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.
You will be billed $0.0077/min as described by https://deepgram.com/pricing. Private pricing available upon request.
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
You pay based on usage, with two separate dimensions. The first bills by the hour for model inference running on an ml.m5.xlarge instance in batch mode. You are charged for each hour that instance runs. The second bills by request count, charging per inference request processed. These dimensions cover different ways to run and pay for speech-to-text transcription. You deploy the model through a SageMaker endpoint and are billed only after the endpoint runs. No upfront commitment applies to this usage-based structure.
Top-of-mind questions for buyers
You pay for each hour the SageMaker endpoint compute instance runs in batch mode. The instance processes pre-recorded audio files as a single-file transcription request. Charges accrue while the instance is deployed and running, not by audio length. Stopping the endpoint stops the hourly software charges.
Each inference call you send to the deployed endpoint counts as one request. For batch or single-file transcription, one submitted audio file equals one request. The request charge applies per call processed, regardless of the audio duration inside that file.
The two dimensions bill independently and can both appear on the same invoice. The hourly charge reflects how long your compute instance runs. The request charge reflects how many inference calls you send. Running time drives cost for continuous endpoints; request volume drives cost for high-throughput usage.
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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