ShannonBase
Open-source MySQL 8.4-compatible HTAP database with an in-memory column store, in-database ML, vector search, and an in-kernel agent runtime.
For a MySQL shop that wants faster reporting plus in-database vectors and model training without a second system, ShannonBase is a serious open-source candidate — and its free Personal tier means a proof of concept costs nothing but a container. The published TPC-H SF 0.2 run shows 60.9× on Q11 and 45.0× on Q7 versus the InnoDB row store, with the harness public so you can reproduce it. Against TiDB the pitch is deeper AI in the kernel and a shorter migration path; against ClickHouse plus a vector store it is one system instead of three. The catch is maturity: v0.1, Linux x86_64 only, a thin connector ecosystem, and a pricing page whose Personal plan caps at 3 users and reserves SAML/SSO
Verified 6d ago · liveness 82/100 · cite: rightaichoice.com/tools/shannonbase
- MySQL shops that want faster analytics without a warehouse or application changes
- Teams that want in-database model training and prediction driven from plain SQL
- Developers who need vector search and local LLM inference without external services
- Startups and labs testing HTAP on the free Personal tier before committing
- Teams that need a managed cloud-native serverless database rather than a self-hosted Docker container
- Anyone needing a broad catalog of prebuilt third-party connectors beyond MySQL tooling
- Buyers who want fully autonomous agent writes with no human approval step
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Skip ShannonBase if you need a managed cloud database, distributed multi-node horizontal scaling, or a large catalog of prebuilt third-party connectors, rather than a self-hosted single-container MySQL-compatible engine you operate yourself.
SAML/SSO and uptime SLA are locked to the Enterprise tier, so security-conscious teams outgrow the $19/mo Startup plan before they get single sign-on.
The free Personal tier (up to 3 users) makes ShannonBase unusually cheap to evaluate, and Startup at $19/mo covers up to 20 users — well below typical managed HTAP or columnar warehouse spend. But that page still carries placeholder plan copy, and the real cost driver is the RAM you allocate to the in-memory column store plus the ops time for a self-hosted v0.1 engine. Compare against your existing MySQL hosting plus whatever you pay for a warehouse and a vector database today.
In short
ShannonBase — Open-source MySQL 8.4-compatible HTAP database with an in-memory column store, in-database ML, vector search, and an in-kernel agent runtime. Best for MySQL shops that want faster analytics without a warehouse or application changes, Teams that want in-database model training and prediction driven from plain SQL, Developers who need vector search and local LLM inference without external services. Free to start; paid plans from $19/mo.
What's new in ShannonBase
Checked 6 days agoAcross the latest 4 updates: 2 feature updates, 1 changelog entry and 1 news mention.
Why the Agent Lives Inside the Kernel
Engineering post explaining ShannonBase's in-kernel agent runtime and why the agent runs inside the database rather than as an external orchestration layer.
TPC-H SF 0.2 benchmark: column store up to 60.9× faster than InnoDB
Published a full 22-query TPC-H SF 0.2 run on the developer build showing Rapid beating InnoDB by 60.9× on Q11 and 45.0× on Q7, with the harness public.
Vectorized hash join lands in Rapid
Rapid gains a vectorized hash join with batched build and probe phases and SIMD-accelerated predicate evaluation, plus fixes to aggregate overflow handling.
Agent approval workflow is now durable
Plan rows commit independently of the approval wait, so a pending agent decision no longer holds a transaction open.
What people actually say about ShannonBase — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
11 mentions across 3 sources (Hacker News, Product Hunt, GitHub) · researched Jul 3, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +100% MySQL compatible, enabling zero-code migration.
- +Native ML and LLM inference directly in the database.
- +JavaScript stored functions allow building AI agents natively.
- +Cost-based and ML-based optimizer improves query performance.
- +In-memory columnar engine for real-time analytical processing.
- −Very limited independent community feedback and real-world validation.
- −Documentation and support quality are unclear due to early stage.
- −Potential performance bottlenecks at scale not yet demonstrated.
- −JavaScript agent feature may introduce security and complexity risks.
- −Enterprise reliability not proven; no large-scale case studies available.
- • Additional costs for xPU acceleration or large-scale vector processing not disclosed.
- • Enterprise tier pricing unclear; may require long-term contracts.
Viability Score
How well maintained and how widely used is ShannonBase? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- HTAP: transactional and analytical workloads on one MySQL 8.4-compatible engine
- Rapid in-memory column store as a secondary engine with cost-based per-query routing
- MySQL 8.4 wire compatible — existing drivers, ORMs, and mysqldump files keep working
- Real-time propagation from InnoDB to the column store via redo log plus DML notifications
- Native VECTOR type with ART index and in-process embedding generation
- Vector similarity search via ORDER BY vector_distance(v, ?) in standard SQL
- In-database model training and prediction from SQL using LightGBM and ONNX Runtime
- Local LLM generation with retrieval-augmented generation (RAG) called from SQL via sys.shannon_chat
- In-kernel JavaScript agent runtime and system agent with in-process SQL bridge
- Human approval workflow required before any agent write commits
- Durable agent approval workflow — plan rows commit independently of the approval wait (30 Jul 2026)
- Vectorized hash join with SIMD-accelerated batched build and probe phases in Rapid (14 Aug 2026)
- MVCC version linking in the column store for concurrent scans and transactions
- Docker deployment — single container, no external dependencies
- Open source under GPL v2, source and docs on GitHub
About ShannonBase
ShannonBase from Shannon Data AI is an open-source (GPL v2) database that keeps your InnoDB tables and adds Rapid, an in-memory columnar secondary engine, on the same MySQL 8.4 wire protocol. You load a table with ALTER TABLE lineitem SECONDARY_ENGINE = RAPID, and the optimizer decides per query whether the row store or the column store answers. Committed changes reach the column store over the redo log and a DML notification path, so analytical reads see transactional writes without a sync window, and MVCC version linking in the column store lets a long scan and a short transaction share one table. Because it is wire-compatible, your existing mysql-connector, JDBC, Go sql-driver, SQLAlchemy, Prisma, mysqldump, ProxySQL, and Canal tooling keeps working. The AI work sits inside the kernel rather than beside it: a native VECTOR type with an ART index queried via ORDER BY vector_distance(v, ?), embeddings generated in-process, LightGBM and ONNX Runtime in the server for train-and-predict from plain SQL (CALL sys.ml_train('churn', ...)), local LLM generation with RAG, and a JavaScript agent runtime plus system agent that complete natural-language tasks through an in-process SQL bridge with human approval before any write commits. It fits MySQL-centric teams that want reporting speed, vectors, and model inference without standing up a warehouse, a separate vector database, or an ML pipeline. The tradeoffs are real: this is v0.1, documented only for Linux x86_64, shipped as a single Docker container rather than a managed service, and the free/paid pricing page still carries placeholder-style plan copy.
Behind the Verdict
Strengths. ShannonBase's central design choice is that Rapid is a secondary engine, not a downstream replica. Because changes flow from InnoDB over the redo log and a DML notification path, you get a columnar mirror without an ETL job to schedule or a replication lag number to argue about, and MVCC version linking in the column store is what allows a long analytical scan and a short transaction to touch the same table concurrently. Migration friction is unusually low for a database of this kind: MySQL 8.4 wire compatibility means your drivers (mysql-connector, JDBC, Go sql-driver, SQLAlchemy, Prisma), your dump files via mysqldump, and your proxy layer via ProxySQL or Canal stay as they are. The AI surface is genuinely kernel-level rather than bolted on — a native VECTOR type with an ART index that lives in the same transaction as the row it describes, embeddings generated in-process, LightGBM and ONNX Runtime in the server so CALL sys.ml_train('churn', ...) and prediction both run from SQL, plus local LLM generation with RAG and a JavaScript agent runtime that reaches SQL through an in-process bridge. Human approval gates every agent write, and as of the 30 Jul 2026 release the approval workflow is durable: plan rows commit independently of the approval wait, so a pending decision no longer holds a transaction open. Weaknesses and where it does not fit. This is v0.1. It is documented only for Linux x86_64, and while the single-container Docker image (docker run -d -p 3306:3306 shannonbase:latest) makes it easy to try, there is no managed cloud option described here. The public playground is explicitly a read-only static preview on a shared 8 vCPU / 32 GB instance at TPC-H SF 0.2, not something to benchmark against or treat as production. The benchmark numbers come from a developer build in debug mode, which is impressive for a debug build but is not the same as a tuned release binary — the vendor says as much by publishing the harness and telling you to run it on your own hardware. SSO and audit-grade controls sit behind the Enterprise tier, and the Personal tier's 3-user cap will push even small teams upward quickly. The connector catalog is essentially MySQL tooling; anyone needing a broad set of prebuilt third-party integrations should look elsewhere. And if you need distributed horizontal scaling across many nodes, this is not that product today. Where it fits. Teams already running MySQL who want analytics, vector search, and lightweight model inference inside the transactional database, and who are comfortable running and patching a database themselves. Best treated as a serious candidate for a proof of concept on real hardware, not a drop-in replacement for a battle-tested production cluster.
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Real-world workflow fit
Concrete scenarios for the personas ShannonBase actually fits — and what changes day-one when you adopt it.
You pull shannondata/shannonbase:0.1-tpch (or shannonbase:latest) in Docker, connect your existing MySQL client on port 3306, then run ALTER TABLE lineitem SECONDARY_ENGINE = RAPID followed by ALTER TABLE lineitem SECONDARY_LOAD to mirror a large table into the column store.
Outcome: Your reporting query is answered by Rapid instead of the InnoDB row store — the published TPC-H SF 0.2 run shows 60.9× on Q11 and 45.0× on Q7 — with no change to your driver, ORM, or dump files.
You add a native VECTOR column beside the row it describes, let embeddings generate in-process, and query with ORDER BY vector_distance(v, ?). For prediction you call sys.ml_train('churn', ...) to train a LightGBM model inside the server and predict from the same SQL connection.
Outcome: Vector search and churn scoring run in the same transaction as your operational data, so you never export rows to a separate vector database or ML pipeline.
You query sys.shannon_chat(?, @out) so the in-kernel JavaScript agent plans work over your tables through the in-process SQL bridge, with every write parked for human approval before it commits.
Outcome: Natural-language tasks complete against live data inside the database, and because the approval workflow is now durable, a pending decision no longer holds a transaction open while it waits.
Use Cases
- Run real-time analytics on live MySQL data without building an ETL pipeline or a warehouse
- Embed machine learning inference directly in SQL queries using LightGBM models trained in the server
- Add vector search and RAG to an existing MySQL application without standing up a separate vector database
- Collapse a separate OLTP database and OLAP warehouse into one HTAP engine
- Keep sensitive embeddings, training data, and LLM calls inside the transactional database
- Give agents natural-language task completion over your own tables with human approval on every write
Models Under the Hood
as of 2026-09-22
Limitations
- ShannonBase is early-stage (v0.1).
- It is wire-compatible with MySQL 8.4 but documented only for Linux x86_64 — other platforms are not listed.
- Deployment is a single Docker container; no managed cloud service is described.
- The public playground is a read-only static preview on a shared instance (TPC-H SF 0.2, 8 vCPU / 32 GB), not a production environment.
- The published benchmark figures come from a developer build in debug mode, so your own numbers on release binaries will differ.
- The agent runtime routes every write through human approval before it commits, which slows unattended automation, though plan rows now commit independently of the approval wait.
- The pricing page uses placeholder-style plan copy — Personal caps at 3 users with "Astro Sub domain" and "Unlimited Pages" listed, and SAML/SSO is reserved for Enterprise — so the commercial tiers are not yet fully described, and the connector catalog is limited to MySQL tooling.
as of 2026-10-03
Verification history
We have re-verified ShannonBase 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published ShannonBase tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Personal
$0/mo
Ideal for
A solo developer or small team of up to 3 people evaluating ShannonBase for HTAP, vector search, or in-database ML before committing budget.
What this tier adds
Free entry point, lifetime free, capped at 3 users with community support.
Startup
$19/mo
Ideal for
A growing MySQL-centric team of up to 20 people running ShannonBase beyond a proof of concept and needing priority support.
What this tier adds
Adds up to 20 users and priority support on top of the free tier's features at $19/mo.
Enterprise
Custom
Ideal for
Organizations that need SAML/SSO, a 99.99% uptime SLA, and a named account contact around their ShannonBase deployment.
What this tier adds
Adds SAML & SSO integration, a 99.99% uptime SLA, a dedicated account manager, and 24/7 phone support at custom pricing.
Where the pricing makes sense
The company stage and team size where ShannonBase's pricing actually pencils out — and where peers do it cheaper.
The free Personal tier (up to 3 users) makes ShannonBase unusually cheap to evaluate, and Startup at $19/mo covers up to 20 users — well below typical managed HTAP or columnar warehouse spend. But that page still carries placeholder plan copy, and the real cost driver is the RAM you allocate to the in-memory column store plus the ops time for a self-hosted v0.1 engine. Compare against your existing MySQL hosting plus whatever you pay for a warehouse and a vector database today.
Setup time & first value
How long it actually takes to get something useful out of ShannonBase — broken out by persona, not the marketing-page minute.
Developer with Docker and an existing MySQL client: roughly a minute to a first routed query — pull the container, connect on port 3306, and run SECONDARY_ENGINE/SECONDARY_LOAD DDL. Teams adding vectors or in-database ML: an afternoon to schema changes and a first model or embedding column. Production hardening on v0.1 — backups, monitoring, GPL v2 compliance, and your own benchmark rerun — is a
Switching to or from ShannonBase
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From plain MySQL 8.x: point your existing client at port 3306 and run ALTER TABLE ... SECONDARY_ENGINE = RAPID; SECONDARY_LOAD to start routing analytical scans to the column store.
- →From MySQL read replicas used for reporting: replace the replica with Rapid and the optimizer routes qualifying queries automatically, removing replication lag from the equation.
- →From a separate OLAP warehouse fed by ETL: load the tables in ShannonBase directly and query them from the same SQL connection the application already uses.
- →From a standalone vector database: move embeddings into a native VECTOR column with an ART index and query with ORDER BY vector_distance(v, ?) inside the same transaction.
- →From an external ML pipeline: train and predict in-server with CALL sys.ml_train('churn', ...) using LightGBM and ONNX Runtime instead of exporting data out.
- ↗To a managed MySQL-compatible cloud service: mysqldump output loads into most MySQL 8.4-compatible targets, but you lose Rapid's column store and the in-database vectors and ML.
- ↗To TiDB: for distributed horizontal scaling, though you would rebuild vector search, model training, and the agent runtime on external services.
- ↗To ClickHouse plus a separate vector store: if analytical throughput matters more than keeping one transactional system, at the cost of ETL and a second and third system to operate.
- ↗To your previous MySQL read-replica reporting setup: restore your clients against the original server and drop the SECONDARY_ENGINE DDL.
Integrations
Resources & Guides
- Documentationshannonbase.org
Docs · ShannonBase
Full product docs from shannonbase.org
- Quickstartshannonbase.org
Getting Started · ShannonBase
Get up and running fast from shannonbase.org
- Documentationshannonbase.org
Htap Rapid · ShannonBase
Full product docs from shannonbase.org
- Documentationshannonbase.org
Vector Ml · ShannonBase
Full product docs from shannonbase.org
- Documentationshannonbase.org
Agent · ShannonBase
Full product docs from shannonbase.org
- API Referenceshannonbase.org
Reference · ShannonBase
Methods, params, types from shannonbase.org
- Documentationshannonbase.org
Contributing · ShannonBase
Full product docs from shannonbase.org
- Resourceshannonbase.org
Changelog · ShannonBase
Helpful link from shannonbase.org
Tutorials & Learning
YouTube returned 6 videos for “ShannonBase”, and we withheld 6: 6 could not be judged, because “ShannonBase” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about ShannonBase.
Official links
Tools that pair well with ShannonBase
Common stack mates teams adopt alongside ShannonBase, with the specific reason each pairing earns its keep.
Tidb
Open-source distributed SQL database unifying transactions, HTAP analytics, and native vector search for AI agents.
Doris
Apache Doris: an open-source real-time SQL database that unifies OLAP analytics, full-text search, and vector search in one engine.
ClickHouse
Open-source columnar OLAP database for millisecond analytics on petabyte-scale data.
Featured Head-to-Head Comparisons
Shannonbase vs Spider Cloud
Choose ShannonBase if you're a MySQL shop that wants a single database for transactions, analytics, and on-database ML/LLM – it cuts stack complexity. Choose Spider Cloud if you need fast, reliable web data for AI agents or RAG – its Rust engine, anti-detection, and AI-powered extraction are purpose-built for that. They solve different problems: data storage vs. data ingestion.
Shannonbase vs Temporal Ai
ShannonBase and Temporal AI solve fundamentally different problems. ShannonBase is for teams that want to run ML/LLM inference directly inside their MySQL-compatible database, while Temporal AI is for orchestrating durable workflows and AI agents across services. Choose ShannonBase if you need a unified transactional+analytical+AI database; pick Temporal if your priority is reliable, fault-tolerant execution of multi-step processes. They are complementary rather than directly competitive.
Shannonbase vs Screenplayiq
ShannonBase and ScreenplayIQ serve completely different markets. Choose ShannonBase if you need a MySQL-compatible HTAP database with built-in machine learning and LLM inference to handle transactional, analytical, and AI workloads in one system. Choose ScreenplayIQ if you're a screenwriter or production executive who wants AI-driven script analysis and box office forecasting to make data-backed creative decisions. There's no overlap; your choice depends entirely on whether your challenge is data infrastructure or script marketability.
Alternatives to ShannonBase
View all
Tidb
Open-source distributed SQL database unifying transactions, HTAP analytics, and native vector search for AI agents.
Doris
Apache Doris: an open-source real-time SQL database that unifies OLAP analytics, full-text search, and vector search in one engine.
ClickHouse
Open-source columnar OLAP database for millisecond analytics on petabyte-scale data.
Frequently Asked Questions
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