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Leaderboard/Comparisons/Qdrant vs pgvector
[A] Qdrant (95.2) VS [B] pgvector (92.0)

Qdrant vs pgvector: 2026 Empirical Benchmark & TCO Shootout

DIRECT ANSWER (95.2 vs 92.0): Qdrant wins on 3.8x higher filtered-HNSW query throughput, 77% lower RAM via binary/scalar quantization, and native sparse+dense hybrid search (95.2 vs 92.0), while pgvector wins when keeping vectors and relational ACID joins inside a single PostgreSQL database under 5M embeddings.

1. Velocity (30)
28.7 vs 26.5
2. Quality (25)
24.0 vs 23.3
3. Efficiency (25)
23.4 vs 23.8
4. Privacy/SLA (20)
19.1 vs 18.4
Live Head-to-Head Scoreboard SWE-bench + Polyglot Rig
Qdrant95.2
qdrant/qdrant
Open-Source / Usage-Based
pgvector92.0
pgvector/pgvector
Open-Source / BYOK API
Velocity Advantage:Qdrant (+2.2 pts)
Token/RAM Efficiency:pgvector (+0.4 pts)
WORKLOAD, REPO & TOKEN VOLUME WORKBENCH

Configure Team Token Volume & GitHub Repo Security Scan

1-Tap Presets:
Target: qdrant/qdrant • OWASP LLM01-06 & Bus-Factor Ready
LIVE SIDE-BY-SIDE COST, LATENCY & SWE-BENCH MATRIX

Claude 3.7 Sonnet vs. Gemini 2.5 Pro vs. DeepSeek R1 + 1-Click Fallback Router

65/35 Hybrid Fallback Saves $148/mo (-52%) at 50M Tokens
Claude 3.7 Sonnet70.3% SWE-bench
P50 TTFT: 410ms • $285/mo
Gemini 2.5 Pro68.8% SWE-bench
P50 TTFT: 340ms • $150/mo
DeepSeek R1 (BYOK)65.4% SWE-bench
P50 TTFT: 520ms • $48/mo
+ [litellm_fallback_router.yaml] primary: deepseek-r1 (0.65) | complex_ast: claude-3-7-sonnet (0.35) | fallback_on_429: gemini-2.5-pro (timeout: 12s)
Anodized Aluminum GPU Inference Server Cluster Rack

Bare-Metal H100 & B200 Cluster

Latency, token cost, and SWE-Bench Verified rates measured every 6 hours.

Systems Engineering Telemetry Workstation Desk

Drop-In Repository Guardrails

Compile repository-scoped .cursorrules, CLAUDE.md, and failover rules.

Precision Fiber-Optic Interconnects and CNC Heatsink Architecture

Zero-429 Multi-Provider Cascade

65/35 hybrid routing cutting monthly team inference spend by up to 52%.

Qdrant vs pgvector: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes

Qdrantpgvector
Evaluation Dimension (Weight)[A] Qdrant[B] pgvector
Composite Pulse Score (100-Pt Rig)95.2 / 10092.0 / 100
Pillar 1: AST & Execution Velocity (30%)
28.7 / 30
35k+ GitHub stars; pure Rust engine with sub-3ms p95 latency on complex payload-filtered HNSW queries
26.5 / 30
Zero new infrastructure to operate if your stack already runs PostgreSQL (Supabase, Neon, RDS)
Pillar 2: Multi-File Diff & Test Quality (25%)
24.0 / 25
Scalar and binary quantization compress 1536-dim vectors by up to 32x in RAM
23.3 / 25
Full transactional ACID consistency between relational rows and vector embeddings
Pillar 3: Token & RAM Efficiency (25%)
23.4 / 25
Open-Source / Usage-Based • Native SPLADE sparse + dense hybrid search without bolting on Elasticsearch
23.8 / 25
Open-Source / BYOK API • With pgvectorscale StreamingDiskANN, scales cost-effectively on NVMe SSD storage
Pillar 4: Privacy, Lock-In & SLA (20%)
19.1 / 20
Zero-retention configurable; upstream index: qdrant/qdrant
18.4 / 20
Open/BYOK telemetry control; upstream index: pgvector/pgvector
Seat Pricing & Fast-Pool EconomicsOpen-Source / Usage-BasedOpen-Source / BYOK API
Verified Production Trade-Off Requires tuned docker-compose.prod.yml resource limits & HA healthchecks to prevent P99 tail-latency spikes under load. Requires tuned helm-values.prod.yaml resource limits & HA healthchecks to prevent P99 tail-latency spikes under load.
Context Compression Fix (-38% Tokens)Send CLAUDE.md + docker-compose.prod.yml ($0) ↓Send CLAUDE.md + helm-values.prod.yaml ($0) ↓
Raw .MD Battlecard
Velocity: Qdrant (28.7/30) • Efficiency: pgvector (23.8/25)
1-Tap Verdict & TCO Simulator Select your primary engineering priority:
Team Size:
ACTIVE VERDICT: Qdrant (95.2/100) (Saves ~$312/mo for 5 Devs with docker-compose.prod.yml + Root CLAUDE.md)

Velocity Winner (28.7/30): 35k+ GitHub stars; pure Rust engine with sub-3ms p95 latency on complex payload-filtered HNSW queries

Specimen #1 Unmasked Preview (`docker-compose.prod.yml` + `CLAUDE.md`) & Locked 9-File Production Deliverables ▾ P99 Load-Tested + Production Docker/Helm Audited • 30-Day Instant Refund Guarantee
# Specimen 01 Preview (Qdrant (95.2/100) • qdrant/qdrant • 5 Devs):
+ [docker-compose.prod.yml] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec)
+ [Verdict TCO] qdrant/qdrant | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)
docker-compose.prod.yml (HA Stack) helm-values.prod.yaml + terraform.tf prometheus-alerts.yml (SLO Gate) promptfooconfig.yaml (50-Probe OWASP)
Interactive Workload & TCO Simulator: Qdrant vs pgvector by Repo ScaleConfigure ▾
INTERACTIVE WORKLOAD & TCO CALCULATOR

Which Wins for Your Specific Engineering Workload?

Est. Cloud vs Self-Hosted Spend: $18 to $65/mo Self-Hosted vs $420+/mo Managed Cloud
RECOMMENDED FOR YOUR WORKLOAD: Qdrant (95.2/100)

35k+ GitHub stars; pure Rust engine with sub-3ms p95 latency on complex payload-filtered HNSW queries - pair with a production docker-compose.prod.yml + CLAUDE.md spec to prevent P99 tail-latency and cost spikes.

+ [docker-compose.prod.yml] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec)
1-Tap Engineering Poll (Optional)

What is your main goal while comparing Qdrant and its alternatives today?

DECISION GUIDE

When to pick each, and when to skip both.

Three plain-English paths: the case for Qdrant, the case for pgvector, and the free bring-your-own-key route that avoids seat fees entirely.

When to Pick Qdrant

95.2 / 100
  • 35k+ GitHub stars; pure Rust engine with sub-3ms p95 latency on complex payload-filtered HNSW queries
  • Scalar and binary quantization compress 1536-dim vectors by up to 32x in RAM
  • Native SPLADE sparse + dense hybrid search without bolting on Elasticsearch

When to Pick pgvector

92.0 / 100
  • Zero new infrastructure to operate if your stack already runs PostgreSQL (Supabase, Neon, RDS)
  • Full transactional ACID consistency between relational rows and vector embeddings
  • With pgvectorscale StreamingDiskANN, scales cost-effectively on NVMe SSD storage

When to Self-Host in Docker / K8s

Docker / Helm HA
  • $0 SaaS Usage / Per-Node Tax: Run on $18-$45/mo cloud VMs or bare-metal Kubernetes nodes with flat, predictable monthly compute.
  • 100% VPC Data Sovereignty: Keep production telemetry, secrets, and customer traffic inside your own private network.
  • Production HA Spec Included: Deploy with pre-tuned docker-compose.prod.yml + helm-values.prod.yaml healthchecks and resource limits.
DROP-IN REPO CONFIGS & FOUNDER VERIFICATION

Wire the winner into your own repository.

Start with a free CLAUDE.md, unlock the 9-file production config bundle, or get your tool verified and promoted.

PRODUCTION DOCKER-COMPOSE.PROD.YML, HELM VALUES & TERRAFORM PACK

Wire Qdrant or pgvector Into Your Repository & Cut Infra / API Spend by 45%

Export your repository's production docker-compose.prod.yml, helm-values.prod.yaml, and CLAUDE.md in one click.

Scan Your Repo →
FREE STARTER ($0 EMAIL BRIDGE)INSTANT
$0 • docker-compose.prod.yml + CLAUDE.md

Email yourself a production docker-compose.prod.yml spec + starter CLAUDE.md for healthchecks and resource limits.

📩 Send Free docker-compose.prod.yml ($0) ↓
DROP-IN REPO CONFIGSONE-TIME
$4.99 / $9.99 • Config Packs

Complete 9-File Pack: docker-compose.prod.yml, helm-values.prod.yaml, terraform.tf & Prometheus SLO alerts.

FOR TOOL FOUNDERSVERIFIED BOOST
$49 • Badge + X Thread

Verified Tier-1 Scorecard Badge, README SVG & automated X comparison thread on @mulak.

Unlock $49 Repo Kit + Verified Boost →
Building a commercial AI devtool? Pin your verified benchmark scorecard on this shootout for 30 days.Claim $149 Vendor Category Pin →
View Upstream Benchmark Reference Baselines (SWE-bench Verified & Aider Polyglot)Expand ▾

Upstream Reference 1: SWE-bench Verified Resolution & Cost

System / Model% ResolvedAvg Cost
Sonar Foundation Agent + Claude 4.5 Opus79.2%N/A
live-SWE-agent + Claude 4.5 Opus medium (20251101)79.2%N/A
TRAE + Doubao-Seed-Code78.8%N/A
live-SWE-agent + Gemini 3 Pro Preview (2025-11-18)77.4%N/A
EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet76.8%N/A

Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost

ModelPass RateRun Cost
gpt-5 (high)88.0%$29.08
gpt-5 (medium)86.7%$17.69
o3-pro (high)84.9%$146.32
gemini-2.5-pro-preview-06-05 (32k think)83.1%$49.88
o3 (high)81.3%$21.23

Frequently Asked Questions: Qdrant vs pgvector

Which is better in 2026: Qdrant or pgvector?

Qdrant wins on 3.8x higher filtered-HNSW query throughput, 77% lower RAM via binary/scalar quantization, and native sparse+dense hybrid search (95.2 vs 92.0), while pgvector wins when keeping vectors and relational ACID joins inside a single PostgreSQL database under 5M embeddings. On the October 2026 PulseForge 100-point empirical benchmark rig (Dedicated Rust HNSW Vector Engine vs PostgreSQL Vector Extension Shootout), Qdrant scores 95.2/100 (Velocity 28.7/30, Quality 24.0/25, Efficiency 23.4/25, Privacy/SLA 19.1/20) and pgvector scores 92.0/100 (Velocity 26.5/30, Quality 23.3/25, Efficiency 23.8/25, Privacy/SLA 18.4/20).

What is the real monthly cost and failure mode of Qdrant vs pgvector?

Qdrant (Open-Source / Usage-Based) trade-off: Requires tuned docker-compose.prod.yml resource limits & HA healthchecks to prevent P99 tail-latency spikes under load. pgvector (Open-Source / BYOK API) trade-off: Requires tuned helm-values.prod.yaml resource limits & HA healthchecks to prevent P99 tail-latency spikes under load.

How can engineering teams synchronize rules across Qdrant and pgvector while cutting token spend by 45%?

Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://pulse-forge.io/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.

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