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Redis alternatives

19 tools we tested head to head against Redis, ranked — and what each one actually does differently.

last reviewed 23 jul 2026 · from our best 20 vector databases ·list curated by Onur Ozcanxin

first — what you'd be leaving

Redis ranks #17 of 20 in our vector databases testing. the lowest latency here, and the worst economics at scale.

60/100

unbeatable latency when the data fits in memory, and in-memory is the whole problem — plus a licence history you need to check before deploying.

why people look for an alternative
  • in-memory only — the most expensive per vector at scale
  • pre-redis-8 search module is source-available only
  • 30mb free tier is not a real evaluation

stay with Redis if lowest latency in the category when data fits in ram is the thing you care about most — nothing below beats it on that.

the short version
best alternativepgvectoralmost everyone — start here and migrate on a measured threshold, not a vendor's table92/100
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  1. 1

    pgvector

    #1 in vector databases · the correct default for most teams, and it quietly got good

    92/100

    verdictfree, permissive, runs on every managed postgres, and the only option here that can update an embedding and its parent row in one transaction.

    pgvector vs Redis
     Redispgvector
    pricefree up to 30MB; essentials from $0.007 / hourfree — PostgreSQL License
    free tieryesyes
    licenseRSALv2 / SSPLv1 / AGPLv3 from Redis 8PostgreSQL License — permissive
    deploymentself-host or redis cloudextension on any postgres
    index typeshnsw, flathnsw, ivfflat, binary quantization
    hybrid searchvia the search modulevia postgres full-text; you write the fusion
    pricing modelhourly by memory tierfree — you pay for postgres

    switch foralmost everyone — start here and migrate on a measured threshold, not a vendor's table

    pros
    • +free and permissively licensed, on every managed postgres
    • +transactional consistency between embeddings and business rows
    • +0.8.0 iterative index scans fixed the filtered-recall flaw
    • +half-precision and binary quantization cut footprint dramatically
    cons
    • index builds are slow and lock-heavy at large scale
    • no native distributed sharding
    • you own all the tuning yourself
  2. 2

    Qdrant

    #2 in vector databases · filtering built into the graph traversal, not bolted around it

    90/100

    verdictthe best dedicated option: apache-2.0 with no strings, the strongest filtered-search design in the category, and the easiest serious self-host.

    Qdrant vs Redis
     RedisQdrant
    pricefree up to 30MB; essentials from $0.007 / hourfree tier; cloud billed hourly on compute and storage
    free tieryesyes
    licenseRSALv2 / SSPLv1 / AGPLv3 from Redis 8Apache-2.0
    deploymentself-host or redis cloudself-host or managed cloud
    index typeshnsw, flathnsw + scalar/binary/product/turboquant
    hybrid searchvia the search modulesparse vectors, native
    pricing modelhourly by memory tierhourly compute + storage, rates unpublished

    switch forteams who genuinely need a dedicated vector database and want to self-host it

    pros
    • +filters applied inside hnsw traversal — best filtered search here
    • +apache-2.0 with no revenue caps or service restrictions
    • +four quantization modes including turboquant
    • +single rust binary — easiest serious self-host
    cons
    • cloud per-unit rates are not published
    • hybrid and private cloud are quote-only
    • smaller enterprise support footprint than the incumbents
  3. 3

    Milvus / Zilliz Cloud

    #3 in vector databases · the credible answer at a billion vectors

    88/100

    verdictthe widest index selection anywhere and genuine billion-scale — and version 2.6 removed the external message queue that made it painful to run.

    Milvus / Zilliz Cloud vs Redis
     RedisMilvus / Zilliz Cloud
    pricefree up to 30MB; essentials from $0.007 / hourfree self-hosted; zilliz cloud billed per compute unit
    free tieryesyes
    licenseRSALv2 / SSPLv1 / AGPLv3 from Redis 8Apache-2.0
    deploymentself-host or redis cloudself-host, lite, or zilliz cloud
    index typeshnsw, flathnsw, ivf, diskann, gpu cagra, scann, rabitq
    hybrid searchvia the search modulefull, with sparse indexes
    pricing modelhourly by memory tiercompute units; rates largely unpublished

    switch for100m to 1bn+ vectors, where nothing else on this list is really appropriate

    pros
    • +widest index selection: diskann, gpu cagra, rabitq, ivf family, sparse
    • +genuinely proven at a billion vectors and beyond
    • +2.6 removed the kafka/pulsar dependency
    • +apache-2.0 with over 100k collections for multi-tenancy
    cons
    • heaviest self-host here despite 2.6's improvements
    • over-engineered below roughly 10m vectors
    • the vendor also maintains the main public benchmark
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  4. 4

    Elasticsearch

    #4 in vector databases · the licence objection to using it for vectors is largely obsolete

    85/100

    verdictbest-in-class hybrid retrieval with transparent serverless pricing — and its dense and sparse vector code sits in the agpl-licensed tree, not the restricted one.

    Elasticsearch vs Redis
     RedisElasticsearch
    pricefree up to 30MB; essentials from $0.007 / hourserverless from $0.09 per search VCU-hour; storage $0.047/GB/month
    free tieryesyes
    licenseRSALv2 / SSPLv1 / AGPLv3 from Redis 8AGPLv3 / SSPLv1 / Elastic 2.0 triple
    deploymentself-host or redis cloudself-host, cloud, serverless
    index typeshnsw, flatlucene hnsw, int8/int4, bbq
    hybrid searchvia the search modulebest in class — bm25 + rrf
    pricing modelhourly by memory tiervcu-hours + storage per gb

    switch forteams already running elasticsearch, and anyone who needs real hybrid search

    pros
    • +vector search is available under agplv3, not licence-restricted
    • +best-in-class hybrid with real bm25 and rank fusion
    • +transparent serverless pricing with 50gb free on vector profiles
    • +better binary quantization and mature filtering
    cons
    • jvm heap tuning and real operational weight
    • licence structure is complex even where it's permissive
    • the elser sparse model is separately restricted
  5. 5

    Weaviate

    #5 in vector databases · multi-tenancy and hybrid search, priced per million dimensions

    84/100

    verdictthe strongest combination of hybrid search and per-tenant isolation here — with a pricing model that punishes large embeddings in a way no competitor's does.

    Weaviate vs Redis
     RedisWeaviate
    pricefree up to 30MB; essentials from $0.007 / hour$45 / month minimum; vector dimensions from $0.00465 per million
    free tieryesyes
    licenseRSALv2 / SSPLv1 / AGPLv3 from Redis 8BSD-3-Clause
    deploymentself-host or redis cloudself-host, cloud, or byoc
    index typeshnsw, flathnsw + product/binary/scalar quantization
    hybrid searchvia the search modulefirst-class bm25 + dense fusion
    pricing modelhourly by memory tierper million vector dimensions

    switch formulti-tenant saas rag where each customer needs isolation

    pros
    • +native multi-tenancy with isolated shards and cold offloading
    • +first-class hybrid search with configurable fusion
    • +bsd-3-clause — clean permissive licence
    • +excellent documentation
    cons
    • dimension-based pricing doubles when you double embedding size
    • $45/month minimum on the entry cloud tier
    • historically memory-hungry without quantization
  6. 6

    Chroma

    #6 in vector databases · the best developer experience, and honest per-unit pricing

    82/100

    verdictpip install and go, with genuinely transparent pricing — the right call up to a few million vectors and the wrong one well before a hundred.

    Chroma vs Redis
     RedisChroma
    pricefree up to 30MB; essentials from $0.007 / hour$0 base with usage-based billing; $0.33 per GiB-month storage
    free tieryesyes
    licenseRSALv2 / SSPLv1 / AGPLv3 from Redis 8Apache-2.0
    deploymentself-host or redis cloudembedded, self-host, or cloud
    index typeshnsw, flathnsw
    hybrid searchvia the search modulemetadata + full-text; weaker than rivals
    pricing modelhourly by memory tierper gib stored, written and queried

    switch forprototypes and small-to-mid production where developer speed matters most

    pros
    • +best developer experience in the category
    • +genuinely transparent per-unit pricing with no base fee
    • +apache-2.0
    • +usage-based billing starts at zero
    cons
    • hnsw only — thinnest tuning surface here
    • unproven above roughly 10m vectors
    • local mode and cloud are different code paths
  7. 7

    AWS S3 Vectors

    #7 in vector databases · storage at six cents a gigabyte, and it reprices the floor of the market

    80/100

    verdictroughly five times cheaper per gigabyte than the premium managed options, and explicitly built for cost rather than latency — choose it deliberately, not by default.

    AWS S3 Vectors vs Redis
     RedisAWS S3 Vectors
    pricefree up to 30MB; essentials from $0.007 / hour$0.06 per GB per month storage
    free tieryesno
    licenseRSALv2 / SSPLv1 / AGPLv3 from Redis 8proprietary, managed only
    deploymentself-host or redis cloudaws managed
    index typeshnsw, flatnot documented
    hybrid searchvia the search modulenot a strength
    pricing modelhourly by memory tierper gb stored, per million queries

    switch forlarge, cold, infrequently queried corpora where sub-second is acceptable

    pros
    • +$0.06/GB/month — the cheapest credible storage here
    • +two billion vectors per index, 31 regions
    • +fully published, legible rate card
    • +first 512KB returned per query is free
    cons
    • sub-second latency — wrong for interactive agents
    • 128KB minimum per PUT punishes unbatched writes ~21×
    • index algorithm and internals undocumented
  8. 8

    turbopuffer

    #8 in vector databases · object-storage economics with the best namespace multi-tenancy

    78/100

    verdictthe architecture the rest of the category converged on, with genuinely good per-tenant isolation — behind unpublished unit rates and no self-host option.

    turbopuffer vs Redis
     Redisturbopuffer
    pricefree up to 30MB; essentials from $0.007 / hour$16 / month minimum usage
    free tieryesno
    licenseRSALv2 / SSPLv1 / AGPLv3 from Redis 8proprietary, managed only
    deploymentself-host or redis cloudmanaged; byoc at enterprise
    index typeshnsw, flatnot fully documented
    hybrid searchvia the search modulesupported
    pricing modelhourly by memory tierusage, above plan minimums

    switch formulti-tenant products with large, mostly-cold corpora

    pros
    • +excellent economics on large, mostly-cold corpora
    • +namespace-per-tenant isolation is genuinely well designed
    • +low $16/month entry minimum
    • +well-regarded engineering team and public writing
    cons
    • per-unit rates are not published anywhere fetchable
    • closed source with no self-host option
    • no free tier, and higher tail latency by design
  9. 9

    OpenSearch

    #9 in vector databases · apache-2.0 with no licence anxiety, and serverless finally scales to zero

    76/100

    verdictthe safe institutional choice on aws — genuinely apache-2.0, with faiss and lucene engines, and the always-on cost floor finally removed.

    OpenSearch vs Redis
     RedisOpenSearch
    pricefree up to 30MB; essentials from $0.007 / hourfree self-hosted; serverless billed per OCU-hour
    free tieryesyes
    licenseRSALv2 / SSPLv1 / AGPLv3 from Redis 8Apache-2.0
    deploymentself-host or redis cloudself-host or aws managed/serverless
    index typeshnsw, flathnsw, ivf, faiss + lucene, disk modes
    hybrid searchvia the search modulesupported, mature
    pricing modelhourly by memory tierocu-hours; scales to zero on nextgen

    switch foraws-committed teams who want vector search without licence questions

    pros
    • +apache-2.0 throughout with no licence ambiguity
    • +faiss and lucene engines, plus disk-based and quantized modes
    • +serverless nextgen scales to zero after 10 minutes
    • +native on aws with full hybrid search
    cons
    • vector collections can't share compute units with other workloads
    • forked-project ergonomics lag elasticsearch
    • compute-unit accounting is confusing
  10. 10

    Vespa

    #10 in vector databases · the most sophisticated ranking engine here, with the steepest learning curve

    75/100

    verdicta full search and ranking engine rather than a vector store — if ranking quality is your product, rank it second; if you just need rag, it's overkill.

    Vespa vs Redis
     RedisVespa
    pricefree up to 30MB; essentials from $0.007 / hour$0.05 per vCPU-hour on the startup plan
    free tieryesno
    licenseRSALv2 / SSPLv1 / AGPLv3 from Redis 8Apache-2.0
    deploymentself-host or redis cloudself-host or vespa cloud
    index typeshnsw, flathnsw + tensor ranking framework
    hybrid searchvia the search modulebest in class, multi-phase ranking
    pricing modelhourly by memory tierper vcpu / gb / gpu hour

    switch forproducts where retrieval and ranking quality is the differentiator

    pros
    • +unmatched ranking and hybrid sophistication
    • +most transparent resource pricing in the category
    • +apache-2.0 and proven at very large scale
    • +unit prices decline as allocation grows
    cons
    • steepest learning curve here by a distance
    • $20,000/month minimum on the enterprise tier
    • overkill for straightforward rag
+ 9 more tested, not detailed here
we ranked 20 vector databases in total. the 9 that didn't make this page are written up in the full ranking →

how these were compared

every tool on this page went through the same test as Redis — same tasks, same order, scored the same way. the comparison tables are the figures from that testing, not vendor spec sheets.

the vector databases test in full →
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