ElevenLabs vs Microsoft Azure Text-to-Speech
Discover the differences between ElevenLabs and Microsoft Azure Text-to-Speech. Learn about features, pricing, and performance.
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Compare ElevenLabs and Microsoft Azure Text-to-Speech
Eleven Labs offers more natural and expressive voices with better emotional range, while Azure Text to Speech provides reliable, clear speech synthesis with consistent quality, making it suitable for enterprise applications.
Updated on:
Feb 20, 2025
Features
ElevenLabs
75 ms for the lower quality Flash Model, and 300ms+ for the full model
Natural and realistic, widely used by all types of content creators
Limited to 40k characters per request
Requires 10 seconds of audio
IPA support but isolated pronunciation
Stability, similarity, and style exaggeration controls
8kHz audio, telephony optimized voices
No on-device or on-prem support
32
Up to 15 on highest self serve tier, custom for enterprise
Microsoft Text-to-Speech
300ms – 800ms
More robotic voices
Limited character count for longer texts
Not supported
Requires a substantial amount of audio data
Less contextual awareness in pronunciation
Limited controls for stability and similarity
Standard audio quality without optimization
No on-device or on-prem support
140
Up to100
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Voice Quality Comparison
This evaluation focuses on the voice quality of ElevenLabs and Microsoft Azure Text-to-Speech.
ElevenLabs achieves a high speech naturalness score in 44.98% of cases, while Azure performs slightly better with a higher pronunciation accuracy of 84.72%.
Both models exhibit minimal background noise, ensuring clear audio output. This comparison provides valuable insights for users seeking high-quality voice synthesis solutions.
Latency Assessment
In this evaluation, we analyze the latency of ElevenLabs and Microsoft Azure Text-to-Speech using the Time to First Audio (TTFA) metric.
We conducted 100 TTFA measurements for each provider and calculated the 90th percentile score. ElevenLabs demonstrated a TTFA of 135ms, showcasing its capability for low-latency voice generation. Microsoft Azure, while competitive, had a slightly higher TTFA, indicating room for improvement in response times.
This assessment is crucial for applications requiring real-time voice synthesis, helping developers choose the right solution for their needs.
Hallucination Rate Analysis
This evaluation examines the hallucination rate of ElevenLabs and Microsoft Azure Text-to-Speech. Hallucination in TTS refers to the generation of incorrect or nonsensical outputs.
ElevenLabs boasts an impressive Word Error Rate (WER) of 2.83%, making it the most accurate model in the field. In contrast, Microsoft Azure's WER stands at 3.18%.
This analysis is essential for developers aiming to create reliable and accurate voice applications, ensuring that the chosen model minimizes errors and enhances user experience.
Voice Design Control
In this evaluation, we explore the voice design controllability of ElevenLabs and Microsoft Azure Text-to-Speech.
ElevenLabs provides users with extensive customization options, allowing for fine-tuning of voice parameters such as pitch, speed, and tone.
Microsoft Azure also offers customization features, but with slightly less granularity. This flexibility is crucial for developers looking to create unique voice experiences tailored to specific applications. By comparing these capabilities, we help users identify which model best suits their voice design needs.
Explore Pricing for ElevenLabs and Microsoft Azure Text-to-Speech
ElevenLabs
Free - $0 per month with 10k characters
Starter - $5 per month with 30k characters
Creator - $11 per month with 100k characters
Pro - $99 per month with 500k characters
Scale - $330 per month with 2M characters
Microsoft Text-to-Speech
Free - 0.5 million characters free per month
Pay as You Go - $15 to $24 per 1M characters to
Commitment Tiers - Starting from $960 for 80M characters
Commitment Tiers – Connected container - Started from $912 for 80M characters
Commitment Tiers – Disconnected container - Starting from $47,424 for 4.8B characters
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