8 Alternatives to Azure for Voice AI STT
Azure does speech-to-text just fine, but on Vapi, you have more than just one option for transcription: you can chop and change according to your preference or needs with the click of a button.
Choose from Gladia, AssemblyAI, Deepgram, Cartesia, Talkscriber, OpenAI, Speechmatics, or Elevenlabs as the transcriber element of your voice agent, and pair it with the voice provider and LLM of your choice.
Every provider works natively with Vapi. Just change one JSON parameter to test a different model. Vapi makes it easier for developers to benchmark performance, calculate real costs, and verify compliance requirements.
Paradox of choice? Well, here we break down each STT provider we offer, and why you might want to give each a go.
» New to Speech-to-text? Click here.
Gladia keeps end-to-end latency under 100 ms using WebSocket connections that stream audio and return transcripts almost instantly. Based in France, their async transcription capabilities make them perfect when every millisecond counts.
Their Whisper-Zero platform, an enterprise-tuned fork of OpenAI Whisper, handles 99 languages and switches between them mid-sentence. What sets Gladia apart is how they combines features that usually require multiple tools.
Their API handles speech-to-text and translation in one shot, though you might need extra integration for speaker identification, emotion detection, timestamps, and summarization.
Unlike Azure, Gladia sacrifices custom model training for raw speed and simplicity. They offer zero-retention processing to keep sensitive recordings off their servers, and straightforward pricing that makes budgeting simple.
Want to test Gladia in Vapi? Just add:
json
"stt": { "provider": "gladia" }
For support bots, voice chat, or any conversation where delays break the magic, Gladia's sub-100 ms processing keeps things feeling human. We've found this particularly useful for customer service applications where response time directly impacts user satisfaction.
» Read more about Gladia & Vapi.
Raw transcripts are just the beginning. When you need to extract meaning from audio, such as summaries, sentiment, topics, and compliance flags, you typically stitch multiple services together. AssemblyAI gives you the whole package in one API call.
Every file you send automatically returns with summaries, topics, sentiment analysis, PII redaction, and chapter markers. Azure can do similar things by chaining several Cognitive Services, but AssemblyAI wraps it all in one developer-friendly endpoint with clear pricing.
Their latest models hit 90–95 percent word-accuracy on open-domain English benchmarks, matching the best cloud services. No minimums or contracts means you can prototype in Vapi without budget approval.
json
"stt": { "provider": "assemblyai" }
Drop that into your Vapi agent and test real-time or batch transcription right away.
Their pricing scales with your needs. New accounts get $50 in credits to start. Pay-as-you-go rates run from $0.12 per audio hour for the Nano model to $0.37 per hour for the high-accuracy async model. Big customers get volume discounts and dedicated support.
Every tier uses identical REST endpoints, so you can scale without changing code. If you're building analytics like call scoring, content moderation, or meeting insights, AssemblyAI handles the heavy lifting server-side while Vapi manages the voice layer.
» Read more about AssemblyAI & Vapi.
Building voice products that need instant responsiveness? Deepgram shines here. Their Nebula models run on an end-to-end deep learning pipeline trained directly on raw audio, not phoneme intermediates like older systems.
Combined with GPU-optimized inference, this keeps latency well below what most conversational applications can tolerate. We've seen this make a significant difference in real-world applications.
Deepgram's customization options set it apart. You can fine-tune models for industry terms, brand names, or regional accents without rebuilding your stack. Your transcription engine adapts to your business instead of the other way around.
You'll really notice the difference in streaming. By processing audio in large, parallel chunks, Deepgram shortens the gap between what users say and how your agent responds. Call centers get faster sentiment scores. Voice bots avoid awkward pauses and hand off to language models more smoothly.
Vapi makes integration simple:
json
"stt": { "provider": "deepgram" }
Start with their free tier, then move to volume pricing in production. If you need sub-300-millisecond latency with specialized vocabulary, Deepgram beats Azure's more general approach.
» Read more about Deepgram & Vapi.
Your audio contains regulated data that can't leave your network. Cartesia keeps your entire speech recognition pipeline inside your infrastructure. It runs on-premises or at the network edge, so recorded calls, medical dictations, and confidential meetings never touch external servers.
Azure lets you pick a region, but Microsoft still owns the hardware. Cartesia puts everything in your rack, giving you complete control. They back this approach with SOC 2 credentials and full audit logs that plug into your existing security tools.
You control retention, access policies, and deletion schedules without complicated shared-responsibility models. This level of control is essential for many regulated industries.
Performance stays strong despite running locally. Cartesia delivers Time-to-First-Audio around 120 ms, fast enough for real-time agents and live captioning in secure environments. By eliminating internet hops, you also remove third-party risk surfaces that security teams hate dealing with.
Testing in Vapi takes one line:
json
"stt": { "provider": "cartesia" }
Pricing comes through custom enterprise agreements based on hardware and support needs. This works well for healthcare networks handling HIPAA data, financial institutions with PCI requirements, and government agencies that must keep citizen data inside their firewalls.
If you need strict data sovereignty without sacrificing speed, Cartesia gives you control that public clouds can't match. We've seen this particularly valuable in healthcare settings where compliance is non-negotiable.
Talkscriber keeps their capabilities private until you test them directly. Without public benchmarks or detailed docs, hands-on testing through Vapi is your best evaluation method.
Getting started is simple:
json
"stt": { "provider": "talkscriber" }
Once integrated, you can stream or batch-process audio and compare performance against other providers in the same workflow. This direct comparison helps establish baselines across multiple engines.
Talkscriber doesn't publish standard pricing, so you'll need a quote for anything beyond small tests. This works for companies wanting to evaluate lesser-known engines without heavy integration work.
» Read more about Talkscriber & Vapi.
When your audio jumps between languages mid-sentence or comes with street noise, most engines stumble. Whisper doesn't. The hosted API gives you the same multilingual model that sparked the open-source wave, supporting 50+ languages with automatic detection in a single stream.
Trained on diverse audio, it handles accents, crosstalk, and poor mic quality that break other systems. We've found this particularly useful for international customer service applications.
Compared to Azure, Whisper excels at language flexibility and extracting meaning from messy input. The downside is speed. Batch requests return quickly, but real-time streaming has higher latency. You'll notice the lag if you need sub-second responses.
Vapi integration takes one line:
json
"stt": { "provider": "openai", "model": "whisper-1" }
Pricing is simple at $0.006 per audio minute with no tiers or minimums. That predictability helps when processing archived calls or long videos. Whisper works best for high-accuracy transcription on mixed-language or prerecorded audio where latency matters less than quality.
If your agent needs to understand global accents or clean up noisy recordings, Whisper belongs in your toolkit. With Vapi, testing Whisper against other providers takes minutes, not days.
Accents break most recognition systems. A Scottish caller or Kenyan customer speaks, and suddenly your transcript looks like nonsense. Speechmatics built Any-Context for this exact problem, training on diverse accents and dialects.
The result stays readable when conversations mix regional English, Swahili phrases, or Portuguese words. For global businesses, this capability alone can dramatically improve customer experiences.
Privacy often matters as much as accuracy. Speechmatics deploys inside your private cloud or data center, keeping sensitive recordings off the public internet. While Azure defaults to cloud processing, Speechmatics gives compliance teams full control over where data lives.
Integration with Vapi takes one line:
json
"stt": { "provider": "speechmatics" }
Billing is simple. You pay for transcribed minutes, with volume discounts and SLAs for enterprise customers. If you serve callers from across the globe without separate language models and have strict rules about data location, Speechmatics fits the bill.
Most voice projects hit the same problem: you need both recognition and synthesis, but managing two APIs means double the auth, different data formats, and extra round trips that kill response times.
ElevenLabs gives you both sides in one place. Their text-to-speech creates human-like, emotionally expressive voices that respond to punctuation and context. Instead of robotic voices, you get speech that sounds recorded, not generated.
With 3,000+ shareable voices, instant voice cloning, and custom voice design across 32+ languages, you can match your brand without juggling multiple tools. This integration saves significant development time.
Their Scribe v1 model handles recognition with high accuracy, automatic language detection, and speaker identification for up to 32 voices. It even marks non-speech sounds like applause. Since both engines share authentication and data formats, you can move from recognition to synthesis in one round trip.
Azure offers more languages, but their neural voices sound mechanical next to ElevenLabs' natural delivery. Azure's Custom Neural Voice also requires lengthy approvals. ElevenLabs trades breadth for speed. Their 75 ms latency and rapid voice cloning matter when your agent needs real-time responses.
Pricing requires a subscription, and you'll need payment details to access the service.
Try it in Vapi with one line:
json
"stt": { "provider": "elevenlabs" }
It works best for content production, dubbing, and conversational agents that must both listen and speak convincingly. Through Vapi's platform, you can quickly test if ElevenLabs meets your specific requirements.
Each Azure alternative brings distinct advantages for different projects. Gladia handles 99 languages with minimal latency. AssemblyAI extracts deeper insights from voice data. Cartesia keeps everything secure behind your firewall.
Your choice depends on what matters most to you. Need instant voice response? Look at Gladia or Deepgram. Want sophisticated analysis? AssemblyAI delivers deeper insights. Work in regulated industries? Cartesia's privacy controls might be essential.
Every option works seamlessly with Vapi, letting you test different engines by changing a single line of code. This flexibility helps developers, AI specialists, and product managers find their perfect fit without complex integration work.
Start building a voice agent right now and discover which engine meets your specific needs. With Vapi's platform, you can compare multiple providers side by side to make data-driven decisions about your voice AI infrastructure.
