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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 flux model which can each speak a set of languages and voices. 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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AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Free trial
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 text-to-speech usage two ways. The first dimension charges by the hour for model inference running on an ml.m5.xlarge instance in batch mode. You pay for the time the compute instance runs. The second dimension charges by request count, so cost scales with the number of inference calls you make. These two dimensions are independent measures of the same deployment. You run the model on your own AWS SageMaker endpoint and are billed through your AWS account based on actual usage.
Top-of-mind questions for buyers
The hourly charge covers running the text-to-speech model on one ml.m5.xlarge compute instance in batch mode. You pay for the time the SageMaker endpoint stays deployed and running. A stopped or deleted endpoint stops the software charge, though underlying AWS storage may still apply.
Both charges apply at the same time on the same invoice. The hourly charge accrues while your instance runs. The request charge accrues per inference call you send. Steady, high-volume traffic makes both grow together, while the request count tracks how much text you actually convert to speech.
An inference request is one call sent to your deployed SageMaker endpoint to convert text into speech. Each call you submit counts toward the request total. Cost scales directly with how many separate calls your application makes, independent of the hours the instance runs.
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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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