AudD vs ACRCloud vs Gracenote: music recognition API comparison

AudD, ACRCloud, and Gracenote each take a different approach to fingerprinting, distribution, and developer experience. This comparison stays factual about what each does and where AudD fits, without inventing competitor specifics or pricing.

What music recognition APIs do

A music recognition API identifies a recording from audio — a file, a stream, or a snippet — by matching an acoustic fingerprint against an indexed database and returning metadata: artist, title, album, and links to streaming services. The quality of a match depends on database coverage, the fingerprinting algorithm, and how the audio is processed.

AudD: developer-first

AudD is built around fast integration and a small, predictable surface area.

Core strengths

AudD recognizes against a public database of about 160 million songs using neural-network audio fingerprinting. A match can return streaming-service links and other metadata on request. You can be running with a single SDK call or curl command.

AudD also supports a custom catalog: with special access you upload your own tracks and assign each an integer audio_id, which comes back when those tracks are later recognized — so you can match unreleased or proprietary audio, not just the public catalog.

For live audio, AudD supports 24/7 stream recognition for radio stations and broadcasts, delivering results over webhook callbacks or longpoll.

Technical implementation

AudD ships eleven official SDKs — Python, Node/TypeScript, Go, Rust, PHP, Swift, Kotlin, .NET, Java, C, and C++ — plus a plain HTTP API. It accepts audio as a file upload, a URL, or a microphone recording.

The API is organized as three surfaces: standard (api.audd.io/) for a short clip, sub-two-second response, returning the top match; enterprise (enterprise.audd.io/) for long audio and video, billed per 12 seconds, returning every match with timestamps; and streams for continuous live recognition.

Getting started

You can test recognition immediately with a token from dashboard.audd.io and a single request, without a sales call.

ACRCloud

ACRCloud offers audio recognition across several content types, including music identification, broadcast monitoring, and custom audio. It provides SDKs for multiple platforms and targets broadcast and streaming use cases alongside developer integrations.

We won’t quote ACRCloud’s database size or pricing here — those change and we don’t want to misstate them. Check ACRCloud’s own documentation for current figures.

Gracenote

Gracenote has a long history in music metadata and brings deep catalog and descriptive metadata (such as genre and mood information) to recognition. It is generally positioned toward enterprise deployments with a sales-led onboarding process.

As with ACRCloud, we don’t quote Gracenote’s database size or pricing; consult Gracenote directly for current details.

Feature comparison

Feature	AudD
Public database	~160 million songs
Recognition	Neural-network audio fingerprinting
Surfaces	Standard (top match), enterprise (every match + timestamps), streams (24/7)
Custom catalog	Yes — upload tracks, get back your `audio_id`
Official SDKs	11 (Python, Node/TS, Go, Rust, PHP, Swift, Kotlin, .NET, Java, C, C++)
Metadata on request	Apple Music, Spotify, Deezer, MusicBrainz, plus `song_link`
Self-serve	Yes — token from the dashboard, no sales call

We’ve left ACRCloud and Gracenote columns out rather than fill them with numbers we can’t verify. Compare against their published documentation for an accurate, current picture.

Integration

AudD emphasizes immediate integration. You can recognize a hosted file with one call:

curl https://api.audd.io/ \
  -F api_token=your-token \
  -F url=https://audd.tech/example.mp3 \
  -F return=apple_music,spotify

Or with an SDK:

from audd import AudD

audd = AudD(api_token="your-token")  # dashboard.audd.io
result = audd.recognize(
    "https://audd.tech/example.mp3",
    return_metadata=["apple_music", "spotify"],
)
print(result.artist, "-", result.title)

The same patterns hold across all eleven SDKs. ACRCloud and Gracenote each have their own SDKs and onboarding; integration effort depends on the features you enable, so evaluate against your own use case.

Database coverage

AudD recognizes against roughly 160 million songs in its public database and lets you extend coverage with a custom catalog for proprietary or unreleased audio. Database size alone doesn’t determine accuracy for a given use case — coverage of the specific catalog you care about matters more than a headline number.

Stream recognition

AudD supports 24/7 stream recognition for radio and live audio, returning each identified song over a webhook callback or longpoll. This suits airplay monitoring, “now playing” displays, and live copyright scanning. Both ACRCloud and Gracenote also offer stream/broadcast capabilities; the right fit depends on your reporting and integration requirements.

Use-case fit

Developer-led projects. If you want to ship quickly with self-serve onboarding and a clean SDK, AudD fits well — music apps, podcast tools, content analysis, and copyright scanners.

Broadcast and media operations. Teams with broadcast-monitoring, content-compliance, or multi-modal needs should evaluate all three against their specific reporting workflows.

Metadata-heavy applications. If you need deep descriptive metadata (mood, detailed genre taxonomies), compare what each provider returns against your product’s needs.

Developer experience

AudD provides documentation, code examples across its SDKs, and open-source community tools. The API surface is small and consistent, which keeps the learning curve short. Evaluate ACRCloud and Gracenote’s documentation and SDKs directly to compare developer experience for your stack.

Making your choice

Choose AudD when you want fast, self-serve integration, a large public catalog, the option to recognize your own audio via a custom catalog, and a small consistent API across eleven languages. Evaluate ACRCloud and Gracenote against your specific broadcast, compliance, or metadata-depth requirements, using their current published figures rather than third-party numbers.

For most developers and growing teams, AudD’s combination of a 160-million-song public database, three recognition surfaces, and self-serve onboarding is the fastest to evaluate and put into production: a dashboard token, one call, and you have results.

FAQ

What’s the main difference? AudD emphasizes self-serve developer integration with a small, consistent API. ACRCloud and Gracenote each have their own positioning across broadcast monitoring and enterprise metadata — compare them on their own published details.

Which offers the fastest integration? AudD lets you recognize a file with a single SDK call or curl command using a dashboard token, with no sales call required.

How large is AudD’s database? About 160 million songs in the public catalog, extendable with a custom catalog for your own tracks.

Can I test before committing? Yes — get a token at dashboard.audd.io and start sending requests.

Which works best for live stream monitoring? AudD supports 24/7 stream recognition with webhook callbacks or longpoll. The other providers also offer stream capabilities; pick based on your reporting needs.

Conclusion

AudD delivers self-serve onboarding, a 160-million-song public database, custom-catalog support, and a consistent API across eleven languages — a strong default for technical teams that want to ship quickly.

Start at dashboard.audd.io and read the reference at docs.audd.io.

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