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.
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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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Supported services
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Container Deployment
Helm Deployment
Latest version
Operating system
Linux
Deployed on AWS
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Financing for AWS Marketplace purchases
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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 .
Legal
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 v1.8.3 (Oct 27, 2025)
Spice v1.8.3 is a patch release focused on performance, reliability, and observability. This release delivers optimizations for DuckDB acceleration, parameterized queries, and query plans. A new opt-in dedicated thread pool for queries is now in preview.
What's New in v1.8.3
DuckDB Data Accelerator Improvements
- Connection Pool Sizing: The DuckDB accelerator now supports a configurable connection_pool_size parameter, supporting fine-grained control over concurrent query execution. This enables tuning for high-concurrency workloads and improved resource utilization.
Example Spicepod.yaml snippet:
datasets: - from: postgres:my_table name: my_table acceleration: enabled: true engine: duckdb params: connection_pool_size: 10- Automatic Statistics Recomputation: The new on_refresh_recompute_statistics parameter, on by default, triggers automatic ANALYZE execution after refreshes. This keeps DuckDB optimizer statistics up-to-date, ensuring efficient query plans and optimal performance.
Example Spicepod.yaml snippet:
datasets: - from: postgres:my_table name: my_table acceleration: enabled: true engine: duckdb params: on_refresh_recompute_statistics: disabled # default enabledTask History SQL Query Plan Capture & Configuration
Spice now supports automated SQL query plan capture and store (via EXPLAIN or EXPLAIN ANALYZE) in the task history, enabling deeper analysis and debugging of query execution. This feature is configurable, supporting control of which queries are included based on duration thresholds and plan type.
- New Configuration Options:
- task_history.captured_plan: Controls which plan is captured (none, explain, or explain analyze). Default none.
- task_history.min_sql_duration: Minimum query duration before a plan is captured.
- task_history.min_plan_duration: Minimum plan execution duration before a plan is captured.
Example spicepod.yaml snippet:
runtime: task_history: captured_plan: explain analyze min_sql_duration: 5s min_plan_duration: 10sQuery plans are captured asynchronously to avoid blocking query execution. The result of the plan is stored in the standard sql_query output in the task history.
Learn more in the Task History Documentation .
Query Performance Optimizations
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Optimized Prepared Statements (Parameterized Queries): Prepared statement caching for parameterized SQL queries has been improved, reducing planning overhead for repeated queries with different parameters. This results in faster execution and lower latency for workloads that reuse query structures.
-
Limit Pushdown via BytesProcessedExec: Introduces the BytesProcessedExec physical operator, enabling limit pushdown for large datasets. This optimization reduces the amount of data processed and improves top-k query performance.
Dedicated Query Thread Pool (Opt-In)
Spice now supports running query execution and accelerated refreshes on a dedicated thread pool, separate from the HTTP server. This prevents heavy query workloads from slowing down API responses, keeping health and readiness checks fast. Opt-In for v1.8.3: This feature is opt-in for this release and will become enabled by default (opt-out) in v1.9.
Example Spicepod.yaml snippet:
runtime: params: dedicated_thread_pool: sql_engine # Default: disabledValidation & Reliability Improvements
-
Selective Evaluation Scorer Loading: Evaluation scorers are now loaded only when evaluation is explicitly defined, reducing unnecessary initialization and improving startup performance.
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Improved Error Reporting: Enhanced error messages for misconfigured full-text search (FTS) on datasets and views, providing actionable feedback for configuration issues.
REPL & Usability
- Execution Time Display: The Spice REPL now displays query execution time even when queries return no results, improving user feedback and diagnostics.
Contributors
Breaking Changes
No breaking changes.
Cookbook Updates
No major cookbook updates.
The Spice Cookbook includes 81 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:1.8.3-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:1.8.3-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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