MongoDB vs PostgreSQL for AI Workloads: Speed, Scalability & Developer Wins Exposed!
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Curious which database dominates AI workloads? We pit MongoDB against PostgreSQL (with PG Vector) in a head-to-head performance showdown for vector search, ingestion speed, and real-time retrieval. Discover why developers are switching for AI scalability!
🔍 What You’ll Learn:
✅ Benchmark Results: Ingestion speed, query latency, and throughput under scale (local machine tests with 100k+ vectors).
✅ Why MongoDB Shines: Out-of-the-box performance for JSON data, zero serialization overhead, and seamless scalability.
✅ PostgreSQL PG Vector Deep Dive: Configuration challenges and when it might still work.
✅ Developer Productivity: Avoid “Postgres Regress” and focus on building AI features faster.
📊 Key Takeaways:
MongoDB handles 4x faster ingestion and 2x lower latency at scale.
Postgres requires tuning (HNSW parameters, JSONB serialization) for AI workloads.
Why latency matters for RAG, conversational AI, and real-time apps in 2024.
⏱ Timestamps:
00:00 - Intro: The AI Database Battle
02:15 - Benchmark Setup (Local Instances, 100k Vectors)
05:40 - Ingestion Speed Showdown: MongoDB vs PG Vector
12:30 - Retrieval Latency: Why Milliseconds Matter for AI
18:50 - Throughput: Queries/Second Under Load
25:00 - Developer Experience: MongoDB’s JSON Advantage
30:45 - When to Choose Postgres? Honest Takeaways
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