The Snowflake Semantic Views alternative where the agent never writes SQL
SynaLemma answers questions on your business data with rules that are proved before they run, then compiled to SQL. The agent never writes SQL, and the same question always gives the same SQL.
SynaLemma vs Snowflake Semantic Views: key differences
-
The agent never writes SQL
The agent writes rules, not SQL. The engine checks every rule, then writes SQL that only reads your tables: no raw SQL and no injection, formally proved.
-
Every rule proved before it runs
A symbolic verifier proves each rule well-formed, safe and terminating. An invalid rule is rejected with the reason, before any SQL runs.
-
Graph questions on your SQL databases
Hierarchies, bills of materials and supply chains are recursive rules, verified to terminate and compiled to SQL on PostgreSQL, BigQuery, Databricks, Trino, Presto, DuckDB and SQLite. No graph database, no migration.
-
Assertions that name the faulty rows
Keys, references, totals or transitivity are written in first-order logic. The engine searches the whole table and returns the rows that break them.
-
Same question, same SQL
The same question always compiles to the same SQL, and every figure cites the rule it came from. Your team can read, correct or lock that rule.
-
Sovereign by construction, not by contract
Data, rules and model stay on your infrastructure. The engine compiles the SQL, so a small open model is enough, with no per-token fees. The Synalog engine is open source, under Apache-2.0.
Example: a recursive query with assertions
A bill of materials at any depth, with two assertions. The data contains a cycle; the engine reports it before any answer is returned.
# The bill of materials, read from the database.
Component(assembly:, part:, quantity:) :- bom(assembly:, part:, quantity:);
# A part is inside an assembly directly, or inside one of its parts.
@Recursive(Contains, 20);
@Assert(Contains,
transitive: "∀ a b c, Contains a b → Contains b c → Contains a c",
no_cycle: "∀ a, ¬ Contains a a");
Contains(assembly:, part:) distinct :- Component(assembly:, part:);
Contains(assembly:, part:) distinct :-
Contains(assembly:, part: sub),
Component(assembly: sub, part:);SynaLemma vs Snowflake Semantic Views at a glance
| Criterion | SynaLemma | Snowflake Semantic Views |
|---|---|---|
| How an agent answers | It writes a rule; the engine proves it and compiles the SQL | Cortex Analyst, a language model, writes SQL for each question |
| What checks the definitions | Every rule proved well-formed, safe and terminating before it runs | Structural validation rules: keys, relationships, expressions |
| Checks on the data | Assertions in first-order logic that return the rows breaking them | None on semantic views: verified queries guide the model and score its SQL |
| Hierarchies and multi-hop chains | Native recursion at any depth, verified to terminate | Rejected: no self-references, no circular relationships |
| Definitions written in | Synalog, an open-source logic language with a compiler | SQL DDL (CREATE SEMANTIC VIEW) or YAML |
| Joins | Any join the rule states, as far as the question needs | Many-to-one, one-to-one and range joins; many-to-many needs a bridge table |
| Language model | The one you choose: a small open model is enough, no per-token fees | Snowflake-hosted models, possibly in another region |
| Deployment | On-premises, on your infrastructure | Snowflake's cloud only |
Why teams look for a Snowflake Semantic Views alternative
-
SQL written by a model
Cortex Analyst generates SQL from each question; for Cortex Agents, Snowflake states that the accuracy of responses is not guaranteed (Cortex Agents).
-
No parent-child hierarchies
A semantic view rejects circular relationships, even through transitive paths, and a table cannot reference itself (validation rules).
-
Structure checks only
Semantic view validation covers the shape of the model. Verified queries are examples for the model, not checks on the data (Cortex Analyst).
-
Cloud only
Semantic views and Cortex run in Snowflake's cloud; depending on an account setting, prompts can be processed in another region (cross-region inference).
Snowflake Semantic Views alternative: FAQ
What is the best Snowflake Semantic Views alternative for AI agents?
SynaLemma. The agent writes rules instead of SQL, every rule is proved before it runs, assertions name the faulty rows, and parent-child hierarchies are native recursive rules. It runs on your infrastructure with a small open model.
Can a Snowflake semantic view model a parent-child hierarchy?
No. Self-references and circular relationships are rejected, so org charts and bills of materials need hand-written recursive SQL. In Synalog, recursion is native and verified to terminate.
Are verified queries the same as assertions?
No. Verified queries are example questions with their SQL, used to guide and score the model. Assertions check a property of every result and return the rows that break it.
Can SynaLemma run with a local model?
Yes. A small open model on your own hardware is enough, because the engine compiles the SQL.
Sources
- Snowflake docs: CREATE SEMANTIC VIEW
- Snowflake docs: semantic views overview
- Snowflake docs: validation rules
- Snowflake docs: modeling best practices
- Snowflake docs: Cortex Analyst
- Snowflake docs: Cortex Agents
- Snowflake docs: cross-region inference
- Synalog on GitHub (Apache-2.0)
Checked against each product's public documentation on the review date. To report an error: contact@synalinks.com.
Other comparisons
Bring a question your current stack can't answer.
In 30 minutes, we show SynaLemma answering it, with the rule behind the number and the checks it passed.