Qdrant vs pgvector
Both are alternatives to Pinecone. Here's how they stack up — verified facts, no spin.
Also searched as pgvector vs Qdrant — same comparison, one verdict.
Qdrant and pgvector are closely matched on ownership (92 vs 94) — this one comes down to pricing and to which trade-offs below you can live with.
Qdrant
TOP PICKFast, open-source, one binary — the default self-hosted vector DB.
Qdrant is a high-performance vector database written in Rust and licensed Apache-2.0 — free to self-host with no usage limits or feature gates. It ships as a single self-contained binary with REST and gRPC APIs, runs via Docker or Kubernetes (official Helm chart and Operator), and offers a managed Qdrant Cloud on AWS, GCP, and Azure when you'd rather not operate it. Strong filtering, quantization, and hybrid search make it the most common 'we left Pinecone' landing spot.
pgvector
Vector search inside the Postgres you already run — zero new infrastructure.
pgvector is an open-source extension (PostgreSQL license) that adds vector similarity search to PostgreSQL itself. If your app already has Postgres, this is the no-new-moving-parts answer: embeddings live next to your relational data, joins and filters are just SQL, and every major managed Postgres (RDS, Cloud SQL, Azure, Supabase, Neon) supports it. Recent releases added parallel HNSW index builds, iterative scans for filtered queries, and halfvec quantization — it now handles serious workloads, not just prototypes.
Side by side
10 points of comparison, every one read from a verified field. Green marks the side that wins a row outright. A dash means we do not hold that fact — never that it is zero.
| Qdrant | pgvector | |
|---|---|---|
| Sovereignty ScoreOur transparent 0–100 composite for data ownership and exit cost. | 92 | 94 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first data | Yes | Yes |
| License | Apache-2.0 | PostgreSQL |
| Pricing | Free self-hosted (no limits); Qdrant Cloud managed tiers with a free 1GB cluster | Free — an extension of PostgreSQL; runs wherever your Postgres runs |
| RAM to run it wellThe figure that actually matters, not the vendor's minimum. | Vectors × dimensions × 4 bytes, in RAM, plus overhead — 1M × 768d is roughly 3 GB | 8 GB — the HNSW index wants to be resident |
| Realistic running costWhat the box costs each month if you run it yourself. | $25–60/mo for a few million vectors, against Pinecone's $50/mo floor plus usage | $0 additional if you already run Postgres. That is the entire argument. |
| Setup timeHonest first-install estimate, not the marketing quickstart. | 30 minutes | 20 minutes — `CREATE EXTENSION vector;` |
| Ongoing maintenanceThe part nobody budgets for. | Low. Snapshots for backup, and watch memory as the collection grows. | None beyond the Postgres you already maintain. |
Qdrant is Macrostack's recommended Pinecone alternative, so it's our pick here.
Weighing both against staying on Pinecone? Is Pinecone free? What it actually costs →
Qdrant
Strengths
- +Apache-2.0, no feature gates — the full engine is open
- +Single Rust binary: laptop to cluster with the same API
- +Excellent metadata filtering, quantization, hybrid search
- +Managed cloud exists when you want zero ops
Trade-offs
- −Self-hosting means you own scaling and backups
- −Smaller managed-service ecosystem than Pinecone's
pgvector
Strengths
- +No new database to operate — it's your existing Postgres
- +Vectors join directly with relational data in SQL
- +Supported by every major managed Postgres provider
- +HNSW indexes, filtered iterative scans, quantization
Trade-offs
- −Very large collections (hundreds of millions of vectors) favor a dedicated engine
- −Tuning happens in Postgres terms — indexes, memory, vacuum
Which one fits you
The trade-offs above, turned into a decision. Find the line that describes your team.
Choose Qdrant
if apache-2.0, no feature gates — the full engine is open.
Choose pgvector
if a lower exit cost matters more to you than any single feature, and no new database to operate — it's your existing Postgres.
Neither, yet
if both carry a real cost you should weigh first — self-hosting means you own scaling and backups, and very large collections (hundreds of millions of vectors) favor a dedicated engine. If either of those is a dealbreaker for your team, the shortlist is wrong rather than the choice.
What it takes to run these yourself
Real requirements and honest running costs, not the vendor quickstart.
Self-hosting Qdrant
Vectors × dimensions × 4 bytes, in RAM, plus overhead — 1M × 768d is roughly 3 GB RAM · 30 minutes
$25–60/mo for a few million vectors, against Pinecone's $50/mo floor plus usage
Self-hosting pgvector
8 GB — the HNSW index wants to be resident RAM · 20 minutes — `CREATE EXTENSION vector;`
$0 additional if you already run Postgres. That is the entire argument.
Qdrant vs pgvector — common questions
Is Qdrant a better fit than pgvector for vector databases & ai search?
It depends on what you are optimising for, and the honest split is this: pgvector scores 94 to Qdrant's 92 on data ownership and exit cost, so it is the safer choice if you care about being able to leave. Qdrant earns its place on a different axis — apache-2.0, no feature gates — the full engine is open. Neither is a wrong answer for every team; the table above is the actual comparison.
What happens if we want to switch later?
Qdrant keeps its data local or in open formats, so leaving is an export rather than a negotiation. pgvector is still self-hostable, so the files stay on your server either way — but it is not local-first by design, so check what its export produces before you rely on it.
Can I self-host Qdrant or pgvector?
Both can be self-hosted. The difference is what it costs you in time rather than whether it is possible — see the setup and maintenance rows above.
Are Qdrant and pgvector both alternatives to Pinecone?
Yes — both appear in our Pinecone comparison, which is why they are worth putting side by side. People usually arrive here already having decided to move off Pinecone and now choosing between the two replacements, which is a narrower and much easier question.
Related alternative guides
Facts verified 2026-07-15. Licenses and pricing change — spotted something out of date? That's a correction we want.