Sold by: Deepgram
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Deepgram is the enterprise Voice AI platform for building and scaling real time voice applications on AWS. This product listing contains multiple versions of the nova-3 model which can each transcribe a set of languages. See version details for more information. Deepgram charges are billed per request as described by https://deepgram.com/pricing
Our APIs for Nova Speech to Text (STT) are natively available in the new SageMaker Bi-Directional Streaming API. Additional native touchpoints with Amazon Bedrock, Lex, and Amazon Connect make it simple to compose full voice experiences with the cloud services your teams already trust.
Overview
Deepgram powers end to end voice solutions on AWS, from real time transcription to lifelike speech synthesis and interruptible, human like voice agents. Deploy our STT/TTS and agent runtime where you need them: In SageMaker, in a Deepgram managed Dedicated environment or self hosted inside your AWS VPC for maximum control and compliance, with native touchpoints to Amazon Bedrock and Amazon Connect to compose complete voice workflows.
Procure through AWS Marketplace to accelerate onboarding with usage based pricing and consolidated billing on your AWS invoice, ideal for trials, POCs, and scaling to production while aligning to AWS commitments.
Deepgrams AWS alignment includes the AWS Generative AI Competency and a multi year strategic collaboration, giving teams confidence that integrations, cosell, and global scale on AWS are first class.
Use cases include: real time contact center transcription and automation with Amazon Connect + Lex, Bedrock powered voice agents with Deepgram STT/TTS, and streaming/batch analytics via S3, API Gateway, Lambda, and EKS/EC2, all built on the AWS patterns your teams already trust.
Highlights
- Real time STT for human like conversations: Sub 300 ms streaming latency with industry leading accuracy (Nova 3: 6.84% median WER streaming; 5.26% batch) to keep pace with fast, noisy speech.
- Natural, low latency TTS: Sub 250 ms responses with lifelike speech and streaming delivery for natural turn taking in real time.
- Production ready voice agents on AWS: Combine Deepgram STT/TTS with Amazon Bedrock for reasoning and Amazon Connect and Lex for contact center workflows supporting interruptible, human like dialogs at scale alongside real time STT voice agents and real time STT for transcription.
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Pricing
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Try this product free for 14 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $3.90/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $0.001/request |
Dimensions summary
This listing bills in two ways for batch speech-to-text. The first dimension charges for model inference time on the ml.m5.xlarge instance, priced by the hour that the endpoint runs. The second dimension charges per inference request you submit. You deploy the model as a SageMaker endpoint and pay through your AWS account. The two dimensions work together: you pay for the compute hours the endpoint stays active and for the volume of transcription requests it processes. There are no upfront commitments; charges reflect your actual usage.
Top-of-mind questions for buyers
You get a SageMaker endpoint running on the ml.m5.xlarge instance type in batch mode. You pay for every hour the endpoint stays active. This is a CPU instance type. Note that GPU instance families like g4dn, g5, g6, or g6e are also selectable when creating your endpoint configuration.
The hourly inference charge applies for every hour the endpoint stays active, whether or not it processes audio. To stop those charges, you must delete or scale the endpoint. Asynchronous endpoints can scale to zero when there are no requests to process, avoiding idle compute charges.
Both charges apply at the same time and appear together. The hourly charge accrues for as long as the endpoint runs. The per-request charge accrues each time you submit a transcription request. Compute hours usually dominate for endpoints left running continuously, while request volume matters more for bursty batch jobs.
developers.deepgram.com
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Additional details
Inputs
- Input MIME type
- audio/*, *
https://github.com/deepgram-devs/dg-sagemaker
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Support
Vendor support
Basic support is provided through email (aws@deepgram.com ). Premium and VIP support packages are also available for enterprise clients.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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