Redis Cloud vs Qdrant
A comparison of Redis Cloud and Qdrant built from values read directly from each provider's own documentation, with the source recorded against every figure.
The short answer
- Pick Redis Cloud if
- Teams that want Redis from the company that maintains it, with an on-demand hourly Essentials tier cheap enough to run a small production workload without committing to a fixed plan.
- Pick Qdrant if
- Teams that want a choice of similarity metric rather than one fixed default, or that want the option to self-host the same open-source engine the managed cloud runs.
3 sourced criteria separate them:
- Pricing model
- Redis Cloud: On-demand hourly billing. Data transfer is included in the price, with limits.
- Qdrant: Usage-based on the Standard tier: billed for compute (vCPU), memory (GB) and storage (GB) consumed by clusters, backup storage, and inference tokens for paid models.
- Similarity metrics supported
- Redis Cloud: Cosine similarity is documented as a supported distance metric in Redis's vector field schema (DISTANCE_METRIC: COSINE in example code); other metrics were not confirmed in what is sourced here.
- Qdrant: Dot product, Cosine similarity (implemented as dot-product over automatically normalized vectors), Euclidean distance, and Manhattan distance.
- Metadata filtering
- Redis Cloud: Supported — a vector KNN query can be combined with a pre-filter on other indexed fields in the same query.
- Qdrant: Supported — collections store points (vectors with a payload) and support dedicated filtering on payload fields.
Pick the criteria you care about. The chart counts how many of them lean toward each provider — the same read as scanning the bars below, just totalled for the ones you chose.
Redis Cloud 0
Qdrant 0
At a glance.
| Criterion | Redis Cloud | Qdrant |
|---|---|---|
| Pricing | ||
| Free tier | Yes | Yes |
| Pricing model | On-demand hourly billing. Data transfer is included in the price, with limits. | Usage-based on the Standard tier: billed for compute (vCPU), memory (GB) and storage (GB) consumed by clusters, backup storage, and inference tokens for paid models. |
| Vector | ||
| Similarity metrics supported | Cosine similarity is documented as a supported distance metric in Redis's vector field schema (DISTANCE_METRIC: COSINE in example code); other metrics were not confirmed in what is sourced here. | Dot product, Cosine similarity (implemented as dot-product over automatically normalized vectors), Euclidean distance, and Manhattan distance. |
| Metadata filtering | Supported — a vector KNN query can be combined with a pre-filter on other indexed fields in the same query. | Supported — collections store points (vectors with a payload) and support dedicated filtering on payload fields. |
Where they differ.
Pricing model
- Redis Cloud
- On-demand hourly billing. Data transfer is included in the price, with limits.
- Qdrant
- Usage-based on the Standard tier: billed for compute (vCPU), memory (GB) and storage (GB) consumed by clusters, backup storage, and inference tokens for paid models.
Sources (2) →Sources ↓
- Redis Cloud Pricing ↗
“On demand. Data Transfer: Included in price, limited.”
Read 2026-09-11 · official pricing
- Qdrant Cloud Pricing ↗
“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.”
Read 2026-09-11 · official pricing
Similarity metrics supported
- Redis Cloud
- Cosine similarity is documented as a supported distance metric in Redis's vector field schema (DISTANCE_METRIC: COSINE in example code); other metrics were not confirmed in what is sourced here.
- Qdrant
- Dot product, Cosine similarity (implemented as dot-product over automatically normalized vectors), Euclidean distance, and Manhattan distance.
Sources (2) →Sources ↓
- Redis vector search documentation ↗
“ALGORITHM: SCHEMA_VECTOR_FIELD_ALGORITHM.FLAT, DIM: 384, DISTANCE_METRIC: 'COSINE'”
Read 2026-09-11 · official docs
- Qdrant documentation ↗
“Dot product (Dot), Cosine similarity (Cosine), Euclidean distance (Euclid), Manhattan distance (Manhattan). Cosine similarity is implemented as dot-product over normalized vectors. Vectors are automatically normalized during upload.”
Read 2026-09-11 · official docs
Metadata filtering
- Redis Cloud
- Supported — a vector KNN query can be combined with a pre-filter on other indexed fields in the same query.
- Qdrant
- Supported — collections store points (vectors with a payload) and support dedicated filtering on payload fields.
Sources (2) →Sources ↓
- Redis vector search documentation ↗
“(@brand:trek)=>[KNN 50 @vector $query_vector]=>{$SHARD_K_RATIO: 0.4; $YIELD_DISTANCE_AS: similarity}”
Read 2026-09-11 · official docs
- Qdrant documentation ↗
“Points, and vectors with a payload. [...] filtering is available as a search feature.”
Read 2026-09-11 · official docs
When these numbers change, hear about it. Sources are re-checked monthly; a repricing goes out as a short note.
Which should you choose?
Choose Redis Cloud if…
Teams that want Redis from the company that maintains it, with an on-demand hourly Essentials tier cheap enough to run a small production workload without committing to a fixed plan.
Editorial · Palash Bagchi · approved
Choose Qdrant if…
Teams that want a choice of similarity metric rather than one fixed default, or that want the option to self-host the same open-source engine the managed cloud runs.
Editorial · Palash Bagchi · approved
Consider something else if…
- Redis Cloud: Multi-region active-active replication is needed on a small budget — that's a Pro-tier feature here, where a serverless competitor may price per-region replication into a lower-cost plan.
- Qdrant: A published, calculator-free entry price matters — Qdrant Cloud's Standard tier pricing is usage-based with no static per-month figure quoted, unlike some competitors' flat entry plans.
Documented by only one.
These criteria are published by one provider and not the other. An absence here means we have not found a source, not that the feature is missing.
Questions this comparison answers.
Should I choose Redis Cloud or Qdrant?
Pick Redis Cloud if Teams that want Redis from the company that maintains it, with an on-demand hourly Essentials tier cheap enough to run a small production workload without committing to a fixed plan.
Pick Qdrant if Teams that want a choice of similarity metric rather than one fixed default, or that want the option to self-host the same open-source engine the managed cloud runs.