SynapCores SQLv2 vs PostgreSQL: The Evolution of Database Systems

The AI Database Revolution

We built window functions (LAG, LEAD, RANK, etc.) in SynapCores, and it got us thinking about how far we've come from traditional databases like PostgreSQL.

Here's what sets SynapCores apart:

AI-Native from Day One

PostgreSQL + pgvector Approach:

-- Need extensions, custom operators, separate indexing
CREATE EXTENSION vector;
CREATE INDEX ON products USING ivfflat (embedding vector_cosine_ops);
SELECT * FROM products
ORDER BY embedding <-> '[0.1, 0.2, ...]'::vector
LIMIT 10;

SynapCores Approach:

-- Built-in, no extensions needed
SELECT * FROM products
WHERE COSINE_SIMILARITY(embedding, EMBED('wireless headphones')) > 0.7
ORDER BY similarity DESC;

The difference? Native embedding generation and vector search in pure SQL.

Time Series Analysis

PostgreSQL:

-- Complex window functions, manual partitioning
SELECT product_id, date, sales,
       LAG(sales, 1) OVER (PARTITION BY product_id ORDER BY date) as prev_sales,
       LAG(sales, 7) OVER (PARTITION BY product_id ORDER BY date) as week_ago
FROM sales_data;

SynapCores:

-- Same syntax, but with ML-powered forecasting
SELECT product_id, date, sales,
       LAG(sales, 1) OVER (PARTITION BY product_id ORDER BY date) as prev_sales,
       PREDICT(sales, 7) OVER (PARTITION BY product_id ORDER BY date) as forecast
FROM sales_data;

PREDICT() as a window function? Yes. That's the power of unifying SQL and ML.

Semantic Search

PostgreSQL + Full-Text Search:

-- Keyword matching, not semantic understanding
SELECT * FROM documents
WHERE to_tsvector('english', content) @@ to_tsquery('database & performance');

SynapCores:

-- Understands meaning, not just keywords
SELECT * FROM documents
WHERE COSINE_SIMILARITY(
    EMBED(content),
    EMBED('How do I make my database faster?')
) > 0.8;

It knows "make faster" = "performance" and "my database" = "database systems". True semantic understanding.

The Real Difference

PostgreSQL is a phenomenal database. We're not competing with it—we're building for a different era.

PostgreSQL was built for:

• Transactional workloads
• Complex JOINs
• ACID guarantees
• Extensibility

SynapCores was built for:

• All of the above, PLUS
• Native vector operations
• Embedded ML models
• Semantic understanding
• AI-powered analytics

Why This Matters

In 2025, every application needs:

1. Vector search (for RAG, recommendations, similarity)
2. Embeddings (for semantic understanding)
3. Time series (for forecasting, anomaly detection)
4. Traditional SQL (for business logic)

With PostgreSQL, you need:

• pgvector extension
• Separate embedding service (OpenAI API, local models)
• TimescaleDB for time series
• Custom ML pipeline
• Complex orchestration

With SynapCores, you write SQL. That's it.

Real Example: E-commerce Search

PostgreSQL approach:

# 1. Generate embeddings (external service)
embedding = openai.Embedding.create(input="wireless headphones")

# 2. Query with pgvector
results = db.execute("""
    SELECT * FROM products
    ORDER BY embedding <-> %s::vector
    LIMIT 10
""", [embedding])

# 3. Re-rank with business logic
# 4. Filter out-of-stock
# 5. Apply personalization

SynapCores approach:

-- One query, all in SQL
SELECT
    product_name,
    COSINE_SIMILARITY(embedding, EMBED('wireless headphones')) as relevance,
    PREDICT(will_purchase, user_id, product_id) as purchase_probability
FROM products
WHERE in_stock = true
  AND relevance > 0.7
ORDER BY purchase_probability DESC
LIMIT 10;

Embedding generation, vector search, and ML prediction—all in one query.

Performance

"But isn't this slower than PostgreSQL?"

Actually, no. Because:

1. No network round-trips to external embedding services
2. Native vector indexes (HNSW) optimized for similarity search
3. Query optimization understands ML operations
4. Single query plan = better cache utilization

We've seen 3-5x faster than PostgreSQL + pgvector + external embeddings for vector workloads.

The Bottom Line

PostgreSQL revolutionized databases in the 90s and 2000s.

SynapCores is doing the same for the AI era.

It's not about replacing PostgreSQL—it's about giving developers tools built for 2025, not 1996.

Try It Yourself

Here's a real query you can run:

-- Find products similar to what a user searched for
SELECT
    p.product_name,
    p.price,
    COSINE_SIMILARITY(p.embedding, EMBED(:search_query)) as similarity
FROM products p
WHERE similarity > 0.7
  AND p.category IN (
    SELECT category FROM user_preferences WHERE user_id = :user_id
  )
ORDER BY similarity DESC
LIMIT 20;

Try doing that in PostgreSQL without multiple round-trips to external services.

Feature Comparison Table

Document Version: 1.0 Last Updated: December 2025 Website: https://synapcores.com