● Live AI Engine

astraiv_ai_engine.py

01

{ } telemetry.json

200 OK

{

"network_status": "operational",

"active_clusters": 128,

"latency_p99": "1.24ms",

"uptime_sla": "99.999%"

}

cluster_stream.log

STDOUT

[200 OK]POST /v1/ai/stream -> 0.9ms

[METRIC]p99: 0.82ms | edge healthy

[DEPLOY]Zero-downtime mesh active

</> enterprise_agent.ts

v2.4.0

export interface EnterpriseAgent {

id: UUID;

concurrency: "ultra-high";

execute(task: Task): Promise<Result>;

}

● Live Server Action

server_action.ts

01

● Live Rust Engine

engine.rs

01

</> k8s_mesh.yamlprod-edge

kind: CloudClusterMesh

spec:

regions: ["iad1", "fra1"]

autoscale: { max: 64 }

zeroDowntime: true

</> semantic_rag.sqlpgvector

SELECT doc_id, tenant_id,

1 - (embedding <=> $query) AS score

FROM astraiv_vault ORDER BY score DESC LIMIT 5;

● Astraiv Hub

astraiv_platform.tsLIVE

01

k8s.cluster.healthy

λdistributed-edge: 0.8ms

Next.js 15 // Server Actions

TLS 1.3 // AES-GCM-256

Prisma 6 // Postgres Pool

0x7F4B92C...SYN/ACK

ASTRAIVTECHNOLOGIES

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Insights/Engineering Blog

Technical Publications & Systems Analysis

Astraiv Engineering Blog

In-depth architectural breakdowns, production engineering tutorials, and distributed systems case studies written by senior practitioners.

Showing 1 of 7 published publications

Evaluating pgvector vs Dedicated Vector Databases for Production AI

Technology

7 min read

Jul 2, 2026

Evaluating pgvector vs Dedicated Vector Databases for Production AI

A hard architectural comparison between PostgreSQL pgvector (HNSW) and standalone vector-only stores like Pinecone or Qdrant across latency, operational complexity, and ACID consistency.

Sarah Mitchell

Head of Cloud & Distributed Systems

SolutionService

Astraiv Engineering Lab

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