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    Spice.ai Enterprise (BYOL)

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    Sold by: Spice AI 
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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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    Spice AI 
    Categories
    Cloud Operations 
    Databases & Analytics Platforms 
    Data Integration 
    Delivery method
    Supported services
    Delivery option
    Container Deployment
    Helm Deployment
    Latest version
    Operating system
    Linux
    Deployed on AWS

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    Pricing

    Spice.ai Enterprise (BYOL)

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    View purchase options
    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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    Usage information

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    Delivery details

    Container Deployment

    Supported services: Learn more 
    • 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 v1.8.0-enterprise (Oct 6, 2025)

    Spice v1.8.0-enterprise delivers major advances in data writes, scalable vector search, and now in preview - managed acceleration snapshots for fast cold starts. This release introduces write support for Iceberg tables using standard SQL INSERT INTO, partitioned S3 Vector indexes for petabyte-scale vector search, and preview of the AI SQL function for direct LLM integration in SQL. Additional improvements include improved reliability, and the v3.0.3 release of the Spice.js Node.js SDK.

    What's New in v1.8.0-enterprise

    Iceberg Table Write Support (Preview)

    Append Data to Iceberg Tables with SQL INSERT INTO: Spice now supports writing to Iceberg tables and catalogs using standard SQL INSERT INTO statements. This enables data ingestion, transformation, and pipeline use cases - no Spark or external writer required.

    • Append-only: Initial version targets appends; no overwrite or delete.
    • Schema validation: Inserted data must match the target table schema.
    • Secure by default: Writes are only enabled for datasets or catalogs explicitly marked with access: read_write.

    Example Spicepod configuration:

    catalogs: - from: iceberg:<https://glue.ap-northeast-3.amazonaws.com/iceberg/v1/catalogs/111111/namespaces> name: ice access: read_write datasets: - from: iceberg:<https://iceberg-catalog-host.com/v1/namespaces/my_namespace/tables/my_table> name: iceberg_table access: read_write

    Example SQL usage:

    -- Insert from another table INSERT INTO iceberg_table SELECT * FROM existing_table; -- Insert with values INSERT INTO iceberg_table (id, name, amount) VALUES (1, 'John', 100.0), (2, 'Jane', 200.0); -- Insert into catalog table INSERT INTO ice.sales.transactions VALUES (1001, '2025-01-15', 299.99, 'completed');

    Note: Only Iceberg datasets and catalogs with access: read_write support writes. Internal Spice tables and other connectors remain read-only.

    Learn more in the Iceberg Data Connector documentation .

    Acceleration Snapshots for Fast Cold Starts (Preview)

    Bootstrap Managed Accelerations from Object Storage: Spice now supports managed acceleration snapshots in preview, enabling datasets accelerated with file-based engines (DuckDB or SQLite) to bootstrap from a snapshot stored in object storage (such as S3) if the local acceleration file does not exist on startup. This dramatically reduces cold start times and enables ephemeral storage for accelerations with persistent recovery.

    Key features:

    • Rapid readiness: Datasets can become ready in seconds by downloading a pre-built snapshot, skipping lengthy initial acceleration.
    • Hive-style partitioning: Snapshots are organized by month, day, and dataset for easy retention and management.
    • Flexible bootstrapping: Configurable fallback and retry behavior if a snapshot is missing or corrupted.

    Example Spicepod configuration:

    snapshots: enabled: true location: s3://some_bucket/some_folder/ # Folder for storing snapshots bootstrap_on_failure_behavior: warn # Options: warn, retry, fallback params: s3_auth: iam_role # All S3 dataset params accepted here datasets: - from: s3://some_bucket/some_table/ name: some_table params: file_format: parquet s3_auth: iam_role acceleration: enabled: true snapshots: enabled # Options: enabled, disabled, bootstrap_only, create_only engine: duckdb mode: file params: duckdb_file: /nvme/some_table.db

    How it works:

    • On startup, if the acceleration file does not exist, Spice checks the snapshot location for the latest snapshot and downloads it.
    • Snapshots are stored as: s3://some_bucket/some_folder/month=2025-09/day=2025-09-30/dataset=some_table/some_table_<timestamp>.db
    • If no snapshot is found, a new acceleration file is created as usual.
    • Snapshots are written after each refresh (unless configured otherwise).

    Supported snapshot modes:

    • enabled: Download and write snapshots.
    • bootstrap_only: Only download on startup, do not write new snapshots.
    • create_only: Only write snapshots, do not download on startup.
    • disabled: No snapshotting.

    Note: This feature is only supported for file-based accelerations (DuckDB or SQLite) with dedicated files.

    Why use acceleration snapshots?

    • Faster cold starts: Skip waiting for full acceleration on startup.
    • Ephemeral storage: Use fast local disks (e.g., NVMe) for acceleration, with persistent recovery from object storage.
    • Disaster recovery: Recover from federated source outages by bootstrapping from the latest snapshot.

    Learn more in the Acceleration Snapshots documentation .

    Partitioned S3 Vector Indexes

    Efficient, Scalable Vector Search with Partitioning: Spice now supports partitioning Amazon S3 Vector indexes and scatter-gather queries using a partition_by expression in the dataset vector engine configuration. Partitioned indexes enable faster ingestion, lower query latency, and scale to billions of vectors.

    Example Spicepod configuration:

    datasets: - name: reviews vectors: enabled: true engine: s3_vectors params: s3_vectors_bucket: my-bucket s3_vectors_index: base-embeddings partition_by: - 'bucket(50, PULocationID)' columns: - name: body embeddings: from: bedrock_titan - name: title embeddings: from: bedrock_titan

    See the Amazon S3 Vectors documentation  for details.

    AI SQL function for LLM Integration (Preview)

    LLMs Directly In SQL: A new asynchronous ai SQL function enables direct calls to LLMs from SQL queries for text generation, translation, classification, and more. This feature is released in preview and supports both default and model-specific invocation.

    Example Spicepod model configuration:

    models: - name: gpt-4o from: openai:gpt-4o params: openai_api_key: ${secrets:openai_key}

    Example SQL usage:

    -- basic usage with default model SELECT ai('hi, this prompt is directly from SQL.'); -- basic usage with specified model SELECT ai('hi, this prompt is directly from SQL.', 'gpt-4o'); -- Using row data as input to the prompt SELECT ai(concat_ws(' ', 'Categorize the zone', Zone, 'in a single word. Only return the word.')) AS category FROM taxi_zones LIMIT 10;

    Learn more in the SQL Reference AI documentation .

    Spice.js v3.0.3 SDK

    Spice.js v3.0.3 Released: The official Spice.ai Node.js/JavaScript SDK  has been updated to v3.0.3, bringing cross-platform support, new APIs, and improved reliability for both Node.js and browser environments.

    • Modern Query Methods: Use sql(), sqlJson(), and nsql() for flexible querying, streaming, and natural language to SQL.
    • Browser Support: SDK now works in browsers and web applications, automatically selecting the optimal transport (gRPC or HTTP).
    • Health Checks & Dataset Refresh: Easily monitor Spice runtime health and trigger dataset refreshes on demand.
    • Automatic HTTP Fallback: If gRPC/Flight is unavailable, the SDK falls back to HTTP automatically.
    • Migration Guidance: v3 requires Node.js 20+, uses camelCase parameters, and introduces a new package structure.

    Example usage:

    import { SpiceClient } from '@spiceai/spice'; const client = new SpiceClient(apiKey); const table = await client.sql('SELECT * FROM my_table LIMIT 10'); console.table(table.toArray());

    See Spice.js SDK documentation  for full details, migration tips, and advanced usage.

    Additional Improvements

    • Reliability: Improved logging, error handling, and network readiness checks across connectors (Iceberg, Databricks, etc.).
    • Vector search durability and scale: Refined logging, stricter default limits, safeguards against index-only scans and duplicate results, and always-accessible metadata for robust queryability at scale.
    • Cache behavior: Tightened cache logic for modification queries.
    • Full-Text Search: FTS metadata columns now usable in projections; max search results increased to 1000.
    • RRF Hybrid Search: Reciprocal Rank Fusion (RRF) UDTF enhancements for advanced hybrid search scenarios.

    Contributors

    Breaking Changes

    This release introduces two breaking changes associated with the search observability and tooling.

    Firstly, the document_similarity  tool has been renamed to search. This has the equivalent change to tracing of these tool calls:

    ## Old: v1.7.1 >> spice trace tool_use::document_similarity >> curl -XPOST <http://localhost:8090/v1/tools/document_similarity> \ -d '{ "datasets": ["my_tbl"], "text": "Welcome to another Spice release" }' ## New: v1.8.0 >> spice trace tool_use::search >> curl -XPOST <http://localhost:8090/v1/tools/search> \ -d '{ "datasets": ["my_tbl"], "text": "Welcome to another Spice release" }'

    Secondly, the vector_search task in runtime.task_history has been renamed to search.

    Cookbook Updates

    The Spice Cookbook  now includes 80 recipes to help you get started with Spice quickly and easily.

    Upgrading

    To upgrade to v1.8.0-enterprise, use one of the following methods:

    CLI:

    spice upgrade

    Homebrew:

    brew upgrade spiceai/spiceai/spice

    Docker:

    Pull the spiceai/spiceai:1.8.0 image:

    docker pull spiceai/spiceai:1.8.0

    For available tags, see DockerHub .

    Helm:

    helm repo update helm upgrade spiceai spiceai/spiceai

    AWS Marketplace:

    Spice is now available in the AWS Marketplace !

    What's Changed

    Dependencies

    • iceberg-rust: Upgraded to v0.7.0-rc.1 
    • mimalloc: Upgraded from 0.1.47 to 0.1.48
    • azure_core: Upgraded from 0.27.0 to 0.28.0
    • Jimver/cuda-toolkit: Upgraded from 0.2.27 to 0.2.28

    Changelog

    Additional details

    Usage instructions

    Prerequisites

    Ensure the following tools and resources are ready before starting:

    • Docker: Install from https://docs.docker.com/get-docker/ .
    • 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

    1. Ensure the spicepod.yaml is in the current directory (e.g., ./spicepod.yaml).
    2. 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:1.8.0-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

    1. Confirm the container is running:

    docker ps

    Look for spiceai-enterprise with a STATUS of Up.

    1. 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:1.7.0-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

    Website 
    Documentation 

    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.

    Get support

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