Softwr

Machine Learning · head to head

Langwatch vs Seldon

Langwatch logo

Langwatch

Machine Learning

LLM engineering platform for testing and evaluating AI agents in production

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: Langwatch free plan limited to 50k events per month, restricting larger deployments; 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: Langwatch covers Agent simulation testing, Seldon covers Kubernetes custom resources.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Langwatch and Seldon actually diverge.

Attributes where Langwatch and Seldon differ
AttributeLangwatchSeldon
Pricing modelTiered subscription with usage-based overage chargesfreemium
PlatformsWeb, Docker, KubernetesLinux
FoundedUnknown2014

Identical on both: starting price (Free), 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 Langwatch

  • Agent simulation testing
  • LLM evaluation
  • OpenTelemetry tracing
  • Langy AI Engineer
  • Governance controls
  • Multiple deployment options
  • Framework support

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.

Langwatch

  • Continuous testing of AI agents before production deploymentnot Seldon
  • Automated test creation from product requirementsnot Seldon
  • LLM response quality evaluation and scoringnot Seldon
  • Production agent monitoring and cost trackingnot Seldon
  • Governance and access control for AI systemsnot Seldon

Seldon

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

Where each one falls short

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

Langwatch

  • Free plan limited to 50k events per month, restricting larger deployments
  • Pricing in EUR may complicate budgeting for US-based teams
  • Usage-based overage model can create unpredictable costs
  • Self-hosted option requires DevOps expertise

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

Langwatch

Free
  • DeveloperFree
    • 50k events per month
    • 14-day data access
    • 2 users
  • Growth$29/month
    • 200k events per month included
    • 5 EUR per 100k additional events
    • 30-day data retention
  • Enterprise$undefined/custom
    • Custom event limits
    • Hybrid, self-hosted or on-premises deployment
    • Custom SSO and RBAC

Seldon

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

Which should you pick?

Choose Langwatch if

  • You need agent simulation testing.
  • You want to start without paying.
  • You work on Web, Docker, Kubernetes.
  • You also want llm evaluation.
Langwatch 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 Langwatch or Seldon better?
Neither clearly leads. Langwatch 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, Langwatch or Seldon?
Langwatch starts at Free and Seldon at Free.
Does Langwatch or Seldon run on more platforms?
Langwatch runs on Web, Docker, Kubernetes. Seldon runs on Linux.
Can I use Langwatch for free?
Both have a free tier, so you can try either at no cost before committing.
What is Langwatch best used for?
Langwatch is most often used for continuous testing of ai agents before production deployment, automated test creation from product requirements, llm response quality evaluation and scoring, production agent monitoring and cost tracking. Of those, continuous testing of ai agents before production deployment and automated test creation from product requirements are not what Seldon is typically brought in for.
What can Langwatch do that Seldon cannot?
Langwatch covers Agent simulation testing, LLM evaluation, OpenTelemetry tracing, Langy AI Engineer. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

Answered from the vendors’ own pages

Langwatch: Is there a permanent free tier?

Yes, Langwatch's Developer plan is free forever with 50k events per month, 14-day data access, 2 users, and no credit card required. It is specifically designed for individual developers prototyping AI applications.

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.

Langwatch: What is Langy and how does it save time?

Langy is an AI-powered tool that automates test creation. It converts product requirements into test scenarios, runs simulations, scores results, and generates pull requests with fixes in a median of 14 minutes.

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.

Langwatch: What frameworks does Langwatch support?

Langwatch works with LangGraph, LangChain, CrewAI, OpenAI Agents, AWS Bedrock, Azure OpenAI, Vertex AI, and other major LLM frameworks and platforms.

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.

Share

Related pages

More on Langwatch

More on Seldon

Keep looking

Other head to heads