AWS Marketplace (SageMaker)

Falcon 2.1 is available on AWS Marketplace as an Amazon SageMaker model package. You subscribe with your AWS account, deploy an endpoint in that account, and call it directly. There is no Murf API key, and no Murf server is in the request path. The container runs with SageMaker network isolation, so text and audio never leave your endpoint. AWS bills you and pays Murf.

The package ships the same production voice prompts as the Murf API: 141 voices across 42 locales and 35 languages. Streaming first audio is about 50 ms on a recommended instance.

Regions

As of November 2026, the listing is available in 16 AWS Regions:

us-east-1, us-east-2, us-west-1, us-west-2, ca-central-1, sa-east-1, eu-west-1, eu-west-2, eu-west-3, eu-central-1, eu-north-1, ap-south-1, ap-southeast-1, ap-southeast-2, ap-northeast-1, ap-northeast-2.

Pick the Region on the listing configuration page and copy the model package ARN from there. The ARN is different in every Region.

Prerequisites

• Subscribe. AWSMarketplaceManageSubscriptions, or the aws-marketplace:ViewSubscriptions, Subscribe, and Unsubscribe actions.
• Deploy. AmazonSageMakerFullAccess and iam:PassRole on a SageMaker execution role. SageMaker Studio and notebook instances already have a role you can use.
• GPU quota. In Service Quotas → Amazon SageMaker, set ml.g6e.xlarge for endpoint usage to at least 1. For batch transform, ml.g6e.xlarge for transform job usage is often 0 until you request it. Quota increases can take a day or more. If ml.g6e is not available in your Region, use ml.g6.xlarge or ml.g5.xlarge.

Quickstart

About 15 minutes, most of it waiting for the endpoint to start. The notebook does the same steps: paste the ARN into the first cell and run the rest.

import boto3, time
region = "us-east-1"  # the Region you copied the ARN for
model_package_arn = "<model package ARN from the listing>"
role = "arn:aws:iam::<account-id>:role/<sagemaker-execution-role>"
name = f"murf-falcon-{time.strftime('%Y%m%d-%H%M%S')}"
sm = boto3.client("sagemaker", region_name=region)
sm.create_model(
    ModelName=name,
    ExecutionRoleArn=role,
    PrimaryContainer={"ModelPackageName": model_package_arn},
    EnableNetworkIsolation=True,
)
sm.create_endpoint_config(
    EndpointConfigName=name,
    ProductionVariants=[{
        "VariantName": "AllTraffic",
        "ModelName": name,
        "InstanceType": "ml.g6e.xlarge",
        "InitialInstanceCount": 1,
        "ContainerStartupHealthCheckTimeoutInSeconds": 900,
    }],
)
sm.create_endpoint(EndpointName=name, EndpointConfigName=name)
sm.get_waiter("endpoint_in_service").wait(
    EndpointName=name, WaiterConfig={"Delay": 30, "MaxAttempts": 60}
)

import base64, json
runtime = boto3.client("sagemaker-runtime", region_name=region)
resp = runtime.invoke_endpoint(
    EndpointName=name,
    ContentType="application/json",
    Body=json.dumps({
        "text": "Hello from Murf Falcon 2.1 on Amazon SageMaker.",
        "voice_id": "en-US-gordon",
        "format": "wav",
    }),
)
body = json.loads(resp["Body"].read())
open("hello.wav", "wb").write(base64.b64decode(body["audio"]))

python examples/invoke.py "$ENDPOINT" realtime --text "Hello there." --out hello.wav

sm.delete_endpoint(EndpointName=name)
sm.delete_endpoint_config(EndpointConfigName=name)
sm.delete_model(ModelName=name)

Voices, languages, and styles

Ask the endpoint for its catalog. Voice IDs match the production Falcon catalog (for example en-US-gordon), and you can preview the same voices in the Voice Library.

voices = json.loads(runtime.invoke_endpoint(
    EndpointName=name,
    ContentType="application/json",
    Body=json.dumps({"action": "list_voices"}),
)["Body"].read())["voices"]

Each voice includes:

• locale: the language it speaks by default, such as en_US.
• supported_locales: every language it can speak, and the styles available in each. Many voices speak more than one language.
• styles: speaking styles for the default locale.

locale and style are optional. The default is the voice’s own locale and the first style listed for it. Locale codes use an underscore (hi_IN), not the hyphenated form the hosted API uses (hi-IN).

{
  "text": "ನಮಸ್ಕಾರ, ನಾನು ನಿಮಗೆ ಹೇಗೆ ಸಹಾಯ ಮಾಡಬಹುದು?",
  "voice_id": "kn-IN-harshitha",
  "locale": "kn_IN"
}

Ways to call the endpoint

Response streaming. Send "stream": true. The response is the audio itself (mp3 by default, or wav, ogg_opus, or raw PCM). "stream_format": "ndjson" returns JSON lines instead: {"type": "chunk", ...}, {"type": "timestamps", ...}, and a final {"type": "done"}. Word timestamps need ndjson.

Bidirectional streaming. One session carries many turns. Send a config frame, then text frames as tokens arrive. flush ends a turn and speaks any remaining text. cancel stops speech for barge-in. close ends the session. boto3 does not support this API yet. Use examples/invoke.py ... bidi, which needs aws-sdk-sagemaker-runtime-http2 and Python 3.12+.

Batch transform. One JSON request per S3 object, ContentType application/json, SplitType None, and "response_format": "binary" for playable files. The notebook has a full example.

Field names, audio formats, sample rates, and error codes are in the request reference. Real-time responses are JSON with base64 audio, duration_s, usage.characters, and request_id. words is present only when add_timestamps is true.

Instances and performance

The 2xlarge and 4xlarge sizes of these families, and ml.g6e.12xlarge, are also supported. Batch transform runs on ml.g6e.xlarge, ml.g6.xlarge, and ml.g5.xlarge.

Requests beyond the concurrent streams queue for up to 2 seconds, then return HTTP 429 capacity_exceeded. Add instances or use SageMaker auto scaling.

Measured on one ml.g6e.xlarge in November 2026, streaming 157-character requests from a client in the same Region:

English intelligibility on that run was 1.0–1.4% word error rate for en-US-natalie, en-AU-estelle, and en-UK-hazel, with 10 concurrent streams. That figure is for those English passages only.

Differences from the Murf API

Not every voice speaks every language. A 422 unsupported_locale or unsupported_style response lists the values that voice accepts.

Pricing

Real-time and streaming endpoints are billed per 1,000 input characters. Batch transform jobs are billed per instance-hour. Both charges are in addition to the SageMaker instance cost, which you pay to AWS. Current prices are on the listing.

Troubleshooting

The endpoint writes one line per request to CloudWatch Logs (/aws/sagemaker/Endpoints/<endpoint-name>) with character count, audio length, and latency. Text is never logged.

Security and privacy

• Text and audio stay in your account. With network isolation, the container has no network access and sends nothing to Murf or any third party.
• Everything the model needs is inside the model package. Nothing is downloaded at start-up.
• Generated audio carries an inaudible AudioSeal watermark that marks it as synthetic speech.

Support

Murf cannot see your requests, text, or audio. Email support@murf.ai with the AWS Region, model package version, the request_id from the response, and the error message. Billing disputes go through AWS Marketplace.