Strategy — AI Data Stack

Where **AI Data Stack** is heading over the next 12 months, grounded in product-axis evidence and verbatim demand from the last 90 days. The judgment column is the engine's read — operators verify and refine.

Product trajectories

Data Labeling Platform for AI / RLAIF — data labeling + preference data pipeline ↗ rising

**Opportunity:** Meta's $14.3B into Scale AI confirms data labeling / preference data is now strategic infra at the frontier-lab tier. Community is asking concrete operational questions ('audit-ready preference data', 'top tools for RLAIF'), suggesting the next layer below Scale (auditable / agent-loop / synthetic alternatives) is open.

Hottest single capital signal in the bucket — Meta-Scale deal validates labeled data as the bottleneck for frontier models. Scale is the incumbent; opportunity sits in auditability, RLAIF/agent-loop labeling, and synthetic data alternatives that the community is openly probing.

_Demand signal:_ What does "audit-ready preference data" actually look like for RLHF distillation pipelines?

Vector Database — vector database · weak signal

**Opportunity:** Massive 'how-to / which one / is it dying' query volume around vector DBs — indicates a confused but high-intent buyer market. Hyperscaler entry (S3 Vectors) is reframing the category from standalone DB to commodity feature; 1B-scale indexing and cost remain unresolved pain points.

Vector DB is the most discussed primitive in the ai-data-stack right now, but the conversation has shifted from 'what is it' to 'is it still a category' after AWS S3 Vectors launch. Hot but contested — incumbents (Pinecone/Weaviate/Milvus class) face commoditization pressure; in-process / lightweight forks (Zvec, LEANN, FerresDB) are emerging as the differentiated wedge.

_Demand signal:_ Will Amazon S3 Vectors kill vector databases or save them?

RAG Infrastructure — retrieval-augmented generation pipeline · weak signal

**Opportunity:** Production RAG cost ($2.4K/mo / 73% reduction story) and operational burden ('mostly infrastructure maintenance') are repeatedly surfacing — gap for managed RAG-ops platforms. Also a trust/leakage concern (LlamaIndex silent OpenAI fallback) signaling need for verifiable local/private RAG.

RAG is past hype peak into 'painful production' phase — the demand signal has shifted from build-it to operate-it/cut-cost-of-it. Big opportunity for RAG-ops, cost optimization, and verifiable-local stacks; existing tooling (LlamaIndex) is being audited.

_Demand signal:_ Production RAG is mostly infrastructure maintenance. Nobody talks about that.

Ontology + Graph Database Layer for LLMs — knowledge graph / ontology layer · weak signal

**Opportunity:** Reaction against vector-only retrieval — demand for structured facts/ontologies layered on top of LLMs and vector search. Likely opportunity for graph+vector hybrid retrieval and ontology-as-a-service.

Emerging counter-trend to 'vector DB solves everything'. Still discussion-stage in this dataset, no committed companies visible, but recurring pairing of graph vs vector suggests hybrid retrieval is the next architectural debate in ai-data-stack.

_Demand signal:_ Why AI Needs Facts: The Case for Layering Ontologies onto LLMs, Graph Databases, and Vector Search

Raw demand (last 90d)

What the field is actually asking for, verbatim:

• > Will Amazon S3 Vectors kill vector databases or save them?
• > Vector database that can index 1B vectors in 48M
• > The Vector Database Hype is Over (and That's Good)
• > Why Your RAG Costs $2,400/Month (and How We Cut It by 73%)
• > Show HN: I hate paying for GPUs while developing – this is how I solved it
• > GPT-4o is already AGI – We are just looking at a "lobotomized" version for profit reasons

_See [Products](./products) for the full landscape and [Hiring](./jobs) for who's investing in which direction._