Speech-To-Text
AssemblyAI has become a widely-used speech AI platform, offering developers a comprehensive suite of tools for transcribing audio and extracting valuable insights. Its combination of accurate transcription, Audio Intelligence features like sentiment analysis and summarization, and the innovative LeMUR framework for applying large language models to voice data has made it a popular choice for companies building voice-enabled applications.
Speech-To-Text
When choosing a text-to-speech API (TTS), developers face crucial practical questions: Which provider delivers the right balance of latency, voice quality, control, and scalability in real production systems?
Speech-To-Text
Automatic speech recognition (ASR), aka speech-to-text (STT) technology, is a constantly evolving field. Knowing which ASR model is right for your product or service can be challenging. CTC, encoder-decoder, transducer, and speech LLMs—each with distinct tradeoffs. What does it all mean? And what do you choose?!
AssemblyAI has become a widely-used speech AI platform, offering developers a comprehensive suite of tools for transcribing audio and extracting valuable insights. Its combination of accurate transcription, Audio Intelligence features like sentiment analysis and summarization, and the innovative LeMUR framework for applying large language models to voice data has made it a popular choice for companies building voice-enabled applications.
But as your audio processing requirements become more specific, you might find yourself needing capabilities that extend beyond what a generalist platform can provide. Perhaps you need to transcribe conversations where speakers switch between languages mid-sentence. Maybe compliance requirements demand that audio never leaves your infrastructure. Or you might need human-verified accuracy for legal depositions where AI errors carry real consequences.
That's where this guide comes in. We'll explore dedicated AssemblyAI alternatives that excel where you need them most, whether you're looking to:
This isn't about finding a "better" platform than AssemblyAI. It's about finding the right fit for your specific requirements. Some readers might use these tools alongside AssemblyAI for specialized use cases, while others might replace it entirely. Let's dive into the alternatives.
AssemblyAI is a speech AI platform that provides developers with advanced AI models for transcribing and understanding human speech through a REST API. Founded in 2017 by Dylan Fox, the platform has grown to serve startups and Fortune 500 companies alike, processing millions of API calls daily.
Its key features include:
AssemblyAI's strength lies in its comprehensive approach to audio intelligence.
When you submit audio through their API, you get not just a transcript but a rich set of insights: who said what, how they felt when saying it, what topics they discussed, and automated summaries that capture key points. The LeMUR framework takes this further by enabling developers to ask custom questions of transcripts or extract specific information using the power of LLMs.
The platform is designed with developers in mind, offering well-documented APIs and SDKs for Python and JavaScript (note: the Java SDK was archived in 2025 and is no longer maintained), and a generous free tier with $50 in credits. For enterprise customers, AssemblyAI provides SOC 2 Type 2 compliance, HIPAA support, and options for self-hosted deployments.
However, organizations with specific requirements may find that specialized alternatives better serve their needs.
Whether that's handling complex multilingual conversations, deploying entirely on-premises, achieving human-level accuracy, or processing audio on-device without any cloud transmission, the alternatives we'll explore each excel in their particular domain.
After testing AssemblyAI and researching the speech-to-text market, we identified specific scenarios where users often need more specialized capabilities than AssemblyAI's comprehensive approach provides. While AssemblyAI is excellent for many transcription and audio intelligence use cases, we focused on platforms that excel at particular jobs:
Each platform on this list excels in one of these specific areas. You might use them alongside AssemblyAI for specialized use cases, or choose one as a full replacement depending on your primary requirements.
❗DISCLAIMER: We aren't covering every single speech-to-text tool on the market! Our focus is on highlighting the best alternatives that address specific limitations or use cases where AssemblyAI may not be the optimal choice. The goal is to help you find tools that cater to your specific needs.
Gladia is an AI-powered audio intelligence platform built specifically for developers and enterprises who need to process multilingual audio at scale.
Founded in 2022 in Paris, the company was born from CEO Jean-Louis Queguiner's frustration with existing transcription services that couldn't accurately understand his French accent or handle conversations where speakers naturally switch between languages.
Its key features include:
For organizations processing international customer calls, multilingual meetings, or content from diverse global sources, Gladia provides capabilities that may offer advantages over AssemblyAI's multilingual handling, particularly around code-switching and accent diversity.
While AssemblyAI offers solid transcription across many languages, Gladia distinguishes itself in several key areas for teams processing multilingual audio.
One of Gladia's most significant advantages is its native support for code-switching, the natural phenomenon where speakers alternate between languages within the same conversation or even mid-sentence.
This is common in international business settings, multilingual households, customer support for global companies, and media content featuring diverse speakers.
When a speaker in a sales call naturally shifts from English to Spanish to explain a concept, or when a support agent switches to a customer's native language for clarity, Gladia's models detect and transcribe these transitions automatically.
AssemblyAI also supports code-switching and has dedicated documentation for the feature.
However, Gladia's specialized focus on multilingual handling, combined with their Solaria-1 model optimizations, may provide advantages for teams where multilingual conversations are the primary use case.
⚡ Gladia in Action: Consider a European contact center handling customer inquiries across multiple countries. A customer might start a conversation in French, switch to English when discussing technical specifications, and include German product names. Gladia's real-time transcription captures each language transition accurately, enabling the AI agent assist system to provide relevant prompts regardless of which language the conversation is currently in.
Gladia's proprietary Solaria-1 model addresses one of the most significant concerns with ASR systems: hallucinations, where the model generates text that wasn't actually spoken.
This can manifest as invented words, repeated phrases, or entirely fabricated sentences, particularly problematic for enterprise applications where transcript accuracy has business or compliance implications.
Solaria-1 is trained on diverse audio, including noisy environments and phone-quality recordings, specifically to reduce these failure modes with real-life, noisy audio. The model supports both real-time and asynchronous transcription modes, a dual-mode capability that is a key technical differentiator.
Source: Gladia
AssemblyAI's Universal model also claims industry-leading accuracy and has its own hallucination mitigation approaches, including a reported 30% reduction in hallucination rates compared to Whisper Large-v3
However, Gladia's specific focus on accuracy and their transparent reporting on hallucination reduction makes them a strong choice for teams who have experienced reliability issues with other solutions.
⚡ Gladia in Action: A compliance team at a financial services firm needs to transcribe recorded client calls for regulatory archival. Standard ASR implementations occasionally generated phantom financial figures or repeated phrases that didn't exist in the original audio. After switching to Gladia's Solaria-1, the team saw significantly fewer instances requiring manual correction, reducing their review workload and improving confidence in their compliance records.
Gladia takes an explicit stance on data privacy that differentiates it from many competitors. For Pro and Enterprise customers, Gladia does not use audio to retrain their models by default. Free-tier audio may be used for training purposes.
For enterprise customers, Gladia offers additional controls including zero-day data retention (audio is processed and immediately deleted), customizable retention policies (1 day, 1 week, or 1 month), and the ability to request complete removal of usage data. The platform is GDPR compliant, HIPAA compliant, and has achieved SOC 2 Type 2 certification.
AssemblyAI also offers strong security certifications and data controls. However, Gladia's explicit positioning around data privacy, including their public statements about not using customer data for model training without consent, may resonate with organizations where this is a primary concern.
⚡ Gladia in Action: A healthcare technology company building a patient intake system needs to transcribe sensitive medical conversations. Regulatory requirements and internal policy prohibit using any service that might incorporate patient audio into shared machine learning models. Gladia's explicit commitment to not training on customer data (for paid plans), combined with zero-day retention options, allows the company to proceed with confidence in their compliance posture.
For applications requiring immediate transcription, such as AI voice agents, live captioning, or real-time agent assist systems, latency is critical. Gladia delivers transcriptions with latency under 300 milliseconds, enabling natural conversational experiences where the AI can respond appropriately without awkward pauses.
The platform uses WebSocket connections for streaming audio and provides both partial and final transcripts, allowing applications to display in-progress text while waiting for higher-confidence final results. Voice Activity Detection helps identify speech versus silence for more accurate transcript boundaries.
AssemblyAI also offers real-time streaming with approximately 300ms median latency. Both platforms are competitive in this space, though Gladia's optimization specifically for real-time multilingual scenarios may provide advantages for applications serving international audiences.
Note that while AssemblyAI supports 99+ languages for pre-recorded audio, its multilingual streaming model currently supports six languages (English, Spanish, French, German, Italian, Portuguese).
🏅 NOTE: We also evaluated other multilingual-focused providers for this category. Gladia's specialized focus on code-switching, combined with their Solaria-1 model optimizations and explicit data privacy positioning, makes them a strong choice for teams where multilingual conversation handling is a primary requirement.
Gladia offers usage-based pricing with separate rates for real-time and asynchronous transcription. This distinction matters because real-time transcription (for voice agents, live captioning) and async transcription (for recorded calls, meeting notes) serve different use cases with different technical requirements. All features like speaker diarization, sentiment analysis, and translation are bundled into the base price with no hidden add-ons.
View the full Gladia pricing page for current rates.
Self-Serve (Pay-as-you-go)
Scaling
Enterprise
Source: Gladia
Choose Gladia if:
Ready to handle multilingual conversations with confidence? Try Gladia's API with 10 free hours of transcription each month, or contact their team to discuss enterprise requirements.
Deepgram is a foundational AI company specializing in voice technology that provides an enterprise-grade speech-to-text platform built on end-to-end deep learning.
Founded in 2015 by three former physicists who applied dark matter detection techniques to speech recognition, the company offers capabilities that address specific enterprise requirements: the ability to train custom models on proprietary data and deploy outside of cloud infrastructure.
Its key features include:
For organizations in regulated industries, those with unique vocabulary requirements, or teams building real-time voice agents, Deepgram provides architectural flexibility that some cloud-focused services may not offer.
Deepgram distinguishes itself from AssemblyAI through capabilities that address specific enterprise requirements around model customization and data sovereignty.
1. Custom Model Training for Domain-Specific Accuracy AssemblyAI offers "Word Boost" to improve recognition of specific terms, but this is fundamentally different from training entirely custom speech models.
Deepgram allows organizations to train on proprietary datasets, achieving accuracy levels for specialized terminology that generic models cannot match. Medical practices with complex pharmaceutical names, law firms with case-specific proper nouns, and financial services with unique product names can achieve higher accuracy through custom training rather than keyword boosting alone.
2. On-Premises and Self-Hosted Deployment While AssemblyAI now offers self-hosted deployment options for enterprises, Deepgram provides multiple well-documented deployment pathways: standard cloud API, on-premises self-hosted installations using Docker or Kubernetes, and "Deepgram Dedicated" single-tenant environments in preferred cloud regions.
For healthcare, finance, or government applications where audio cannot leave controlled infrastructure, this flexibility is valuable.
3. Unified Voice Agent API Building conversational AI with AssemblyAI requires integrating a separate TTS service, as AssemblyAI does not offer text-to-speech (though it does provide an LLM Gateway for transcript analysis).
Deepgram's Voice Agent API provides a single interface combining STT, TTS, and LLM orchestration with native handling of conversational dynamics like barge-in and end-of-thought detection. This reduces both development complexity and cumulative latency from orchestrating separate services.
🏅 NOTE: We also evaluated Speechmatics and Google Cloud Speech-to-Text for this category. While Speechmatics excels at deployment flexibility and Google Cloud offers deep GCP integration, Deepgram provides a strong combination of custom model training, deployment options, and unified voice agent infrastructure for enterprises building production-scale conversational AI.
Deepgram uses usage-based pricing with credits purchased upfront and deducted per second of audio processed. View the full Deepgram pricing page for current rates.
Pay-As-You-Go
Growth Plan ($4,000+ annual commitment)
Enterprise
Source: Deepgram
Choose Deepgram if:
Speechmatics is a Cambridge-based speech recognition company that provides a unified automatic speech recognition (ASR) and text-to-speech (TTS) platform.
Unlike AssemblyAI, which focuses exclusively on speech-to-text and audio intelligence, Speechmatics offers both directions of voice technology in a single platform with deployment flexibility spanning cloud, on-premises, and hybrid options.
Its key features include:
For teams building voice agents, IVR systems, or any application requiring both understanding and generating speech, Speechmatics eliminates the need to integrate and manage separate vendors.
Speechmatics offers capabilities that address specific gaps in AssemblyAI's architecture.
1. Combined STT and TTS Eliminates Multi-Vendor Complexity AssemblyAI is exclusively a speech-to-text provider. Organizations building voice agents, IVR systems, or conversational AI must source text-to-speech from a separate vendor.
Speechmatics offers TTS with sub-150ms latency suitable for voice agent applications, allowing development teams to handle both speech recognition and synthesis through a single API, single vendor relationship, and single billing arrangement.
Source: Speechmatics
2. Deployment Flexibility Beyond Cloud While AssemblyAI now offers self-hosted deployment options for enterprises, Speechmatics provides a broader range of deployment pathways: cloud SaaS on Microsoft Azure with multi-region options, Virtual Appliances for on-premises environments, Docker containers for lightweight local processing, and on-device solutions for edge computing.
For healthcare organizations or financial institutions with data residency requirements, this means audio never needs to leave controlled infrastructure.
3. Accent-Agnostic Global Language Models Rather than requiring users to select specific dialect variants (American English, British English, etc.), Speechmatics trains comprehensive models on audio from 40+ countries.
Their Global English and Global Spanish packs reduce the need for accent-specific model selection (though you still specify the base language), particularly valuable for contact centers handling international customers.
🏅 NOTE: We also evaluated Google Cloud Speech-to-Text and AWS Transcribe. While Google Cloud offers deep GCP integration and AWS provides seamless Amazon service connectivity, Speechmatics offers a strong combination of TTS capabilities, deployment flexibility, and accent-agnostic models for enterprises needing a holistic voice AI platform.
Speechmatics offers tiered pricing with different rates for standard and enhanced accuracy models. View the full Speechmatics pricing page for current rates.
Free Tier
Pro Tier (Pay-as-you-go)
Enterprise
Source: Speechmatics
Choose Speechmatics if:
Rev is a hybrid human-AI transcription platform that provides an accessible alternative to AssemblyAI for teams who need high-accuracy transcripts without writing code or managing API integrations. Founded in 2010 with a network of over 70,000 human transcriptionists, Rev offers both AI-powered transcription for speed and human-verified transcription for mission-critical accuracy.
Its key features include:
For legal professionals, medical documentation teams, academic researchers, and content creators who need accurate transcripts without technical overhead, Rev provides a fundamentally different approach than API-first services.
Rev addresses needs that AssemblyAI's developer-focused model doesn't serve.
1. Human Verification for Mission-Critical Accuracy Even the best AI models produce errors, particularly with challenging audio, overlapping speakers, or specialized terminology. AssemblyAI users needing near-perfect accuracy must manually review and correct transcripts themselves.
Rev solves this directly with human-verified transcription carrying a 99% accuracy guarantee. For legal depositions, medical dictations, or academic research where errors carry professional consequences, human review provides quality assurance.
2. Zero Technical Barrier to Entry AssemblyAI is explicitly designed as a "developer-first" platform with REST APIs, SDKs, and technical documentation. Rev's web interface provides drag-and-drop file upload, clear service selection, and completed transcripts delivered to an inbox.
A marketing manager transcribing interviews, a journalist processing sources, or a small business owner documenting meetings doesn't need to write code.
3. Simpler Pricing Structure AssemblyAI charges separately for different capabilities: $0.15/hour base plus additional fees for speaker diarization, sentiment analysis, summarization, and other features. Rev's pricing is simpler: flat per-minute rates for core transcription, with optional add-ons (rush delivery, verbatim, timestamps) available at extra cost if needed.
🏅 NOTE: We also evaluated Otter.ai and Descript. While Otter.ai excels at real-time meeting transcription with live collaboration, and Descript is a powerful audio/video editing suite, Rev offers the most seamless path to human-verified accuracy for teams needing professional-grade transcripts without technical expertise.
View the full Rev pricing and subscription options for current rates.
Pay-Per-Minute (Human Services)
Subscription Plans (AI Services)
Source: Rev
Choose Rev if:
Source: GitHub
OpenAI Whisper is an MIT-licensed open-source automatic speech recognition system that provides a compelling alternative for teams prioritizing cost elimination and complete data control over managed convenience.
Released in September 2022 and trained on 680,000 hours of multilingual data, Whisper offers strong robustness across diverse audio conditions without any recurring API fees when self-hosted.
Its key features include:
For early-stage startups, proof-of-concept projects, or organizations with strict data sovereignty requirements, Whisper eliminates recurring API costs while providing production-capable transcription.
Whisper addresses needs that managed API services may not serve as well.
1. Zero Recurring API Costs AssemblyAI charges $0.15/hour for their Universal model plus additional fees for features. Whisper eliminates these recurring charges entirely when self-hosted. Organizations processing substantial audio volumes can see significant savings, though this requires investment in computer infrastructure.
For early-stage startups and proof-of-concept projects where unpredictable API bills strain limited budgets, Whisper provides cost predictability.
2. Complete Data Sovereignty Through Self-Hosting While AssemblyAI now offers self-hosted deployment options for enterprises, many organizations still use their cloud API, which requires sending audio to AssemblyAI's servers. Some organizations have regulatory requirements or policies prohibiting any third-party audio processing.
Whisper runs entirely on-premises or within your own cloud environment, keeping audio within controlled infrastructure. For healthcare, finance, government, or any scenario where data residency is non-negotiable, this capability is valuable.
3. Open-Source Flexibility for Customization AssemblyAI's models are proprietary and accessible only through their API or enterprise self-hosted deployments. Whisper's open-source nature enables fine-tuning on custom datasets for domain-specific terminology, experimentation with model modifications, and deep integration into custom pipelines.
Teams with ML engineering expertise can build differentiated products where ASR is a core component rather than a commodity service.
🏅 NOTE: We also evaluated Mozilla DeepSpeech and Vosk. While DeepSpeech was an early pioneer and Vosk offers lightweight offline recognition, OpenAI Whisper provides the best combination of multilingual accuracy, active development, community ecosystem, and permissive MIT licensing.
Self-Hosted (Primary Use Case)
OpenAI API (Alternative)
Source: OpenAI
Choose OpenAI Whisper if:
Picovoice is an end-to-end on-device voice AI platform that provides a complete alternative to cloud-based services for organizations requiring strict privacy, offline operation, or deterministic latency.
Unlike AssemblyAI, which processes audio through cloud servers, Picovoice is primarily designed to run on local hardware (though cloud deployment options are also available), ensuring audio data never leaves the device when used in on-device mode.
Its key features include:
For organizations building voice-enabled products in regulated industries, embedded systems manufacturers, or any application where data cannot leave the device, Picovoice provides capabilities cloud services architecturally cannot offer.
Picovoice addresses requirements that cloud-based services fundamentally cannot serve.
Picovoice eliminates this by design: audio never leaves the local device. For healthcare, government, defense, or highly sensitive communications, this architectural approach makes compliance significantly simpler since there's no data transmission to audit or third-party processing agreements required.
Picovoice's edge-first architecture eliminates network latency entirely, providing consistent response times determined only by local hardware capabilities. For voice-controlled industrial equipment, in-vehicle systems, or interactive voice applications where timing is critical, this determinism is essential.
🏅 NOTE: We evaluated other edge-focused solutions, and Picovoice offers a comprehensive end-to-end platform providing wake words, speech-to-text, text-to-speech, speech-to-intent, and local LLM inference across embedded, mobile, and web platforms in a single integrated ecosystem.
View the full Picovoice pricing page for current rates.
Free Plan
Foundation Plan (Startup Special)
Enterprise
Source: Picovoice
Choose Picovoice if:
Soniox is a real-time speech AI platform built around a single universal model that handles transcription, translation, and speaker diarization across 60+ languages simultaneously.
Its architecture delivers transcribed words one token at a time with very low latency (targeting sub-200ms), creating near-instantaneous feedback that feels synchronized with natural speech, all at pricing that significantly undercuts most competitors.
Its key features include:
For developers building latency-sensitive voice applications where response time meaningfully impacts user experience, and where cost efficiency at scale is critical, Soniox provides a compelling combination.
Soniox differentiates itself through architectural choices optimized for speed and cost.
1. Token-Level Streaming Optimized for Instantaneous Feedback AssemblyAI's real-time transcription delivers transcripts with approximately 300ms median word emission time. Soniox's architecture streams text token-by-token targeting sub-200ms latency, providing both "provisional" and "final" tokens.
Provisional tokens appear almost immediately, then get confirmed as final once the model has additional context. For voice agent applications where the AI needs to detect when users finish speaking and respond naturally, this faster feedback creates more responsive experiences.
2. Lower Pricing with Features Bundled AssemblyAI's Universal model costs $0.15/hour, with additional charges for speaker diarization ($0.02/hr), translation ($0.06/hr), and other features. A transcription with multiple add-ons could cost $0.23/hour or more.
Soniox charges approximately $0.10/hour for async and $0.12/hour for real-time with transcription, translation, and diarization included. For high-volume applications, particularly those requiring translation, the savings are substantial.
3. True Unified Multilingual Model Soniox uses a single universal model that inherently handles all 60+ supported languages without switching between models. This becomes relevant for code-switching scenarios where speakers alternate languages mid-sentence. The model detects and transcribes these switches automatically without requiring language pre-selection.
🏅 NOTE: We also evaluated Deepgram and Rev AI for real-time capabilities. While Deepgram offers competitive speed and Rev AI provides human-in-the-loop options, Soniox delivers a compelling combination of pricing and token-level streaming latency for cost-conscious, latency-critical applications.
Soniox uses pay-as-you-go, token-based pricing.
Asynchronous Transcription
Real-Time Streaming
Source: Soniox
Choose Soniox if:
While AssemblyAI serves as a comprehensive speech AI platform with robust Audio Intelligence and LLM integration through LeMUR, organizations with specific requirements often need specialized solutions that go deeper in particular areas.
Based on our research, here are the best alternatives:
Remember, you don't have to choose between AssemblyAI and these alternatives exclusively.
Many organizations successfully use multiple speech AI providers for different use cases within their tech stack. Consider your specific requirements around language support, deployment constraints, accuracy needs, and cost structure when deciding which solution fits best.
Need to handle multilingual conversations where speakers switch languages naturally? Try Gladia with 10 free hours of transcription each month, or contact their team to discuss enterprise requirements for real-time voice applications at scale.
