# Marengo Embed v2.7
##  TwelveLabs — Marengo Embed v2.7
## Model Details
Marengo Embed v2.7 is TwelveLabs' video embedding model for multimodal video understanding, search, and classification. For more information about model development and performance, see the [model/service card](https://docs.twelvelabs.io/docs/concepts/models/marengo).
+ **Model launch date:** Dec 4, 2024
+ **EOL no sooner than:** Dec 4, 2025
+ **Legacy period:** at least 6 months
+ **Model lifecycle policy:** [Model lifecycle (For Models Launched Prior to Sept 7 2026)](model-lifecycle-legacy.md)
+ **Model EOL date:** November 30, 2026
+ **End User License Agreements and Terms of Use:** [View](https://aws.amazon.com/legal/bedrock/third-party-models/)
+ **Model lifecycle:** Legacy
+ **Marketplace product ID:** `prod-o6xchhpirymvs`
| **Input Modalities** | **Output Modalities** | **[APIs supported](apis.html)** | **[Endpoints supported](endpoints.html)** |
| --- | --- | --- | --- |
|  Audio |  Embedding |  Responses |  bedrock-runtime |
|  Image |  Image |  Chat Completions |  bedrock-mantle |
|  Speech |  Speech |  Invoke | |
|  Text |  Text |  Converse | |
|  Video |  Video |  StartAsyncInvoke | |
**Tip**
Whenever possible, we recommend using the `bedrock-runtime` endpoint for new applications. See [Inference endpoints supported by Amazon Bedrock](endpoints.md) for details.
## Pricing
This model is a third-party model offered and billed through AWS Marketplace. Charges appear on your AWS bill and in AWS Cost Explorer under the model provider (not under Amazon Bedrock). For pricing, see the [Amazon Bedrock Pricing](https://aws.amazon.com/bedrock/pricing/) page.
## Programmatic Access
Use the following model IDs and endpoint URLs to access this model programmatically. For more information about the available APIs and endpoints, see [APIs supported](apis.html) and [Endpoints supported](endpoints.html).
| **Endpoint** | **Model ID** | **In-Region endpoint URL** | **Geo inference ID** | **Global inference ID** |
| --- | --- | --- | --- | --- |
| bedrock-runtime | twelvelabs.marengo-embed-2-7-v1:0 | https://bedrock-runtime.{region}.amazonaws.com | `us.twelvelabs.marengo-embed-2-7-v1:0`
`eu.twelvelabs.marengo-embed-2-7-v1:0` | Not supported |
*For example, if region is us-east-1 (N. Virginia), then the bedrock-runtime endpoint URL will be "https://bedrock-runtime.us-east-1.amazonaws.com" and for bedrock-mantle will be "https://bedrock-mantle.us-east-1.api.aws/v1".*
**Note**
The model ID to use depends on the API. `StartAsyncInvoke` takes the base model ID, `twelvelabs.marengo-embed-2-7-v1:0`, as shown in the Sample Code section; passing a Geo inference ID to `StartAsyncInvoke` returns a validation error. The Geo inference IDs apply to synchronous `InvokeModel` requests, which don't accept the base model ID for this model. The **In-Region** and **Geo** columns in the Regional Availability section describe synchronous on-demand inference.
## Service Tiers
Amazon Bedrock offers multiple service tiers to match your workload requirements. **Standard** provides pay-per-token access with no commitment (set `"service_tier": "default"` or omit the field). **Priority** delivers the fastest response times for a price premium (set `"service_tier": "priority"`). **Flex** provides lower-cost access for flexible, non-time-sensitive workloads (set `"service_tier": "flex"`). **Reserved** provides dedicated throughput with a term commitment for predictable workloads; it is set at the account level rather than per request (contact your AWS account team to enable). For more information, see [service tiers](service-tiers-inference.html).
| **Standard** | **Priority** | **Flex** | **Reserved** |
| --- | --- | --- | --- |
|  |  |  |  |
## Regional Availability
***Regional availability at a glance***
Amazon Bedrock offers three inference options: **In-Region** keeps requests within a single Region for strict compliance, **Geo Cross-Region** routes across Regions within a geography (such as US, EU, and APAC) while respecting data residency, and **Global Cross-Region** routes anywhere worldwide when there are no residency constraints. Refer to the [Regional availability by models](models-region-compatibility.md) page for more details.
| **Region** | **In-Region** | **Geo** | **Global** |
| --- | --- | --- | --- |
| us-east-1 (N. Virginia) |  |  |  |
| eu-west-1 (Ireland) |  |  |  |
***Geo inference details***
**Geo: US**
Geo Inference ID: `us.twelvelabs.marengo-embed-2-7-v1:0`
| **Source Region** | **Destination Regions** |
| --- | --- |
| us-east-1 (N. Virginia) | us-east-1 (N. Virginia), us-east-2 (Ohio), us-west-1 (N. California), us-west-2 (Oregon) |
**Geo: EU**
Geo Inference ID: `eu.twelvelabs.marengo-embed-2-7-v1:0`
| **Source Region** | **Destination Regions** |
| --- | --- |
| eu-west-1 (Ireland) | eu-central-1 (Frankfurt), eu-north-1 (Stockholm), eu-south-1 (Milan), eu-south-2 (Spain), eu-west-1 (Ireland), eu-west-3 (Paris) |
## Quotas and Limits
Your AWS account has default quotas to maintain the performance of the service and to ensure appropriate usage of Amazon Bedrock. The default quotas assigned to an account might be updated depending on regional factors, payment history, fraudulent usage, and/or approval of a quota [increase request](quotas-increase.html). For more information, see [Quotas for Amazon Bedrock](quotas.md) documentation and see the [limits](/general/latest/gr/bedrock.html#limits_bedrock) for the model.
## Sample Code
**Step 1 - AWS Account:** If you have an AWS account already, skip this step. If you are new to AWS, sign up for an [AWS account](https://portal.aws.amazon.com/billing/signup).
**Step 2 - API key:** Go to the [Amazon Bedrock console](https://console.aws.amazon.com/bedrock/home#/api-keys/long-term/create) and generate a long-term API key.
**Step 3 - Get the SDK:**
```
pip install boto3
```
**Step 4 - Set environment variables:**
```
AWS_BEARER_TOKEN_BEDROCK=""
```
**Step 5 - Run your first inference request:** This model uses `StartAsyncInvoke`. Save the file as `bedrock-first-request.py`
```
import boto3
client = boto3.client('bedrock-runtime', region_name='us-east-1')
response = client.start_async_invoke(
modelId='twelvelabs.marengo-embed-2-7-v1:0',
modelInput={},
outputDataConfig={'s3OutputDataConfig': {'s3Uri': 's3://your-bucket/output/'}}
)
print(response)
```