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Machine Learning · head to head

Pinecone vs Seldon

Pinecone logo

Pinecone

Machine Learning

Vector database for machine learning

From
Free
Rated
-
Full reviewWebsite
Seldon logo

Seldon

Machine Learning

Kubernetes model serving whose current version is licensed under the Business Source Licence

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Pinecone reads and writes are billed on separate meters, and reads are far more expensive, at $16 to $18 per million against $4 to $4.50 for writes on Standard; Seldon seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
  • They diverge on capability: Pinecone covers Vector similarity search, Seldon covers Kubernetes custom resources.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Pinecone and Seldon actually diverge.

Attributes where Pinecone and Seldon differ
AttributePineconeSeldon
PlatformsWebLinux
Founded20192014

Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), user rating (Not yet rated), category (Machine Learning).

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 Pinecone

  • Vector similarity search
  • Metadata filtering
  • Namespace partitioning
  • Real-time updates
  • Hybrid search
  • OpenAI
  • Cohere
  • LangChain

Only in Seldon

  • Kubernetes custom resources
  • Inference graphs
  • Traffic strategies
  • Open Inference Protocol
  • Alibi Explain
  • Alibi Detect
  • Kafka-backed pipelines in v2
  • Commercial control plane

What people use each for

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

Pinecone

  • Vector database for AI/ML applicationsnot Seldon
  • Semantic search implementationnot Seldon
  • Recommendation systemsnot Seldon
  • RAG (Retrieval-Augmented Generation) architecturesnot Seldon

Seldon

  • Serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate servicesnot Pinecone
  • Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot Pinecone
  • Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot Pinecone
  • Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot Pinecone

Where each one falls short

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

Pinecone

  • Reads and writes are billed on separate meters, and reads are far more expensive, at $16 to $18 per million against $4 to $4.50 for writes on Standard
  • Unit prices vary by region, so the same workload costs different amounts in different places
  • The Standard plan carries a $50 monthly minimum and Enterprise $500, charged whether or not the usage reaches it
  • Enterprise pays more per unit as well as more in minimum, at $24 to $27 per million reads against Standard's $16 to $18
  • Indexes and namespaces are capped by plan, at 5 indexes on the free tier and 20 on Standard
  • RBAC and SSO require the Standard plan

Seldon

  • Seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
  • Core v1 remains Apache 2.0 but is in maintenance, so taking the free route means running software that receives no new development while the architecture it belongs to moves on without it.
  • Version 2 is a different system rather than a newer release, with different custom resources, a scheduler component and a Kafka-based pipeline model, so migrating from v1 is a re-implementation of every deployment manifest rather than an upgrade.
  • Kafka is a dependency for v2 pipelines, so an organisation that does not already operate it takes on a distributed log with its own storage, retention, rebalancing and failure modes purely in order to serve models.
  • Everything assumes Kubernetes fluency and the failure modes are Kubernetes failure modes, custom resource version mismatches, an operator that will not reconcile, admission webhooks and resource limits terminating an inference pod mid-request, so it needs a platform engineer rather than a data scientist.

Pricing, plan by plan

Pinecone

Free
  • StarterFree
    • 2GB storage
    • 2M write units/month
    • 1M read units/month
  • Builder$20/month
    • 10GB storage
    • 5M write units
    • 2M read units
  • Standard$50/month
    • Unlimited storage ($0.33/GB/month)
    • 20 indexes per project
    • 100K namespaces
  • Enterprise$500/month
    • 99.95% uptime SLA
    • BYOC (Bring Your Own Cloud) option
    • Private endpoints

Seldon

Free
  • Seldon CoreFree
    • Open source
    • Kubernetes deployment
    • Model serving
  • Seldon DeployFree
    • Enterprise features
    • GUI
    • Monitoring

Which should you pick?

Choose Pinecone if

  • You need vector similarity search.
  • You want to start without paying.
  • You also want metadata filtering.
Pinecone review

Choose Seldon if

  • You need kubernetes custom resources.
  • You want to start without paying.
  • You work on Linux.
  • You also want inference graphs.
Seldon review

Questions people ask

Is Pinecone or Seldon better?
Neither clearly leads. Pinecone starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Pinecone or Seldon?
Pinecone starts at Free and Seldon at Free.
Does Pinecone or Seldon run on more platforms?
Pinecone runs on Web. Seldon runs on Linux.
Can I use Pinecone for free?
Both have a free tier, so you can try either at no cost before committing.
What is Pinecone best used for?
Pinecone is most often used for vector database for ai/ml applications, semantic search implementation, recommendation systems, rag (retrieval-augmented generation) architectures. Of those, vector database for ai/ml applications and semantic search implementation are not what Seldon is typically brought in for.
What can Pinecone do that Seldon cannot?
Pinecone covers Vector similarity search, Metadata filtering, Namespace partitioning, Real-time updates. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

Answered from the vendors’ own pages

Pinecone: Does Pinecone offer a free plan?

Yes, Pinecone's Starter tier is free and includes 2GB storage, 2M write units/month, 1M read units/month, and supports up to 2 users and 1 project.

Source
Seldon: Is Seldon open source?

Partly, and this is the thing to check before you build on it. Core v1 is Apache 2.0 but in maintenance. Core v2 was moved to the Business Source Licence in 2024, which allows evaluation but not unlicensed production use. Verify the current licence of each component you intend to run, including MLServer and the Alibi libraries.

Pinecone: What are Pinecone's storage costs on the Standard plan?

On the Standard plan, storage costs $0.33/GB per month. Read units cost $16-18 per million units; write units cost $4-4.50 per million units.

Source
Seldon: What is the difference between v1 and v2?

Architecture, not just version number. v2 introduces a scheduler, a different set of custom resources and Kafka-backed pipelines. Manifests, mental model and operations all change, so treat a move as a project.

Pinecone: What support options does Pinecone provide?

Starter tier includes community Discord support. Builder tier includes free support. Standard tier support costs $29/month for Developer or $250/month for Pro. Enterprise tier includes Pro support.

Source
Seldon: Do I need Kubernetes?

Yes. It is a Kubernetes-native system and there is no meaningful deployment without a cluster and someone competent to run it.

Seldon: What is MLServer?

Seldon's Python inference server implementing the Open Inference Protocol, usable inside Seldon deployments or on its own. Check its current licence alongside Core's, since the company has moved projects onto the Business Source Licence.

Seldon: Do I have to run Kafka?

For v2 pipelines, yes. If you only need single models served, that dependency is a large amount of infrastructure for the benefit, and a simpler serving layer may be the better answer.

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