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Databases · head to head

Dragonfly vs Vespa

Dragonfly logo

Dragonfly

Databases

High-performance Redis-compatible in-memory datastore with 25x better throughput

From
Free
Rated
-
Vespa logo

Vespa

Databases

Distributed AI search platform for retrieval, ranking, and inference

From
Free
Rated
-

The short version

  • Each has a real cost: Dragonfly flex tier starting at $36/month may be underpriced, requiring careful usage monitoring; Vespa pricing not publicly listed, requires contacting sales
  • They diverge on capability: Dragonfly covers Redis API compatibility, Vespa covers Vector search.

Where they differ

Only the attributes on which Dragonfly and Vespa actually diverge.

Attributes where Dragonfly and Vespa differ
AttributeDragonflyVespa
Pricing modelUsage-based cloud pricing with flexible tierscontact-sales
PlatformsCloud, AWS, GCP, AzureCloud, Self-hosted
FoundedUnknown2023

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Databases).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Dragonfly

  • Redis API compatibility
  • Thread-per-core architecture
  • High-performance caching
  • Memory efficiency
  • Real-time leaderboards
  • Message queue support
  • ML feature serving
  • Cloud deployment

Only in Vespa

  • Vector search
  • Text and structured search
  • Machine-learned ranking
  • Real-time serving
  • SQL interface
  • Automatic scaling
  • Open-source

What people use each for

The jobs each tool is most often brought in to do.

Dragonfly

  • High-throughput caching for web applicationsnot Vespa
  • Real-time leaderboards and rankingsnot Vespa
  • Message queue and event processingnot Vespa
  • ML model feature serving at millisecond latenciesnot Vespa
  • Gaming session state and player data storagenot Vespa

Vespa

  • Build RAG systems with semantic search over documentsnot Dragonfly
  • Power e-commerce search with ML rankingnot Dragonfly
  • Create recommendation engines for personalizationnot Dragonfly
  • Implement real-time search for news or feedsnot Dragonfly
  • Deploy private semantic search over sensitive datanot Dragonfly

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Dragonfly

  • Flex tier starting at $36/month may be underpriced, requiring careful usage monitoring
  • Business tier $2,000/month represents significant jump in cost
  • Limited to in-memory storage, not suitable for cold data or archival
  • Bring-your-own-cloud requirement on Business tier adds operational complexity
  • Cloud availability dependent on AWS/GCP/Azure uptime

Vespa

  • Pricing not publicly listed, requires contacting sales
  • Steeper learning curve compared to simpler search tools
  • Operational complexity for self-hosted deployments
  • Smaller ecosystem compared to cloud-native alternatives

Pricing, plan by plan

Dragonfly

Free
  • Free TierFree
    • 100 cloud credits for new signups
    • Equivalent to free trial
  • Business$2000/month
    • Starting price for enterprise offering
    • Bring-your-own-cloud deployment
    • Auto-scaling with custom SLAs
  • Enterprise$undefined/custom
    • Custom pricing
    • Any-cloud deployment
    • Custom instances and sizing

Vespa

Free

No published plan breakdown. See the Vespa review.

Which should you pick?

Choose Dragonfly if

  • You need redis api compatibility.
  • You want to start without paying.
  • You work on Cloud, AWS, GCP, Azure.
  • You also want thread-per-core architecture.

Choose Vespa if

  • You need vector search.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want text and structured search.

Questions people ask

Is Dragonfly or Vespa better?
Neither clearly leads. Dragonfly starts at Free and Vespa at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dragonfly or Vespa?
Dragonfly starts at Free and Vespa at Free.
Does Dragonfly or Vespa run on more platforms?
Dragonfly runs on Cloud, AWS, GCP, Azure. Vespa runs on Cloud, Self-hosted.
Can I use Dragonfly for free?
Both have a free tier, so you can try either at no cost before committing.
What is Dragonfly best used for?
Dragonfly is most often used for high-throughput caching for web applications, real-time leaderboards and rankings, message queue and event processing, ml model feature serving at millisecond latencies. Of those, high-throughput caching for web applications and real-time leaderboards and rankings are not what Vespa is typically brought in for.
What can Dragonfly do that Vespa cannot?
Dragonfly covers Redis API compatibility, Thread-per-core architecture, High-performance caching, Memory efficiency. Vespa covers Vector search, Text and structured search, Machine-learned ranking, Real-time serving.

Answered from the vendors’ own pages

Dragonfly: How much faster is Dragonfly than Redis?

Dragonfly achieves 3.97M queries per second compared to Redis's 718K QPS, representing a 25x improvement. Memory efficiency is also 30% better.

Source
Vespa: Is Vespa open-source?

Yes, Vespa is open-source under the Apache 2.0 license. The code is available on GitHub, and you can self-host or use the managed cloud service.

Source
Dragonfly: Can I migrate from Redis to Dragonfly without code changes?

Yes. Dragonfly maintains full API compatibility with Redis and Memcached, allowing drop-in replacement with minimal to no code modifications.

Source
Vespa: What latency can Vespa achieve?

Vespa is designed for sub-100 millisecond latencies with thousands of queries per second, suitable for real-time search and recommendation applications.

Source
Vespa: Does Vespa support vector search?

Yes, Vespa provides native vector search capabilities alongside text, structured data, and tensor operations for building comprehensive search and AI applications.

Source
Vespa: What is the pricing model for Vespa Cloud?

Vespa Cloud pricing is not publicly listed and requires contacting their sales team to discuss your specific use case and scale requirements.

Source
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