Top 7 Best Speech-to-Text API Providers in the US

Key criteria in this comparison: We ranked these platforms on real-time WebSocket latency, model-agnostic architecture, pricing transparency, TCPA compliance support, and documentation quality, since these are the factors that most affect a developer team’s build speed and total cost when deploying AI voice agents in the US.

Quick Answer: The best speech to text API USA provider in 2026 is FreJun Teler, which delivers sub-100ms latency via raw WebSocket streaming and works with any STT, LLM, or TTS provider the developer already uses. Teler starts at $15 per channel per month for UAE deployments and offers same-day integration with no proprietary lock-in, making it the top choice for US developers building real-time AI voice agents.

The best speech to text API USA option for developers building AI voice agents in 2026 is FreJun Teler, which combines sub-100ms WebSocket latency with a fully model-agnostic architecture that no general CPaaS platform can match for real-time AI voice workloads.

What is a speech to text API USA provider?

A speech to text API USA provider is a cloud service that converts spoken audio into machine-readable text via a programmable interface, enabling developers to add voice transcription, real-time conversation, and audio intelligence to their applications without building ASR (Automatic Speech Recognition) models from scratch. The best providers offer WebSocket streaming for live audio, REST endpoints for batch transcription, and SDKs across multiple languages so engineering teams can integrate quickly. Leading platforms go beyond raw transcription and expose speaker diarization, noise cancellation, language detection, and LLM-ready output formatting. US-based teams specifically need providers that comply with TCPA (Telephone Consumer Protection Act) requirements for automated calling, offer USD pricing with transparent per-minute billing, and deliver the sub-100ms latency that natural AI conversation demands.

As of 2026, the US speech recognition market is growing faster than any other region, driven by enterprise adoption of AI voice agents in contact centers, healthcare, and financial services. According to Grand View Research, the global market is projected to reach $26.8 billion by 2030 at a 14.6% CAGR. What separates the best speech to text API USA platforms from basic transcription tools is the combination of real-time streaming architecture, model-agnostic design, and carrier-grade telephony infrastructure that lets developers ship production AI voice agents without assembling five separate vendors.

Developers comparing a broader voice AI API USA shortlist will find the same latency and model-agnostic requirements apply whether the workload is transcription-first or full conversational voice AI. For teams building the reverse pipeline, choosing among the available text to speech API providers in India is just as important as picking the right speech-to-text layer for a complete voice AI stack.

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Which speech to text API USA providers rank highest in 2026?

Here is a quick comparison of the top seven speech to text API USA providers, ranked by real-time AI voice readiness, pricing transparency, and developer experience.

#	Provider	Best for	Starting price (USD)	Free trial
1	FreJun Teler	Real-time AI voice agents	$15/channel/month	Yes – free credits on signup
2	Deepgram	Real-time voice AI	Contact sales	Check website
3	AssemblyAI	Fast live transcription	$0.15/hour	Yes – free playground and API credits
4	Google Cloud Speech-to-Text	Enterprise GCP apps	Contact sales	Yes – try API without billing
5	OpenAI Whisper	Multilingual batch transcription	$0.006/minute	Check website
6	Amazon Transcribe	AWS call pipelines	Contact sales	Check website
7	Microsoft Azure Speech	Microsoft enterprise speech	Contact sales	Check website

Teler leads because it combines carrier-grade telephony with raw WebSocket audio streaming, giving developers the full voice transport layer they need without any proprietary lock-in. This is the same reason developers evaluating a voice API USA platform look for carrier-grade telephony bundled directly with the transcription layer rather than assembling both separately. Scroll for the full breakdown of each tool, covering features, pros, cons, pricing, and ideal use case.

How to choose the best speech to text API USA provider in 2026?

Choosing the right speech to text API USA provider depends on your AI architecture, your call volume, and the compliance requirements your product must meet. These five steps will help you make the right call before you write a single line of integration code.

Step 1: Define your AI voice architecture

Before evaluating any speech to text API USA provider, map out the full STT (Speech-to-Text, the component that converts the caller’s voice into text the LLM can process), LLM (Large Language Model, the AI brain that reads the text and decides what to respond), and TTS (Text-to-Speech, the component that converts the AI’s text response back into natural-sounding audio) stack you plan to use. If you are building a conversational AI agent, you need sub-100ms end-to-end latency, which means the latency (the delay between when a person speaks and when they hear a response) budget for the STT layer alone is roughly 50 to 80ms. If you are building a form-filling bot or async transcription pipeline, up to 500ms is acceptable. Knowing this upfront eliminates half the vendor list immediately.

Step 2: Choose your integration path

Two integration paths exist for real-time voice work. WebSocket streaming keeps a persistent, full-duplex connection open between the caller and your server, so audio flows continuously with no per-request overhead. Choosing the right telephony API USA provider alongside your speech-to-text stack determines whether you need to build call routing, SIP trunking, and carrier connectivity yourself or get it bundled in. This is the right path for AI voice agents that need sub-100ms response loops. SIP (Session Initiation Protocol, the standard protocol for establishing voice calls over the internet) trunking is better for connecting existing SIP-enabled platforms or enterprise telephony infrastructure to a carrier network. If you are building from scratch, start with WebSocket. If you are connecting an existing PBX or contact center platform, SIP trunking is the faster path. Teler supports both, so you are not forced to choose one architecture permanently.

Step 3: Check US carrier and compliance requirements

In the US, the FCC (Federal Communications Commission) and TCPA (Telephone Consumer Protection Act) govern automated calling. TCPA requires prior express written consent before placing automated or prerecorded calls to mobile numbers. For AI outbound calling specifically, this means your system must verify consent before dialing, maintain a DNC (Do Not Call) registry check, and log consent records. Violations carry fines of $500 to $1,500 per call. Any speech to text API USA provider you choose should either handle this compliance layer natively or integrate cleanly with a consent management system you already operate.

Step 4: Evaluate pricing in USD

Compare per-minute rates, channel costs, media streaming fees, and recording charges across providers. A provider charging $0.006 per minute for batch transcription looks cheap until you add WebSocket streaming fees, storage costs, and the engineering time required to build the telephony layer yourself. Calculate a realistic monthly cost based on your expected call volume. For a team running 10,000 minutes per month, the difference between $0.006 and $0.20 per minute is significant, but so is the difference between a provider that includes telephony infrastructure and one that requires you to build it separately.

Step 5: Test latency and audio quality on a real call

Benchmark numbers from a vendor’s marketing page rarely match production performance. Before committing to any speech to text API USA provider, run a real call through the full Media Streaming Loop (Voice input, STT transcription, LLM inference, TTS synthesis, Voice playback) and measure the round-trip time. Teler offers $5 in free calling credits on first upgrade for UAE deployments and 3,000 free minutes for India deployments, so you can test the full stack before paying. Most providers offer some form of free tier or playground, so there is no reason to commit without a real latency test first.

Top 7 speech to text API USA providers in 2026

1. FreJun Teler – Purpose-built voice API for AI agents

FreJun Teler is a Voice API platform built for developers who need to connect AI chatbots and LLMs to real phone calls, making it the most complete speech to text API USA solution for teams building production AI voice agents.

Best for: Real-time AI voice agent infrastructure

Key features

• Raw WebSocket streaming delivers L16/8000Hz audio directly to your server, so your STT engine receives the caller’s voice with no conversion layer and no added latency between the phone network and your AI stack.
• The full Media Streaming Loop (Voice input, STT transcription, LLM inference, TTS synthesis, Voice playback) completes in under 100ms, which is fast enough for natural, uninterrupted conversation without awkward pauses.
• Teler is fully model-agnostic, meaning developers bring their own STT engine (Google STT, Deepgram, Whisper, AssemblyAI), their own LLM (OpenAI GPT, Anthropic Claude, Google Gemini), and their own TTS (ElevenLabs, Azure TTS, Play.ht) and pay those providers directly.
• SIP trunking with TCP/TLS transport and both credential-based and IP-based authentication connects existing SIP-enabled platforms or enterprise telephony infrastructure directly to Teler’s carrier network.
• Webhook callbacks fire on every call state change (start, end, transfer, error), so your application logic stays in sync with the call without polling an API endpoint.
• Virtual numbers are available for India and UAE, with same-day provisioning via the platform dashboard at frejun.ai.
• SDKs across multiple languages plus REST API endpoints mean developers can get a working first call the same day using the getting-started guide at the FreJun knowledge base.

Pros and cons

Pros	Cons
Sub-100ms WebSocket latency is purpose-built for AI voice, not retrofitted onto a general CPaaS platform	Virtual numbers currently available for India and UAE only, not yet for US local numbers natively
Zero model lock-in: works with any STT, LLM, or TTS provider the team already uses	Elastic SIP trunking (auto-scaling without channel limits) is on the roadmap but not yet available
Same-day integration via WebSocket or SIP with no physical telecom infrastructure required	Minimum channel commitment of 10 channels per month applies to both Voice Apps and SIP trunking plans

Pricing

Pricing (USD): UAE plans start at $15/channel/month (minimum 10 channels), with outbound calling at $0.20/minute, inbound at $0.10/minute, media streaming at $0.0017/minute, and recording at $0.00046/minute. SIP trunking starts at $11.50/channel/month (minimum 10 channels, 6-month commitment) with outbound at $0.12/minute. India plans start at the equivalent of $15/channel/month billed in INR. First upgrade includes $5 in free calling credits (UAE) or 3,000 free minutes (India). Billing is prepaid on monthly, quarterly, half-yearly, or annual cycles. Teams building similar AI voice agents for the Indian market should evaluate a voice API provider in India with the same criteria: real-time latency, model-agnostic design, and carrier-grade compliance. See full pricing

Why Teler is the top recommendation

2. Deepgram – Low-latency WebSocket streaming for voice AI

Deepgram is a speech recognition and audio-intelligence API built for developers who need live transcription and call analysis, making it a strong speech to text API USA option for teams focused on real-time voice AI workflows.

Best for: Real-time voice AI transcription

Key features

• Native WebSocket streaming via the /v1/listen endpoint keeps the audio channel continuously open, so transcripts arrive with timestamps and speaker details in real time rather than after a file upload completes.
• The Flux model is built specifically for voice agents, with integrated end-of-turn detection so the AI knows when the caller has finished speaking without a separate silence-detection layer.
• Deployment options include cloud, on-premises, and private cloud, which matters for US enterprises with data residency requirements under HIPAA or financial services regulations.
• Official SDKs exist for Python, Node.js, Go, Java, C#, Ruby, and PHP, so most engineering teams can integrate without writing raw HTTP clients.
• Audio intelligence outputs beyond raw text (sentiment, topic detection, summarization) reduce the post-processing work required before feeding transcripts to an LLM.

Pros and cons

Pros	Cons
Native real-time WebSocket streaming is the primary integration path, well-suited for live voice-agent workflows	Provider-locked to Deepgram models; third-party STT providers are not supported
Developer comparisons cite less than 300ms lag and 10x real-time batch speed for high-throughput workloads	Pricing is not publicly listed, which makes cost estimation difficult before a sales conversation

Pricing

Pricing (USD): Publicly listed pricing was not found at time of writing. Contact Deepgram’s sales team for current per-minute rates and enterprise plan details. See full pricing

3. AssemblyAI – Feature-rich live transcription with voice agent modes

AssemblyAI is a developer transcription API that combines live transcription with post-processing intelligence, making it a competitive speech to text API USA choice for teams who want voice-agent-ready features without assembling separate services.

Best for: Fast live transcription with LLM features

Key features

• Universal-3.5 Pro Real-Time is the recommended model for voice agents, with three selectable modes (min-latency, balanced, max-accuracy) so developers can tune the latency versus accuracy trade-off for their specific use case.
• Context Carryover uses prior conversation turns to improve transcription accuracy on follow-up utterances, which reduces the need for manual prompt engineering between turns.
• Voice Focus noise cancellation processes audio before transcription, so calls from noisy environments (call centers, mobile phones) produce cleaner transcripts without a separate preprocessing step.
• Multilingual and code-switching support handles conversations where speakers mix languages, which reduces language-detection plumbing for US teams serving multilingual customer bases.
• A free playground and post-signup API credits let developers test the full transcription pipeline without entering billing details first.

Pros and cons

Pros	Cons
Free playground and post-signup API credits make testing frictionless before any billing commitment	Provider-locked to AssemblyAI models; no support for swapping in a third-party STT engine
Universal-Streaming at $0.15/hour is competitively priced against Deepgram for real-time workloads	A full end-to-end WebSocket audio loop example is not prominently surfaced in the public documentation

Pricing

Pricing (USD): Universal-Streaming starts at $0.15/hour, billed per hour of audio processed. Free API credits are available after signup. See full pricing

4. Google Cloud Speech-to-Text – Enterprise-grade ASR for GCP-native teams

Google Cloud Speech-to-Text is Google’s managed ASR service for developers building enterprise transcription, captions, and voice features on Google Cloud, making it a natural speech to text API USA fit for teams already running GCP infrastructure.

Best for: Enterprise GCP applications

Key features

• Synchronous, asynchronous, and streaming recognition modes give teams flexibility to handle both real-time calls and large batch transcription jobs from the same API surface.
• Support for 120+ languages, automatic punctuation, and speaker diarization (identifying which speaker said what) reduces post-processing work before feeding transcripts to downstream systems.
• Native integration with Google Cloud Storage means batch jobs can process audio files directly from a GCS bucket without a separate upload step.
• Service-account authentication fits naturally into existing GCP IAM policies, so security teams do not need to manage a separate credential system for the speech API.
• Noise-robust models handle audio from phone calls, meetings, and field recordings without requiring audio preprocessing.

Pros and cons

Pros	Cons
120+ languages, automatic punctuation, and speaker diarization in a single managed service	Requires Google Cloud project setup and internet connectivity, adding onboarding overhead for non-GCP teams
Clear Cloud Storage batch workflows and service-account auth fit enterprise GCP security requirements	No native WebSocket or SIP path documented, so voice-agent plumbing requires additional engineering

Pricing

Pricing (USD): Publicly listed per-minute pricing was not found at time of writing. Google Cloud offers a free tier for testing via the “Try this API” flow without linking billing. Contact Google Cloud sales for enterprise pricing. See full pricing

5. OpenAI Whisper – Open-source multilingual batch transcription

OpenAI Whisper is an automatic speech recognition model built for multilingual transcription, translation, and language detection, making it a popular speech to text API USA choice for teams that need strong accuracy on diverse audio without a real-time streaming requirement.

Best for: Multilingual batch transcription

Key features

• Multilingual transcription handles accents, background noise, and technical vocabulary across 99 languages, so teams serving diverse US customer bases do not need separate models per language.
• Speech translation converts audio from other languages directly into English text in a single API call, removing the need for a separate translation step in the pipeline.
• Automatic language detection identifies the spoken language without requiring the caller to select it, which simplifies IVR flows for multilingual applications.
• Prompt conditioning lets developers steer transcription output toward specific vocabulary, formatting, or style, which improves accuracy for domain-specific audio like medical or legal recordings.
• The open-source model is self-hostable under the MIT license, so teams with data residency requirements can run Whisper inside their own infrastructure rather than sending audio to an external API.

Pros and cons

Pros	Cons
Strong multilingual accuracy including accents, background noise, and technical language	Does not support real-time transcription out of the box; live use requires extra orchestration
Open-source and self-hostable, so teams can run it inside their own stack with no vendor dependency	The official API flow is multipart/form-data file upload only, not WebSocket streaming

Pricing

Pricing (USD): The OpenAI Whisper API is priced at approximately $0.006/minute, billed by the second. Self-hosted deployment via the open-source model is free but requires your own compute infrastructure. See full pricing

6. Amazon Transcribe – AWS-native transcription for call pipelines

Amazon Transcribe is AWS’s managed speech-to-text service for audio files and live audio streams, making it the natural speech to text API USA choice for teams already running contact center or data pipeline workloads inside AWS.

Best for: AWS-native call pipelines

Key features

• Both file transcription and live audio streaming are supported from the same service, so teams can handle real-time call transcription and batch post-call analysis without switching providers.
• Native integration with AWS IAM, S3, and CloudWatch means security, storage, and monitoring all fit into the same AWS account structure the team already manages.
• Broad language coverage and custom vocabulary support let teams improve accuracy for industry-specific terminology without fine-tuning a model from scratch.
• Speaker diarization identifies individual speakers in a call recording, which is useful for quality assurance and compliance review workflows.
• Official AWS SDKs exist for Python, Java, JavaScript/TypeScript, Go, .NET, Ruby, and PHP, covering most enterprise engineering stacks.

Pros and cons

Pros	Cons
Fits naturally into AWS storage and IAM workflows, reducing integration glue for AWS-native teams	Cloud transcription workflows often require uploading audio, creating jobs, and retrieving results from separate endpoints
Supports both file transcription and live audio streams from a single managed service	No native SIP-trunking or call-control layer documented, so telephony infrastructure must be built separately

Pricing

Pricing (USD): Publicly listed per-minute pricing was not found at time of writing. AWS offers a free tier for new accounts. Contact AWS sales for enterprise pricing details. See full pricing

7. Microsoft Azure Speech – Enterprise speech for Microsoft-stack teams

Microsoft Azure Speech is Microsoft’s managed speech platform for transcription and translation, making it the expected speech to text API USA choice for enterprises already running Azure infrastructure or Microsoft 365 workflows.

Best for: Microsoft enterprise speech workloads

Key features

• Real-time and batch transcription are both available from the same service, so teams can handle live call transcription and post-call analysis without switching providers.
• Audio translation converts speech from other languages into English text in a single pass, which simplifies multilingual contact center workflows.
• Mixed-language audio handling returns English output even when callers switch between languages mid-conversation, reducing language-detection engineering overhead.
• Prompt-guided transcription lets developers steer output toward specific vocabulary or formatting, improving accuracy for domain-specific audio.
• The OpenAI Whisper model is available through Azure’s tooling, so teams can access Whisper’s multilingual accuracy within the Azure security and compliance boundary they already operate in.

Pros and cons

Pros	Cons
Covers real-time and batch transcription, translation, and mixed-language handling in one platform	No native SIP-trunking or call-control layer documented; telephony infrastructure must be built separately
Exposes the OpenAI Whisper model through Azure tooling, giving teams Whisper accuracy inside Azure’s compliance boundary	Documentation emphasizes REST and file transcription more than a low-level voice-agent streaming loop

Pricing

Pricing (USD): Publicly listed per-minute pricing was not found at time of writing. Microsoft Azure offers a free tier for testing. Contact Azure sales for enterprise pricing. See full pricing

Speech to text API USA feature comparison: 2026

This table compares all seven speech to text API USA providers on the six features that most separate them for real-time AI voice agent development.

Feature	Teler	Deepgram	AssemblyAI	Google Cloud STT	OpenAI Whisper	Amazon Transcribe	Azure Speech
Real-time WebSocket streaming	Yes	Yes	Yes	Partial – gRPC streaming only	No	Partial – streaming via SDK	Partial – SDK-based streaming
Model-agnostic architecture	Yes	No	No	No	Partial – self-hosted only	No	No
Built-in telephony and carrier layer	Yes	No	No	No	No	No	No
SIP trunking support	Yes	No	No	No	No	No	No
Sub-100ms end-to-end latency	Yes	Partial – under 300ms claimed	Partial – min-latency mode available	No	No	No	No
Self-hostable or on-premises option	No	Yes – on-premises and private cloud	No	No	Yes – open-source MIT license	No	No

Teler is the only provider on this list that combines real-time WebSocket streaming, a built-in telephony and carrier layer, SIP trunking, and a model-agnostic architecture in a single platform, which is why it ranks first for teams building production AI voice agents.

Which speech to text API USA provider is right for your team in 2026?

Different teams have different constraints. The right speech to text API USA provider depends on your existing infrastructure, your latency requirements, and whether you need telephony built in or just a transcription layer on top of a stack you already operate.

If you are building a real-time AI voice agent from scratch: Choose Teler. It is the only provider that gives you carrier-grade telephony, raw WebSocket audio streaming, and a model-agnostic architecture in one platform, so you do not need to assemble a separate telephony vendor, a separate STT provider, and a separate compliance layer.

If you are already running a GCP-native data pipeline and need batch transcription: Choose Google Cloud Speech-to-Text. The native GCS integration and service-account auth fit directly into your existing GCP security model without additional credential management.

If you need strong multilingual accuracy and your use case is batch or async transcription: Choose OpenAI Whisper. The self-hosted option under the MIT license is the right call for teams with data residency requirements, since audio never leaves your own infrastructure.

If you are an AWS-native team running a contact center or call analytics pipeline: Choose Amazon Transcribe. The IAM and S3 integration reduces the glue code required to connect transcription to your existing AWS data workflows.

If you want a feature-rich live transcription API with a generous free tier for testing: Choose AssemblyAI. The free playground, competitive $0.15/hour pricing, and voice-agent-specific modes make it a strong option for teams prototyping before committing to a production architecture.

Which tool wins overall in 2026?

If you want the best overall speech to text API USA provider for 2026 with AI voice agent infrastructure, USD billing, and TCPA compliance support: Choose Teler. No other provider on this list combines sub-100ms WebSocket latency, a built-in carrier layer, SIP trunking, and zero model lock-in in a single platform that a developer can integrate the same day.

Frequently asked questions about speech to text API USA providers in 2026

What is the best speech to text API USA provider in 2026?

The best speech to text API USA provider in 2026 is FreJun Teler. It delivers sub-100ms latency via raw WebSocket streaming, which is fast enough for natural, uninterrupted AI conversation. Teler is fully model-agnostic, so developers bring their own STT engine, LLM, and TTS provider without any lock-in. It also includes a built-in carrier layer and SIP trunking, which means teams do not need to assemble a separate telephony vendor alongside their transcription API.

How much does a speech to text API USA provider cost in 2026?

Pricing varies widely across speech to text API USA providers. Teler starts at $15/channel/month (minimum 10 channels) for UAE deployments, with outbound calling at $0.20/minute, inbound at $0.10/minute, and media streaming at $0.0017/minute. AssemblyAI’s Universal-Streaming is priced at $0.15/hour. OpenAI Whisper charges approximately $0.006/minute via the API. Deepgram, Google Cloud Speech-to-Text, Amazon Transcribe, and Azure Speech do not publish standard per-minute rates publicly. For a team running 10,000 minutes per month, the total cost difference between providers can be significant once telephony infrastructure costs are included.

What latency can developers expect from a speech to text API USA provider?

As of 2026, Teler’s WebSocket streaming delivers sub-100ms latency for real-time AI voice loops, which is fast enough for natural, uninterrupted conversation. General CPaaS (Communications Platform as a Service, a cloud-based platform that adds voice and messaging capabilities via APIs) platforms typically add 400 to 800ms by abstracting the audio layer, because they convert raw audio into a proprietary format before passing it to the developer’s server. Deepgram claims under 300ms for its WebSocket path. Whisper and batch-oriented providers are not suitable for sub-500ms real-time conversation since they process audio in chunks rather than a continuous stream.

Which speech to text API USA provider works with any STT, LLM, or TTS provider?

Teler is the only provider on this list with a fully model-agnostic architecture. Developers bring their own STT engine (Google STT, Deepgram, Whisper, AssemblyAI), their own LLM (OpenAI GPT, Anthropic Claude, Google Gemini, or any custom model), and their own TTS (ElevenLabs, Azure TTS, Play.ht) and pay those providers directly. Teler handles only the voice transport layer. This matters for production AI voice agents because it means the team can swap any component of the stack without rebuilding the telephony integration, and there is no Teler-mandated AI provider that creates cost or capability lock-in over time.

What does TCPA compliance mean for AI outbound calling in the US?

TCPA (Telephone Consumer Protection Act) is the primary US federal law governing automated calls and texts. It requires prior express written consent before placing automated or prerecorded calls to mobile numbers. For AI voice agents making outbound calls, this means the system must verify and log consent before dialing, check the FCC’s National Do Not Call Registry, and avoid calling between 9 PM and 8 AM in the recipient’s local time zone. Violations carry fines of $500 to $1,500 per call, and class-action exposure is significant. Any speech to text API USA provider used for outbound AI calling should integrate cleanly with a consent management and DNC registry check layer.

What is the difference between SIP trunking and WebSocket streaming for AI voice?

WebSocket streaming keeps a persistent, full-duplex connection open between the caller and your server, so raw audio flows continuously with no per-request overhead. This is the right path for AI voice agents that need sub-100ms response loops, since the connection never closes between turns. SIP (Session Initiation Protocol) trunking uses the standard protocol for establishing voice calls over the internet and is better for connecting existing SIP-enabled platforms, PBX systems, or enterprise telephony infrastructure to a carrier network. If you are building an AI voice agent from scratch, start with WebSocket. If you are integrating with an existing contact center platform, SIP trunking is the faster path. Teler supports both.

How do I get started building an AI voice agent with a speech to text API USA provider?

The fastest path is to follow the Media Streaming Loop: the caller speaks, Teler captures the voice via WebSocket and streams raw L16/8000Hz audio to your server, your STT engine converts the audio to text, the text goes to your LLM which decides what to respond, the LLM response goes to your TTS engine which converts it back to audio, and that audio streams back through Teler to the caller. The full loop completes in under 100ms on Teler. Sign up at the Teler platform and follow the getting-started guide in the FreJun knowledge base. The first upgrade includes $5 in free calling credits (UAE) or 3,000 free minutes (India), so you can test the full stack before committing to a paid plan. WebSocket integration can be completed the same day.

Conclusion: Best speech to text API USA providers in 2026

The top three speech to text API USA providers in 2026 are Teler (best for full AI voice agent infrastructure), Deepgram (best for low-latency WebSocket transcription without telephony), and AssemblyAI (best for feature-rich live transcription with a generous free tier). For US developers building production AI voice agents, Teler is the clear top recommendation because it is the only provider that combines carrier-grade telephony, sub-100ms WebSocket streaming, SIP trunking, and a fully model-agnostic architecture in a single platform, with no proprietary lock-in and same-day integration. Start building your AI voice agent today by signing up at the Teler platform or visiting FreJun’s website to learn more.

About the Author: Subhash Kalluri is the Co-Founder of FreJun, an AI-powered call automation platform he has been building since 2019. With over 8 years of entrepreneurial experience in voice communication and SaaS, he helps sales and support teams automate calls, improve connect rates, and integrate calling workflows with their CRMs. Connect with him on LinkedIn.