⚡Stack Radar

NoSQL Databases · Reviewed 21 Oct 2026

Dragonfly Cloud

Fully managed, multi-threaded in-memory data store compatible with Redis and Memcached.

from $36 · free tier · dragonflydb.io ↗

Paid against free › NoSQL Databases › Dragonfly Cloud

Dragonfly Cloud at a glance
80.6KEstimated total trafficModeled monthly visits1.6KRanking keywordsPublic search sample98/100Technical health15 pages checked0Referring domainsLive backlink summary

Overview

01

Summary

The useful answer before the supporting detail.

Dragonfly Cloud is a fully managed, multi-threaded in-memory data store built for real-time workloads at scale S28. It is used by teams that need a Redis-compatible datastore without operating infrastructure, with a management console and automatic failover S28. Its practical value is delivering high throughput and sub-millisecond latency while reducing operational overhead and cost S6 S28.

02

Product identity and positioning

Name, category, positioning, audience, and first-party claims.

  • Identity: Dragonfly Cloud, a fully managed in-memory data store S28.
  • Core purpose: Provides a Redis-compatible datastore for real-time workloads without infrastructure management S28.
  • Intended users: Teams needing a managed datastore for high-throughput, low-latency applications S28.
  • Product scope: A managed service built on the Dragonfly engine, with console, scaling, and support S28.
  • Positioning: Fully managed, multi-threaded, and cost-effective compared to Redis S28 S25.
  • Claimed distinction: Achieves 25x performance vs Redis and sub-millisecond latency S9 S6.
03

Use cases and audiences

Who the product serves and the jobs it is designed to handle.

AudiencePractical use case
AdtechRunning a 310GB cache at 100k RPS S25
Online deliveryHigh-throughput read-heavy feature store S25
Teams needing managed infrastructureShip instead of operating infrastructure S28

Features

09

Features and capabilities

Core functions and evidence-backed product capabilities.

CapabilityWhat it does
Redis/Memcached API compatibilityDrop-in replacement without code changes S1 S9.
Multi-threaded architectureHigher throughput on same hardware S6.
Management consoleDashboards, logs, alerts, and API integration S28.
Automatic failover and backupsMulti-region backups and failover S28.
RBAC and ACLUser access management with audit logs S28.
Bring-your-own-cloudDeploy on AWS, GCP, Azure, or on-prem S28.
Kubernetes OperatorDeploy and manage via K8s S6.
Prometheus supportNative monitoring without agent S6.
10

Integrations and ecosystem

Connected products, exports, APIs, and workflow compatibility.

Integration or interfaceTypeWhat it enables
Redis APIAPIDrop-in replacement for Redis S9
Memcached APIAPIDrop-in replacement for Memcached S9
Kubernetes OperatorExtensionDeploy and manage via K8s S6
PrometheusIntegrationNative monitoring without agent S6
Management console APIAPIProgrammatic access to console features S28

Pricing

11

Pricing and plans

Published plans, free access, trials, credits, and pricing limitations.

Plan or offerVerified price and billingIncludedLimits
FlexFrom $36 per month, monthly S25Managed service, horizontal/vertical scaling S25Example: 12 GB for $77/month S25
BusinessFrom $2,000 per month, monthly S25Bring your own cloud, SSO, custom SLA S25Not established
Self-ManagedContact us S25Your own infrastructure, LDAP, RBAC S25Not established
Free trialFree trial on Flex S25Try Dragonfly Cloud free S28Not established

Competition

12

Competitive landscape

Domains competing for the same organic search demand.

Sampled competitors

CompetitorFirst-page appearancesSampled queriesAverage positionQueries observed
reddit.com221.5dragonflydb, redis alternatives
redis.io215redis alternatives
news.ycombinator.com226dragonflydb, redis alternatives
github.com216.5dragonflydb
youtube.com111redis alternatives
aws.amazon.com113redis alternatives
facebook.com117redis alternatives
stackoverflow.com118redis alternatives
diva-portal.org119redis alternatives

Website intelligence — Collected evidence

Customers, testimonials, and first-party review claims

No supporting public-page evidence was collected for this category.

Independent review sources discovered

PlatformStatusDiscovered source
Googlediscovered_not_scrapedhttps://cloud.google.com/bigquery

Website intelligence — Scout synthesis

  • The site directly compares Dragonfly to Redis, Valkey, Memcached, AWS ElastiCache, Redis Cloud, and Upstash W5W90W189W191W196W230.
  • A dedicated blog post compares Dragonfly Cloud vs. AWS ElastiCache, highlighting ElastiCache's high costs and operational complexity due to Redis's single-threaded nature W90W91W103W104.
  • A guide lists 8 best Redis alternatives, including Valkey, Memgraph, Memcached, Apache Ignite, and managed services like DynamoDB and Google Cloud Memorystore W189W190W191W192W195W197.
  • The Upstash guide positions Dragonfly Cloud as a managed alternative with Redis-compatible APIs and higher performance W194W230.
  • Scout inference: The competitive content is comprehensive and directly targets users evaluating alternatives to Redis and its managed ecosystems.
13

AI visibility

Measured ChatGPT and Google AI mentions with cited sources.

AI-assistant referral traffic

  • Estimated current generative-AI referral share: 1.6%
MonthSource
—All AI assistants
—All AI assistants

Website intelligence — Collected evidence

Site-stated AI capabilities

Page typeEvidenceSource
featuresIn the world of machine learning (ML), feature stores have emerged as a critical component for managing, serving, and reusing features across different models and teams. Simply put, a feature store is a centralized repository that standardizes the storage, retrieval, and sharing of features—the measurable properties or inputs used by ML models. Using a feature store helps maintain consistency between training and inference, reduce redundant computation, and enable collaboration across teams.https://www.dragonflydb.io/blog/feature-store-architecture-and-storage
featuresOrganizations today can select from multiple robust feature store solutions, both open-source and proprietary. Among open-source options, Feast has gained wide adoption for its flexible support of multiple storage backends (like ScyllaDB and Dragonfly), while Feathr specializes in low-latency serving at scale, and Hopsworks offers an end-to-end ML platform with integrated feature store capabilities. Cloud providers like AWS and GCP also provide managed solutions through SageMaker and Vertex AI, respectively. While all these options are powerful, Feast’s strong community support and platform-agnostic nature make it appealing for avoiding vendor lock-in and adapting to diverse infrastructure needs.https://www.dragonflydb.io/blog/feature-store-architecture-and-storage
featuresIn this blog post, we explore the seamless integration of Dragonfly as a drop-in replacement for Redis in Feast—an acclaimed feature storage and server project widely recognized in the machine learning domain. With just some simple steps, we unveil the outstanding compatibility of Dragonfly as an online store solution for Feast, ensuring a smooth transition without compromising existing functionalities.https://www.dragonflydb.io/blog/running-the-feast-feature-store-with-dragonfly
featuresFeast stands as an exceptional open-source feature store, revolutionizing the efficient management and uninterrupted serving of machine learning (ML) features for real-time applications. At its core, Feast offers a sophisticated interface for storing, discovering, and accessing features—the individual measurable properties or characteristics of data essential for ML modeling. Operating on a distributed architecture, Feast harmoniously integrates several pivotal components, including the Feast Registry, Stream Processor, Batch Materialization Engine, and Stores.https://www.dragonflydb.io/blog/running-the-feast-feature-store-with-dragonfly
featuresThe significance of low-latency feature serving provided by in-memory data stores cannot be overstated. It empowers machine learning models to access necessary features swiftly, allowing for more efficient and timely predictions or inferences. Whether it's delivering personalized recommendations, making instant decisions, or powering real-time analytics, in-memory data stores ensure quick access to features, contributing to improved model performance and elevated user satisfaction.https://www.dragonflydb.io/blog/running-the-feast-feature-store-with-dragonfly
featuresExplore why modern e-commerce AI needs a feature store backbone like Dragonfly for predictable, ultra-low latency and massive throughput.https://www.dragonflydb.io/blog/scaling-the-e-commerce-brain-how-dragonfly-powers-modern-ml-feature-stores
featuresFor infrastructure engineers, this means scaling a data platform that never sleeps and never stops evolving, just like AI/ML models themselves. Traditional data stores often buckle under this pressure, which turns agility into operational overhead. This leaves us with the critical infrastructure question: How do we build a data layer for the modern ML feature store that is inherently scalable, consistently performant, and operationally simple? The answer lies not in adding more complexity to the stack but in choosing a foundational technology designed for this specific class of problem.https://www.dragonflydb.io/blog/scaling-the-e-commerce-brain-how-dragonfly-powers-modern-ml-feature-stores
featuresIn the world of machine learning (ML), feature stores have emerged as a critical component for managing, serving, and reusing features across different models and teams. Simply put, a feature store is a centralized repository that standardizes the storage, retrieval, and sharing of features—the measurable properties or inputs used by ML models. Using a feature store helps maintain consistency between training and inference, reduce redundant computation, and enable collaboration across teams.https://www.dragonflydb.io/blog/feature-store-architecture-and-storage
featuresOrganizations today can select from multiple robust feature store solutions, both open-source and proprietary. Among open-source options, Feast has gained wide adoption for its flexible support of multiple storage backends (like ScyllaDB and Dragonfly), while Feathr specializes in low-latency serving at scale, and Hopsworks offers an end-to-end ML platform with integrated feature store capabilities. Cloud providers like AWS and GCP also provide managed solutions through SageMaker and Vertex AI, respectively. While all these options are powerful, Feast’s strong community support and platform-agnostic nature make it appealing for avoiding vendor lock-in and adapting to diverse infrastructure needs.https://www.dragonflydb.io/blog/feature-store-architecture-and-storage
featuresIn this blog post, we explore the seamless integration of Dragonfly as a drop-in replacement for Redis in Feast—an acclaimed feature storage and server project widely recognized in the machine learning domain. With just some simple steps, we unveil the outstanding compatibility of Dragonfly as an online store solution for Feast, ensuring a smooth transition without compromising existing functionalities.https://www.dragonflydb.io/blog/running-the-feast-feature-store-with-dragonfly
featuresFeast stands as an exceptional open-source feature store, revolutionizing the efficient management and uninterrupted serving of machine learning (ML) features for real-time applications. At its core, Feast offers a sophisticated interface for storing, discovering, and accessing features—the individual measurable properties or characteristics of data essential for ML modeling. Operating on a distributed architecture, Feast harmoniously integrates several pivotal components, including the Feast Registry, Stream Processor, Batch Materialization Engine, and Stores.https://www.dragonflydb.io/blog/running-the-feast-feature-store-with-dragonfly

These are first-party product claims, not independent measurements of AI-search visibility.

Website intelligence — Scout synthesis

  • The site's content is structured around clear, factual claims (e.g., "Dragonfly is multi-threaded", "30-60% better memory utilization") that are likely to be cited by AI models W18W71.
  • The presence of detailed comparison guides and benchmark references provides citable source material for AI-generated answers about Redis alternatives W207W209.
  • Scout inference: The site's authoritative tone and data-backed claims may improve its visibility in AI-generated responses, though no direct evidence of AI citations is present in the catalog.
14

Marketing opportunities

Actions calculated directly from the collected evidence.

OpportunityRecommended actionEvidence
Competitor gapReview the sampled queries where reddit.com appears.2 first-page appearances across dragonflydb, redis alternatives

Website intelligence — Collected evidence

Website evidence gaps

  • customers: no usable public page was collected; verify whether the site publishes this evidence.
  • reviews: no usable public page was collected; verify whether the site publishes this evidence.
  • changes: no usable public page was collected; verify whether the site publishes this evidence.
  • identity: no usable public page was collected; verify whether the site publishes this evidence.
  • technical: no usable public page was collected; verify whether the site publishes this evidence.

Website intelligence — Scout synthesis

  • The site has a strong library of comparison and use-case content (feature stores, Redis alternatives) that can be expanded to target additional keywords like "Valkey vs Dragonfly" or "Memcached alternatives" W178W202.
  • The pricing page includes detailed cost-saving case studies that could be repurposed into standalone marketing assets or landing pages W54W55W56W57.
  • The blog content on feature stores (e.g., Feast integration) positions Dragonfly for the ML/AI infrastructure market, an area with growing demand W135W159.
  • Scout inference: There is an opportunity to create more targeted content around specific use cases (e.g., real-time bidding, fraud detection) mentioned in the blog, to capture niche search demand W118.

Trust and proof

15

Customers, proof, and reviews

Published proof, customer signals, and review evidence.

Independent reviews

PlatformPageHow it was foundNote
AWS MarketplaceAWS Marketplace: Dragonfly Cloud ReviewsGrounded web search### Reviews from AWS customer 0 AWS reviews 5 star 0 4 star 0 3 star 0 2 star 0 1 star 0 ### External reviews 15 reviews from ... for us in terms of performance and scalability. We needed a more powerful alternative to Redis that could
Top Business SoftwareDragonfly Verified Customer Reviews (2026)Grounded web search# Dragonfly Verified Customer Reviews (2026) 4.9 out of 5 stars. 16 User Reviews Write a Review Unlock unparalleled performance and savings with modern efficiency. ... . This transition not only enhances productivity but also allows teams to explore new

Customer proof

Proof typeNamed customer or sourceEvidence
Case studyRay Hughescustomer case study — - Latency Reduction: Average response times plummeted from ~200ms to ~30ms, giving bettors critical extra milliseconds to capitalize on arbitrage opportunities. (Case Study: Sharp App Boosts Sports Betting with Dragonfly)
Case studyShareChatcustomer case study — - 40% cost reduction – Lower infrastructure footprint and more efficient resource usage. (ShareChat Achieves 40% Cloud Cost Reduction with Dragonfly Cloud)
Case studydragonflydb.iocustomer case study — # Case Study: Zedia Powers High-Performance AdTech with Dragonfly for Cost Efficiency Discover how Zedia slashed costs & scaled ad delivery using Dragonfly’s high-performance, Redis-compatible in-memory data store wit… (Case Study: Zedia Powers High-Performance AdTech with Dragonfly)

Independent mentions

  • Dragonfly: Modern Replacement for Redis and Memcached / Hacker News — Great blog with a lot of good stuff. Loved their Scalable In Memory Dragonfly Swarm post, for example. https ... I do wish they did more to show folks how to go deeper in integrating Dragonfly ... about a native distributed log that would offer super fast
  • We evaluated DragonflyDB for Memcache. It was repeatably orders of magnitude slo... / Hacker News — Yes, your results are either inaccurate or deceptive at best. I challenge you run to memcached, under all default settings, and Dragonfly, under all default settings, and memtier_benchmark, under all default settings. Performance is reproducibly orders of magnitude slower, and Dragonfly is also much less efficient--consuming more than double the CPU usage for the same workload. We also created a test Dragonfly cluster mirroring a small percentage of production traffic in order to do a side-by-si
  • Dragonfly Is Production Ready (and we raised $21M) / Hacker News — Dragonfly Is Production Ready (and we raised $21M) / Hacker News Dragonfly Is Production Ready (and we raised $21M) (dragonflydb.io) 82 points by ksec on March 21, 2023 / hide / past / favorite / 66 comments
  • Dragonfly / An In-Memory Data Store without Limits — "By migrating from managed Valkey to Dragonfly Cloud we've been able to cut our cluster size by 70% while also reducing our average and P99 latencies by 40-50% while further optimizing network cost in ways not possible with our managed Valkey service."
  • Meesho Migrates from Redis to Dragonfly: Cuts Latency by 50% — The latency was reduced by roughly 40 to 50% and the cost reduction was around 60%,” shared ... saw an improvement in throughput, enabling them to handle more concurrent requests while using fewer resources. These improvements underscore the importance of

External links were grounded in one live web search. Search snippets are leads, not independently scraped page content; rejected links remain available in Review evidence.

16

Company, security, and trust

Ownership, policies, security signals, and public trust evidence.

SignalFindingSource
Company and ownershipDragonfly Leadership Teamsource
Contact and supportNot published on the collected pages.—
Legal and privacyThe library offers advanced features such as connection pooling, context-based request cancellation, and structured logging. Its API is idiomatic and consistent with Go’s conventions, making it easy to integrate into prosource
Security and complianceSecurity: While all tiers provide encryption at rest and in transit, additional security enhancements such as Virtual Network (VNet) support are available solely in the Premium tier.source
Uptime and status pageStandard Tier: This tier provides a more robust service with high-availability and automatic failover. It has two nodes, a primary and a replica, located in two different zones for redundancy purposes. In case the prsource

Not found on the collected pages: contact and support. This only means Scout did not find it on the pages it read; it says nothing about the company itself.

17

Documentation and product updates

Help resources, product guidance, and visible changes over time.

Resource or updateTypeWhat is availableDate or freshness
Documentation / DragonflyDocumentationDragonfly is a modern in-memory datastore, fully compatible with Redis & Memcached APIs. Learn how to use Dragonfly with our comprehensive documentation. (source)Not dated
Azure Cache for Redis Pricing - The Ultimate GuideGuideGuides, comparisons, pricing breakdowns, use cases, and how-to articles. (source)Not dated
DigitalOcean Redis Pricing - The Ultimate GuideGuideGuides, comparisons, pricing breakdowns, use cases, and how-to articles. (source)Not dated
ElastiCache Pricing - Everything You Need to KnowGuideGuides, comparisons, pricing breakdowns, use cases, and how-to articles. (source)Not dated
Answered What are the differences in pricing between Amazon ElastiCache and DAX?GuideThis page answers the following question: What are the differences in pricing between Amazon ElastiCache and DAX? (source)Not dated
Google Memorystore Redis Pricing - Everything You Need To KnowGuideGuides, comparisons, pricing breakdowns, use cases, and how-to articles. (source)Not dated
Understanding Heroku Redis Pricing - In-Depth GuideGuideGuides, comparisons, pricing breakdowns, use cases, and how-to articles. (source)Not dated
Amazon MemoryDB Pricing - Everything You Need to KnowGuideGuides, comparisons, pricing breakdowns, use cases, and how-to articles. (source)Not dated

Not found in the collected public sources: public roadmap, changelog or release notes, status page, separate help center or knowledge base.

Technical

18

Technical and delivery signals

Delivery model, platforms, API, data ownership, reliability claims and release activity.

SignalFindingSource
Delivery model (cloud, self-hosted, desktop, browser extension)Fully managed with bring-your-own-cloud on AWS, GCP, Azure, or on-prem S28.S28
Platforms and appsDeploy on AWS, GCP, Azure, or on-prem S28.S28
API and extensibility (API, SDK, webhooks, MCP, plugins)Management console API integration out of the box S28.S28
Data ownership and exportData kept inside your own cloud account S28.S28
Performance and reliability claimsConsistent sub 1ms P99 latency S6.S6
Release activityNot published on the collected pages.—
19

Measured infrastructure

Domain, certificate, and hosting signals, measured directly.

SignalFinding
First archived2022-05-30 — web archive
TLS certificateLet's Encrypt, valid, expires 2027-01-05
Security headersHSTS, CSP, X-Frame-Options
Headers not servedX-Content-Type-Options, Referrer-Policy
Server headerVercel
Mail handled byaspmx.l.google.com, alt1.aspmx.l.google.com
Nameserversns-cloud-e1.googledomains.com, ns-cloud-e3.googledomains.com

Not measurable: rdap: no RDAP server published for .io. A gap here means the lookup was unavailable, not that the finding is negative.

20

Technical site audit

A bounded multi-page crawl with page-level issues and URLs.

Scout — Bounded technical crawl

  • Pages crawled: 15 of 15 maximum
  • Technical health: 98/100
  • Critical issues: 0
  • Warnings: 7
  • Notices: 1

Issues

SeverityIssueValueAffected URL
warningTitle length should be reviewed74https://www.dragonflydb.io/industries/media-and-entertainment
noticeMeta description length should be reviewed177https://www.dragonflydb.io/industries/media-and-entertainment
warningTitle length should be reviewed78https://www.dragonflydb.io/industries/financial-services
warningPage should have one primary H10https://www.dragonflydb.io/blog
warningDuplicate title in audit sample2https://www.dragonflydb.io/
warningDuplicate title in audit sample2https://www.dragonflydb.io/
warningDuplicate meta description in audit sample2https://www.dragonflydb.io/
warningDuplicate meta description in audit sample2https://www.dragonflydb.io/

Crawled pages

StatusTitleH1sWordsResponseURL
200Dragonfly \An In-Memory Data Store without Limits1685356 ms
200Dragonfly \An In-Memory Data Store without Limits1685226 ms
200Documentation \Dragonfly117673 ms
200Dragonfly Pricing - Up to 80% Off Redis Costs1662495 mshttps://www.dragonflydb.io/pricing
200What is Dragonfly?11283308 mshttps://www.dragonflydb.io/what-is-dragonfly
200Dragonfly Cloud - Fully Managed In-Memory Data Store11138312 mshttps://www.dragonflydb.io/cloud
200A comparison of Dragonfly vs. Redis.1819522 mshttps://www.dragonflydb.io/dragonfly-vs-redis
200DragonflyDB for Media & Entertainment \Scale Real-Time Content Seamlessly1594432 ms
200DragonflyDB for Financial Services \Scale Real-Time Transactions and Insights1650293 ms
200Dragonfly for Adtech \Real-Time Bidding at Scale1697328 ms
200Dragonfly \Memory-efficient, high-performance caching1783341 ms
200DragonflyDB \Low-latency, fault-tolerant feature store1775319 ms
200Dragonfly \High-Throughput Queues for Concurrent Job Systems1838335 ms
200Dragonfly \Sorted Sets for Rankings & Priority Queues1761385 ms
200Dragonfly - Blog0890367 mshttps://www.dragonflydb.io/blog

This is a bounded Scout crawl. Core Web Vitals and Lighthouse scores are not claimed unless a dedicated performance audit is connected.

21

Search Console and Analytics

First-party search and behavioral data connection state.

Modeled acquisition channels

ChannelEstimated share
direct38.3%
search38.2%
search organic33.5%
search paid4.7%
social6.6%
social organic5.9%
social paid0.8%
referrals9.5%
paid referrals2.2%
mail2.3%
gen ai1.6%
affiliate1.3%
other0.0%

First-party connections

IntegrationStatusData available after connection
Google Search ConsoleNot connectedClicks, impressions, CTR, average position, queries, pages, countries, and devices
Google AnalyticsNot connectedOrganic sessions, engagement, landing pages, conversions, geography, and devices

No first-party Search Console or Analytics numbers are shown until the site owner authorizes the corresponding Google account. Scout traffic model values above are modeled estimates, not private analytics totals.

Limits and method

22

Limitations and open questions

Known constraints and missing evidence from the collected pages.

The collected pages do not mention specific competitors by name. Evidence gaps include lack of detailed security certifications, uptime SLAs, and independent review ratings. Open questions remain about exact pricing for Business and Self-Managed plans, and the full scope of compliance features.

23

Sources, method, and limitations

Evidence inventory, collection method, provider use, and constraints.

This report is based on 15 collected pages from dragonflydb.io, including product, pricing, and documentation pages. External proof search was used but only to verify existence of results, not to extract claims. Pricing data was sourced from the official pricing page. Limitations include potential marketing bias and lack of third-party verification.

Paid against free, in your inbox

One job at a time: the paid tools worth paying for, the free ones you can run yourself, and who should pick which.

No tracking pixels. One click to leave. We use your address to send Stack Radar and nothing else. Privacy.