Overview
Architecture
Architecture

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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.
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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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Delivery details
Container Deployment
- Amazon ECS
- Amazon EKS
- Amazon ECS Anywhere
- Amazon EKS 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 v1.10.0 (Dec 2, 2025)
Spice v1.10.0 introduces a new Caching Acceleration Mode with stale-while-revalidate (SWR) semantics for disk-persisted, low-latency queries with background refresh. This release also adds the TinyLFU eviction policy for the SQL results cache, a preview of the DynamoDB Streams connector for real-time CDC, S3 location predicate pruning for faster partitioned queries, improved distributed query execution, and multiple security hardening improvements.
What's New in v1.10.0
Caching Acceleration Mode
Low-Latency Queries with Background Refresh: This release introduces a new caching acceleration mode that implements the stale-while-revalidate (SWR) pattern. Queries return cached results immediately while data refreshes asynchronously in the background, eliminating query latency spikes during refresh cycles. Cached data persists to disk using DuckDB, SQLite, or Cayenne file modes.
Key Features:
- Stale-While-Revalidate (SWR): Returns cached data immediately while refreshing in the background, reducing query latency
- Disk Persistence: Cached results persist across restarts using DuckDB, SQLite, or Cayenne file modes
- Configurable Refresh: Control refresh intervals with refresh_check_interval to balance freshness and source load
Recommendation: Use retention configuration with caching acceleration to ensure stale data is cleaned up over time.
Example spicepod.yaml configuration:
datasets: - from: <http://localhost:7400> name: cached_data time_column: fetched_at acceleration: enabled: true engine: duckdb mode: file # Persist cache to disk refresh_mode: caching refresh_check_interval: 10m retention_check_enabled: true retention_period: 24h retention_check_interval: 1hFor more details, refer to the Data Acceleration Documentation .
TinyLFU Cache Eviction Policy
Higher Cache Hit Rates for SQL Results Cache: A new TinyLFU cache eviction policy is now available for the SQL results cache. TinyLFU is a probabilistic cache admission policy that maintains higher hit rates than LRU while keeping memory usage predictable, making it ideal for workloads with varying query frequency patterns.
Example spicepod.yaml configuration:
runtime: caching: sql_results: enabled: true eviction_policy: tiny_lfu # default: lruFor more details, refer to the Caching Documentation and the Moka TinyLFU Documentation for details of the algorithm.
DynamoDB Streams Data Connector (Preview)
Real-Time Change Data Capture for DynamoDB: The DynamoDB connector now integrates with DynamoDB Streams for real-time change data capture (CDC). This enables continuous synchronization of DynamoDB table changes into Spice for real-time query, search, and LLM-inference.
Key Features:
- Real-Time CDC: Automatically captures inserts, updates, and deletes from DynamoDB tables as they occur
- Table Bootstrapping: Performs an initial full table scan before streaming changes, ensuring complete data consistency
- Acceleration Integration: Works with refresh_mode: changes to incrementally update accelerated datasets
Note: DynamoDB Streams must be enabled on your DynamoDB table. This feature is in preview.
Example spicepod.yaml configuration:
datasets: - from: dynamodb:my_table name: orders_stream acceleration: enabled: true refresh_mode: changes # Enable Streams captureFor more details, refer to the DynamoDB Connector Documentation .
OpenTelemetry Metrics Exporter
Spice can now push metrics to an OpenTelemetry collector, enabling integration with platforms such as Jaeger , New Relic , Honeycomb , and other OpenTelemetry-compatible backends.
Key Features:
- Protocol Support: Supports the gRPC (default port 4317) protocol
- Configurable Push Interval: Control how frequently metrics are pushed to the collector
Example spicepod.yaml configuration for gRPC:
runtime: telemetry: enabled: true otel_exporter: endpoint: 'localhost:4317' push_interval: '30s'For more details, refer to the Observability & Monitoring Documentation .
S3 Connector Improvements
S3 Location Predicate Pruning: The S3 data connector now supports location-based predicate pruning, dramatically reducing data scanned by pushing down location filter predicates to S3 listing operations. For partitioned datasets (e.g., year=2025/month=12/), Spice now skips listing irrelevant partitions entirely, significantly reducing query latency and S3 API costs.
AWS S3 Tables Write Support: Full read/write capability for AWS S3 Tables , enabling direct integration with AWS's managed table format for S3. Use standard SQL INSERT INTO to write data.
For more details, refer to the S3 Data Connector Documentation and Glue Data Connector Documentation .
Faster Distributed Query Execution
Distributed query planning and execution have been significantly improved:
- Fixed executor registration in cluster mode for more reliable distributed deployments
- Improved hostname resolution for Flight server binding, enabling better executor discovery
- Distributed accelerator registration: Data accelerators now properly register in distributed mode
- Optimized query planning: DistributeFileScanOptimizer improvements for faster planning with large datasets
For more details, refer to the Distributed Query Documentation .
Search Improvements
Search capabilities have been improved with several performance and reliability enhancements:
- Fixed FTS query blocking: Full-text search queries no longer block unnecessarily, improving query responsiveness
- Optimized vector index operations: Eliminated unnecessary list_vectors calls for better performance
- Improved limit pushdown: IndexerExec now properly handles limit pushdown for more efficient searches
For more details, refer to the Search Documentation .
Security Hardening
Multiple security improvements have been implemented:
- SQL Identifier Quoting: Hardened SQL identifier quoting across all database connectors (PostgreSQL, MySQL, DuckDB, etc.) to prevent SQL injection attacks through table or column names
- Token Redaction: Sensitive authentication tokens are now fully redacted in debug and error output, preventing accidental credential exposure in logs
- Path Traversal Prevention: Fixed tar extraction operations to prevent directory traversal vulnerabilities when processing archived files
- Input Sanitization: Added strict validation for top_n_sample order_by clause parsing to prevent injection attacks
- Glue Credential Handling: Prevented automatic loading of AWS credentials from environment in Glue connector, ensuring explicit credential configuration
Developer Experience Improvements
- Health probe metrics: Added health probe latency metrics for better observability
- CLI improvements: Fixed .clear history command in the REPL to fully clear persisted history
Contributors
Breaking Changes
No breaking changes.
Cookbook Updates
No major cookbook updates.
The Spice Cookbook includes 82 recipes to help you get started with Spice quickly and easily.
Upgrading
To upgrade to v1.10.0, use one of the following methods:
CLI:
spice upgradeHomebrew:
brew upgrade spiceai/spiceai/spiceDocker:
Pull the spiceai/spiceai:1.10.0 image:
docker pull spiceai/spiceai:1.10.0For available tags, see DockerHub .
Helm:
helm repo update helm upgrade spiceai spiceai/spiceaiAdditional 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:1.10.0-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
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.