| AI agents and MCP-driven workflows | **Podchaser** — REST, GraphQL, and MCP server access |
| A simple podcast directory or metadata lookup | Either; Listen Notes if SDK-driven dev speed matters most |
| Anything that may scale past 5,000 requests/month | **Podchaser** — better free-tier headroom plus custom enterprise pricing |

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Key Takeaways

– **Directory API vs intelligence API** — Listen Notes is a podcast directory API with strong dev ergonomics; Podchaser is a podcast intelligence API with the credit, transcript, sponsor, demographic, and chart depth required for ad-tech, PR, and AI use cases.
– **Catalog gap compounds** — 6M+ vs 3.77M podcasts means the long-tail and previously-active shows that matter for niche search and recommendations are over-indexed on Podchaser and under-indexed on Listen Notes.
– **Credits and transcripts are unique to Podchaser** — 32M+ structured credits and 150K+ podcasts with native transcripts have no Listen Notes equivalent.
– **GraphQL + MCP define the developer tier** — REST works; GraphQL avoids round-trips; MCP makes the API usable directly by LLM agents.
– **Free tier headroom matters for evaluations** — 25,000 points/month vs 300 requests/month means a team can build a real prototype on Podchaser before deciding to pay; Listen Notes’ free tier suspends on overage.
– **Pricing aligns to use case** — Listen Notes’ $200/month minimum works for small directory use cases; Podchaser’s custom packaging works for teams whose data needs grow.

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*Podchaser API is the podcast data layer for ad-tech, PR tooling, brand intelligence, and AI products. Access 6M+ podcasts, 32M+ creator and guest credits, 150K+ transcripts, sponsor and ad-spend data, first-party audience demographics, and full REST + GraphQL + MCP coverage.*

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