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AI · head to head

LatchBio vs Seldon

LatchBio logo

LatchBio

AI

AI agents that run bioinformatics analyses, from a company that pivoted from workflow hosting

From
On request
Free plan
No
Full reviewWebsite
Seldon logo

Seldon

Machine Learning

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

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • Only Seldon has a free tier, so it costs nothing to try first.
  • Each has a real cost: LatchBio the company has pivoted from hosted bioinformatics workflows to AI agents, so organisations that adopted the earlier SDK and workflow product are now on a line that is no longer the strategic focus.; 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: LatchBio covers Agent driven analysis, Seldon covers Kubernetes custom resources.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which LatchBio and Seldon actually diverge.

Attributes where LatchBio and Seldon differ
AttributeLatchBioSeldon
Starting priceOn requestFree
Pricing modelquotefreemium
Free tierNoYes
PlatformsWebLinux
CategoryAIMachine Learning
FoundedUnknown2014

Identical on both: user rating (Not yet rated).

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 LatchBio

  • Agent driven analysis
  • Multi assay support
  • Published benchmarks
  • White labelled portals
  • Common file format handling
  • Managed compute

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.

LatchBio

  • A small biotech with sequencing data and no bioinformatician who needs a first pass differential expression or cell type annotation quicklynot Seldon
  • An assay or kit vendor that wants to give customers an analysis portal under its own branding rather than shipping raw filesnot Seldon
  • A wet lab scientist who wants to explore a dataset interactively before commissioning a formal analysis from a core facilitynot Seldon
  • A research group benchmarking agent produced analyses against its own established pipeline to decide how much routine work can be delegatednot Seldon

Seldon

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

Where each one falls short

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

LatchBio

  • The company has pivoted from hosted bioinformatics workflows to AI agents, so organisations that adopted the earlier SDK and workflow product are now on a line that is no longer the strategic focus.
  • Agent generated analysis code has to be validated by someone who could have written it, which inverts the value proposition for the small labs that most need it and cannot check the output.
  • No pricing is published, so the cost of routine agent analysis at team scale cannot be estimated without contacting sales, and compute billed through the platform makes the bill usage dependent.
  • Regulated use is a poor fit: an agent that writes non deterministic analysis code is difficult to validate under GxP, so this is a research tool rather than something that will sit in a regulated diagnostic or submission workflow.
  • The vendor publishes its own benchmarks, which is more than most competitors do, but self reported benchmarks on selected datasets are not a substitute for testing the agent on your own data before committing.

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

LatchBio

On request
  • LatchBio Platform$undefined/year
    • Quoted through a sales process
    • Interactive sandbox available for trying the agent before contracting
    • Enterprise and white labelled portal deployments priced separately

Seldon

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

Which should you pick?

Choose LatchBio if

  • You need agent driven analysis.
  • You also want multi assay support.
LatchBio 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 LatchBio or Seldon better?
Neither clearly leads. LatchBio starts at On request and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, LatchBio or Seldon?
Seldon has a free tier; the other does not. Paid plans start at On request for LatchBio and Free for Seldon.
Does LatchBio or Seldon run on more platforms?
LatchBio runs on Web. Seldon runs on Linux.
Can I use Seldon for free?
Yes. Seldon has a free tier, so you can try it without paying. LatchBio starts at On request.
What is LatchBio best used for?
LatchBio is most often used for a small biotech with sequencing data and no bioinformatician who needs a first pass differential expression or cell type annotation quickly, an assay or kit vendor that wants to give customers an analysis portal under its own branding rather than shipping raw files, a wet lab scientist who wants to explore a dataset interactively before commissioning a formal analysis from a core facility, a research group benchmarking agent produced analyses against its own established pipeline to decide how much routine work can be delegated. Of those, a small biotech with sequencing data and no bioinformatician who needs a first pass differential expression or cell type annotation quickly and an assay or kit vendor that wants to give customers an analysis portal under its own branding rather than shipping raw files are not what Seldon is typically brought in for.
What can LatchBio do that Seldon cannot?
LatchBio covers Agent driven analysis, Multi assay support, Published benchmarks, White labelled portals. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

Answered from the vendors’ own pages

LatchBio: Is LatchBio still selling the original workflow platform?

The company is now led by agent.bio and its agent products. Buyers evaluating the older Python SDK workflow platform should confirm current support directly.

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.

LatchBio: Can I try it before buying?

Yes. There is an interactive sandbox where you can upload data and run the agent before entering a sales conversation.

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.

LatchBio: Is it suitable for regulated work?

Not really. Non deterministic agent generated code is hard to validate under GxP, so treat it as a research and exploration tool.

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.

LatchBio: What data types does it handle?

Single cell RNA sequencing, spatial transcriptomics, epigenomics, proteomics and pharmacology data, ingesting FastQ, H5AD and spreadsheet formats.

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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