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Qdrant AIOtvoren-kodu baza podataka za vektorski za brzi i skalabilni pretragivanje sličnosti i uvjetno preuzimanje AI-a.

4.4 (5)
Daniel NikulshynRecenzirao Daniel Nikulshyn·Ažurirano srpanj 2026.

Pregled

Qdrant je otvoren-kodni vektorski bazni sistem i sustav pretraživanja sličnosti dizajniran za proizvodnu količinu AI obrađivačkih opterećenja. Spriječava zapisivanje visoko-razrednih ugradi uz strukturirane naknadne tereta, omogućavajući primjere kao što su semantički pretraživanje, sustavi prihvaćanja preporuka, dohvata-augmentovane generacije i detekciju anormalnih slučajeva. Izgrađen u Rustu za performanse, Qdrant podržava filtirirani pretraživanje vektora, horizontalni skaliranje i upravljanje u oblaku. Razvijači mogu se međusobno povezati kroz REST i API-ove gRPC, te klijentske biblioteke za Python, JavaScript, Go i Rust. Integrira se s popularnim okvirima za AI kao što su LangChain i LlamaIndex, čineći ga popularnim izborom za ekipe kojegrade LLM-powered aplikacije koje zahtijevaju brzo, pouzdano pohranjivanje na velikom nizu.

Ključne značajke

  • Pronalazak približno najbližih srodnika (HNSW)
  • filtriranje podataka na osnovu sadržaja (payload)
  • vodoravno skaliranje i šaržiranje (sharding)
  • API-ji REST i gRPC
  • Pomnožena usluga Qdrant Cloud (managed cloud)
  • Integracije s LangChain i LlamaIndex

Cijene

Model
Freemium
Kategorija
Razvoj softvera
Ocjena
4.4 / 5 (5)

Slučajevi uporabe

Generacija s uvezdžavanjem uključenjem u RAG tokove za LLM-a

Zadržavajte i upražnjavajte uvezdžavanje za osmišljavanje konteksta za LLM-a, koristeći integraciju s LangChain i LlamaIndex za osmišljavanje tokova RAG-a.

Pretraga po značenju u velikim skupovima podataka

Indikirajte visoke-dimenzionalne uvezdžavanja sa sadržajem za omogućavanje brzih i filtriranog uvjetno pretraživajućih po dokumentima, proizvodima ili medijima na velikim skalama.

Sistemi za preporuke

Koristite približno najbliža srodnika potpora kombinirana s filtrom sadržaja za podizanje personaliziranih preporuka temeljeno na korisnikovim ili predmetnimuvezdžavanjima.

Upozorenja na anomalije kod uvezdžavanja

Identificirajte izdanci unutar visokodimenzionalne datoteke uporedivanje sličnosti uvezdžavanja, podržavajući izdane kao što su uvezdžavanje, sigurnost ili kvalitetu kontrolu radne opterećenjima.

Prednosti i nedostaci

Prednosti

  • Otvoren-kodna licenca sa davanjem dopuštenja (permissive license)
  • Visoka performanse zahvaljujući implementaciji u Rust-u
  • Riži filtriranje kombinirano s pretragom vektora
  • Opcije usluge izvorne (managed cloud) i samohostirane
  • Visoka integracija u ekosustavu

Nedostaci

  • Potrebna je poznavnost vektorskih uvezdžavanja (vector embeddings)
  • Operativne prilike potrebno je prilagoditi na vrlo velikim skalama
  • Manje pogodnosti za poduzeća nego kod nekih komercijalnih rivala (commercial rivals)

Recenzije

4.4

Prosjek iz 5 ocjena.

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Prijavi se za ostavljanje recenzije.

EB

Ethan Brooks

May 14, 2026

Solid for our team

We rolled this out across the team last quarter and high performance due to Rust implementation. REST and gRPC APIs fits neatly into how we already work, and horizontal scaling and sharding removed a step we used to do by hand. but it has held up under daily use.

Esther Adeyemi

Esther Adeyemi

Mar 14, 2026

Use it every day

Honestly didn't expect to like it this much. Payload-based metadata filtering is exactly what I needed, and open-source with a permissive license. I do wish requires familiarity with vector embeddings, but I reach for it almost every day now and it just clicks.

BC

Beatriz Costa

Sep 14, 2025

Solid for our team

We rolled this out across the team last quarter and managed cloud and self-hosted options. Horizontal scaling and sharding fits neatly into how we already work, and horizontal scaling and sharding removed a step we used to do by hand. but it has held up under daily use.

NP

Nadia Petrova

Aug 15, 2025

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on rEST and gRPC APIs, and high performance due to Rust implementation caught me off guard. Fewer enterprise features than some commercial rivals is why this isn't a perfect score, still, I'd recommend giving it a real trial.

Frank Müller

Frank Müller

Jun 29, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is payload-based metadata filtering — handled better than most — and open-source with a permissive license. Fewer enterprise features than some commercial rivals is my one real gripe. Worth the time if this is your use case.

Pitanja

How Is Billing Calculated Month-to-Month?

Billing is calculated based on actual resource usage during the billing period. You're charged for compute (vCPU), memory (GB), storage (GB) consumed by your clusters, storage (GB) consumed by backups, and used inference tokens of paid models. Usage is billed hourly, and you can monitor it through the Qdrant Cloud dashboard.

Asked by Xiomara Delgado · Dec 6, 2025

What's the Difference Between Managed, Hybrid, and Private Cloud?

Managed Cloud is fully managed by Qdrant. Hybrid Cloud lets you bring your own infrastructure while using Qdrant's management plane. Private Cloud gives you complete control with on-premise deployment.

Asked by Wesley Adekunle · Nov 8, 2025

How Do I Choose Between Free Tier and Standard Tier?

The Free Tier is ideal for testing, and prototypes being limited to 1GB RAM and 4GB disk without high availability. Choose the Standard Tier for workloads that require dedicated clusters, higher availability, and advanced features.

Asked by Ingrid Bauer · Oct 28, 2025

What Happens if I Exceed Free Tier Limits?

If your data size growth exceeds the Free Tier limits (1GB RAM and 4GB disk), you can easily upgrade to a Standard Tier and scale up your cluster.

Asked by Kwame Mensah · Sep 28, 2025

How Does Qdrant Cloud Pricing Work?

Qdrant Cloud pricing is based on resource usage, if you have a bigger cluster, you pay for more. The free tier includes 1GB RAM and 4GB disk storage, upgrading will get you a dedicated cluster.

Asked by Farah Rahimi · Sep 28, 2025

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