Sold by: Spice AIÂ
Deployed on AWS
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Spice.ai Enterprise is a portable (<150MB) compute engine built in Rust for data-intensive and intelligent applications. Deployable as a container on AWS ECS, EKS, or hybrid cloud+edge, it includes Enterprise licensing, support, and SLA.
Overview
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Spice.ai Enterprise is a portable (<150MB) compute engine built in Rust for data-intensive and intelligent applications. It accelerates SQL queries across databases, data warehouses, and data lakes using Apache Arrow, DataFusion, DuckDB, or SQLite. Integrated and co-deployed with data-intensive applications, Spice materializes and accelerates data from object storage, ensuring sub-second query performance and resilient AI applications. Deployable as a container on AWS ECS, EKS, or hybrid cloud & edge, it includes enterprise licensing, support, and SLAs.
Note: Spice.ai Enterprise requires an existing commercial license. For details, please contact sales@spice.ai .
Highlights
- Unified data query and AI engine accelerating SQL queries across databases, data warehouses, and data lakes. Delivers sub-second query performance while grounding mission-critical AI applications with real-time context to minimize errors and hallucinations.
- Advanced AI and retrieval tools, featuring vector and hybrid search, text-to-SQL, and LLM memory, enabling data-grounded AI applications with more than 25 data connectors enabling federated queries and real-time applications.
- Deployable as a container on AWS ECS, EKS, or on-premises, with dedicated support and SLAs for scalable, secure integration into any architecture.
Details
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Delivery method
Supported services
Delivery option
Container Deployment
Helm Deployment
Latest version
Operating system
Linux
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
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.
Quick Launch
Leverage AWS CloudFormation templates to reduce the time and resources required to configure, deploy, and launch your software.
Pricing
Pricing and entitlements for this product are managed through an external billing relationship between you and the vendor. You activate the product by supplying a license purchased outside of AWS Marketplace, while AWS provides the infrastructure required to launch the product. AWS Subscriptions have no end date and may be canceled any time. However, the cancellation won't affect the status of the external license.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Vendor refund policy
Refunds for Spice.ai Enterprise container subscriptions are not available after activation, as usage begins immediately upon deployment. Ensure compatibility with AWS ECS, EKS, or on-premises setups before purchase. For billing inquiries, contact AWS Marketplace support or Spice AI directly at support@spice.ai .
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Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Container Deployment
Supported services: Learn moreÂ
- Amazon ECS
- Amazon EKS
- Amazon ECS Anywhere
Container image
Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
Version release notes
Spice 1.6.1 is a patch release that provides improved Kafka type inference and JSON flattening support, alongside several bug fixes.
What's New in v1.6.1-enterprise
Improved Kafka Type Inference: Improve Kafka type inference by configuring the number of Kafka messages sampled during schema inference. Increasing the sample size can improve the robustness and reliability of inferred schemas, especially in cases where data contains optional fields or varying structures.
Example spicepod.yml:
dataset: - from: kafka:orders_events name: orders params: schema_infer_max_records: 100 # Default 1.For details, see the Kafka Data Connector Documentation .
Improved Kafka JSON Support: Enable nested JSON Kafka messages to be represented in flattened JSON format for the dataset schema.
Example spicepod.yml:
dataset: - from: kafka:orders_events name: orders params: flatten_json: true # default falseFor example, the object:
{ "order_id": "a1f2c3d4-1111-2222-3333-444455556666", "customer": { "id": 101, "name": "Alice", "premium": true, "contact": { "email": "alice@example.com", "phone": "555-1234" } }, "discount": 5.0, "shipped": false }With flatten_json: true the result is:
+------------------------+-----------+-------------+ | column_name | data_type | is_nullable | +------------------------+-----------+-------------+ | order_id | Utf8 | YES | | customer.id | Int64 | YES | | customer.name | Utf8 | YES | | customer.premium | Boolean | YES | | customer.contact.email | Utf8 | YES | | customer.contact.phone | Utf8 | YES | | discount | Float64 | YES | | shipped | Boolean | YES | +------------------------+-----------+-------------+With flatten_json: false or ommitted the result is:
+-------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------+ | column_name | data_type | is_nullable | +-------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------+ | order_id | Utf8 | YES | | customer | Struct([Field { name: "id", data_type: Int64, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "name", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "premium", data_type: Boolean, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "contact", data_type: Struct([Field { name: "email", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "phone", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }]), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }]) | YES | | discount | Float64 | YES | | shipped | Boolean | YES | +-------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------+For details, see the Kafka Data Connector Documentation .
Contributors
Breaking Changes
No breaking changes.
Cookbook Updates
No new cookbook recipes added in this release.
The Spice Cookbook includes 77 recipes to help you get started with Spice quickly and easily.
Additional details
Usage instructions
Prerequisites
Ensure the following tools and resources are ready before starting:
- Docker: Install from https://docs.docker.com/get-docker/Â .
- AWS CLI: Install from https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html .
- AWS ECR Access: Authenticate to the AWS Marketplace registry: aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 709825985650.dkr.ecr.us-east-1.amazonaws.com
- Spicepod Configuration: Prepare a spicepod.yaml file in your working directory. A spicepod is a YAML manifest file that configures which components (i.e. datasets) are loaded. Refer to https://spiceai.org/docs/getting-started/spicepods for details.
- AWS ECS Prerequisites (for ECS deployment): An ECS cluster (Fargate or EC2) configured in your AWS account. An IAM role for ECS task execution (e.g., ecsTaskExecutionRole) with permissions for ECR, CloudWatch, and other required services. A VPC with subnets and a security group allowing inbound traffic on ports 8090 (HTTP) and 50051 (Flight).
Running the Container
- Ensure the spicepod.yaml is in the current directory (e.g., ./spicepod.yaml).
- Launch the container, mounting the current directory to /app and exposing HTTP and Flight endpoints externally:
docker run --name spiceai-enterprise
-v $(pwd):/app
-p 50051:50051
-p 8090:8090
709825985650.dkr.ecr.us-east-1.amazonaws.com/spice-ai/spiceai-enterprise-byol:1.6.1-enterprise-models
--http 0.0.0.0:8090
--flight 0.0.0.0:50051
- The -v $(pwd):/app mounts the current directory to /app, where spicepod.yaml is expected.
- The --http and --flight flags set endpoints to listen on 0.0.0.0, allowing external access (default is 127.0.0.1).
- Ports 8090 (HTTP) and 50051 (Flight) are mapped for external access.
Verify and Monitor the Container
- Confirm the container is running:
docker ps
Look for spiceai-enterprise with a STATUS of Up.
- Inspect logs for troubleshooting:
docker logs spiceai-enterprise
Deploying to AWS ECS Create an ECS Task Definition and use this value for the image: 709825985650.dkr.ecr.us-east-1.amazonaws.com/spice-ai/spiceai-enterprise-byol:1.6.1-enterprise-models. Configure the port mappings for the HTTP and Flight ports (i.e. 8090 and 50051).
Override the command to expose the HTTP and Flight ports publically and link to the Spicepod configuration hosted on S3:
"command": [ "--http", "0.0.0.0:8090", "--flight", "0.0.0.0:50051", "s3://your_bucket/path/to/spicepod.yaml" ]
Register the task definition in your AWS account, i.e. aws ecs register-task-definition --cli-input-json file://spiceai-task-definition.json --region us-east-1
Then run the task as you normally would in ECS.
Resources
Vendor resources
Support
Vendor support
Spice.ai Enterprise includes 24/7 dedicated support with a dedicated Slack/Team channel, priority email and ticketing, ensuring critical issues are addressed per the Enterprise SLA.
Detailed enterprise support information is available in the Support Policy & SLA document provided at onboarding.
For general support, please email support@spice.ai .
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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