Spotlight

What if your database could actually grasp what your data means—not just find the right words?

Quick Pitch: Lantern is a Postgres extension that adds vector search to your database—so AI can search by meaning, not just keywords. It’s fast, open-source, and works with the Postgres databases teams already use.

The Problem

• AI Compatibility Gap: AI models need data in vector format—numerical representations that capture meaning—while most enterprise data lives in relational databases.

• Tooling Overhead: Vector databases like Pinecone require a separate database, adding more to manage alongside your existing relational database. Most enterprises want a single, integrated setup for all their data and AI workloads.

• Performance Issues: Tools like pgvector use older indexing methods like IVFflat, which struggle at scale due to slow recall, limited parallelization, and higher maintenance.

• Missed Potential: Without the right foundation, AI apps underperform and are harder to build.

Snapshot

• Industry: AI infrastructure / Databases

• Headquarters: San Francisco, California

• Founded: 2023 

• Traction: Graduated Y Combinator W24 batch with production deployments

Founder Profiles

• Di Qi, CEO, Co-Founder: Previous Y Combinator founder (S20), Former YC's Work at a Startup Engineering Team member, ex-Facebook ML Engineer, CS graduate from Princeton

• Narek Galstyan,CTO, Co-Founder: PhD student from UC Berkeley, Former TimescaleDB Engineer, CS graduate from Princeton

Funding

• Total Funding: Raised (Pre-Seed $500K)

• Lead Investors: Y Combinator, Wayfinder

Revenue Engine

• Developer Adoption: Open-source and built for easy onboarding, with vector search natively integrated into Postgres

• Cloud Offering: Monetized via Lantern Cloud, a managed hosted service

• Pricing Structure: Usage-based, starting free and scaling with storage, queries, and team features

What Users Love

• Finds relevant information 20x faster than standard solutions

• Works with existing relational databases—no replatforming needed

• Scales with growing data without performance loss

• Simple installation with clear documentation for developers

Playing Field

• pgvector (OSS):Popular Postgres extension, but uses outdated IVFflat indexing that struggles at scale. Even hosted versions face performance tradeoffs.

• Pinecone: Fast vector DB, but adds a separate database stack to manage.

• Weaviate / Milvus / Chroma:  Strong standalone tools, but lack support for blending structured + unstructured data.

• TimescaleDB: Optimized for time-series—not vector or AI-native use cases.

• Zilliz / Qdrant / Vespa: Emerging options in the vector DB space, but each introduces its own infrastructure and integration overhead.

Why It Matters

Enterprise AI needs more than better models. It requires infrastructure that helps teams use the data they already have, in a format AI can understand. From surfacing past support cases to spotting patterns in user feedback, every team has data that could power smarter applications.

What Sets Them Apart

• AI in Postgres: Combines structured and unstructured data in one familiar system

• Performance: 5–20x faster search across millions of records

• One Stack: No separate database or complex data movement

• Cloud-Ready: Runs on AWS, Azure, GCP, Neon, and Supabase

• Built for Scale: Modern indexing, hybrid search, and low memory use

Analysis

Bulls Case 📈 

• Postgres is widely adopted—Lantern enhances it rather than replacing it

• Meets rising demand as more companies build AI-powered apps

• Founders bring technical depth and YC startup experience

• Cloud offering simplifies onboarding and scaling

Bears Case 📉 

• Competes with well-funded, specialized vector DBs

• Hard to stand out in a crowded AI infra market

• Lean funding may limit growth

• Open-source core adds pressure to monetize effectively

Verdict

Lantern’s approach—enhancing rather than replacing Postgres—meets a real need for teams building AI features. Its speed advantage directly improves user experience in search-heavy workflows. The founding team brings strong technical and startup experience. Still, success depends on broadening adoption beyond technical users and turning open-source traction into paying customers.

The Startup Pulse

• Supabase raised $200M at a $2B valuation just 7 months after its last round, as demand surges for developer-first backends with AI-ready features like vector search.

• OpenAI's AI Dev Push: OpenAI pursued Cursor before entering $3B talks to buy Windsurf—spotlighting rising M&A momentum in AI developer tools.

• HoneyHive Raises $7.4M: NYC-based AI observability startup closed a $5.5M Seed led by Insight Partners, following a $1.9M Pre-Seed, to help teams monitor and debug AI agents.

Written by Ashher

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