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Machine Learning · head to head

Semantic Kernel vs Traceloop

Semantic Kernel logo

Semantic Kernel

Machine Learning

Model-agnostic SDK for AI orchestration

From
Free
Rated
-
Full reviewWebsite
Traceloop logo

Traceloop

Logging

LLM reliability platform with open-source observability and evaluation

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Semantic Kernel steep learning curve for advanced features; Traceloop free tier limited to 50k spans per month and 24-hour retention, restricting production use
  • They diverge on capability: Semantic Kernel covers Multi-model support, Traceloop covers Open-source SDK (OpenLLMetry).
  • Prices and features above were last checked on 29 August 2026.

Where they differ

Only the attributes on which Semantic Kernel and Traceloop actually diverge.

Attributes where Semantic Kernel and Traceloop differ
AttributeSemantic KernelTraceloop
Pricing modelOpen source, no pricingFreemium with pay-as-you-go enterprise option
PlatformsPython, .NET, JavaCloud, On-premises, Air-gapped, Python, TypeScript, Go, Ruby
CategoryMachine LearningLogging

Identical on both: starting price (Free), free tier (Yes), 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 Semantic Kernel

  • Multi-model support
  • Agent framework
  • Multi-agent systems
  • Plugin ecosystem
  • Vector database integration
  • Multimodal support
  • Local model support
  • Enterprise observability

Only in Traceloop

  • Open-source SDK (OpenLLMetry)
  • Multi-provider support
  • Observability platform integration
  • Framework support
  • Monitoring dashboard
  • Evaluation system
  • Deployment flexibility

What people use each for

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

Semantic Kernel

  • Building enterprise AI applications with LLM integrationnot Traceloop
  • Creating multi-agent systems for complex workflowsnot Traceloop
  • Developing AI-powered chatbots and assistantsnot Traceloop
  • Implementing RAG systems with vector databasesnot Traceloop

Traceloop

  • Monitoring LLM application performance in productionnot Semantic Kernel
  • Instrumenting LLM apps with minimal code overheadnot Semantic Kernel
  • Continuous evaluation and quality scoring of LLM outputsnot Semantic Kernel
  • Debugging LLM application issues with full trace visibilitynot Semantic Kernel
  • Integrating observability data into existing monitoring stacksnot Semantic Kernel

Where each one falls short

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

Semantic Kernel

  • Steep learning curve for advanced features
  • Documentation focuses on Azure cloud services
  • Configuration complexity for multi-model scenarios
  • Requires understanding of AI/LLM concepts

Traceloop

  • Free tier limited to 50k spans per month and 24-hour retention, restricting production use
  • Company acquisition by ServiceNow creates uncertainty about future roadmap
  • Requires integration with separate observability platforms for visualization
  • Less feature-rich than dedicated LLM evaluation platforms

Pricing, plan by plan

Semantic Kernel

Free
  • Open SourceFree
    • MIT license
    • Full framework access
    • All language SDKs

Traceloop

Free
  • FreeFree
    • 50,000 spans per month
    • Up to 5 seats
    • 24-hour data retention
  • Enterprise$undefined/custom
    • Unlimited spans per month
    • Unlimited seats
    • Custom data retention

Which should you pick?

Choose Semantic Kernel if

  • You need multi-model support.
  • You want to start without paying.
  • You work on Python, .NET, Java.
  • You also want agent framework.
Semantic Kernel review

Choose Traceloop if

  • You need open-source sdk (openllmetry).
  • You want to start without paying.
  • You work on Cloud, On-premises, Air-gapped, Python, TypeScript, Go, Ruby.
  • You also want multi-provider support.
Traceloop review

Questions people ask

Is Semantic Kernel or Traceloop better?
Neither clearly leads. Semantic Kernel starts at Free and Traceloop at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Semantic Kernel or Traceloop?
Semantic Kernel starts at Free and Traceloop at Free.
Does Semantic Kernel or Traceloop run on more platforms?
Semantic Kernel runs on Python, .NET, Java. Traceloop runs on Cloud, On-premises, Air-gapped, Python, TypeScript, Go, Ruby.
Can I use Semantic Kernel for free?
Both have a free tier, so you can try either at no cost before committing.
What is Semantic Kernel best used for?
Semantic Kernel is most often used for building enterprise ai applications with llm integration, creating multi-agent systems for complex workflows, developing ai-powered chatbots and assistants, implementing rag systems with vector databases. Of those, building enterprise ai applications with llm integration and creating multi-agent systems for complex workflows are not what Traceloop is typically brought in for.
What can Semantic Kernel do that Traceloop cannot?
Semantic Kernel covers Multi-model support, Agent framework, Multi-agent systems, Plugin ecosystem. Traceloop covers Open-source SDK (OpenLLMetry), Multi-provider support, Observability platform integration, Framework support.

Answered from the vendors’ own pages

Semantic Kernel: What LLM providers does Semantic Kernel support?

Semantic Kernel supports OpenAI, Azure OpenAI, Hugging Face, Nvidia, and other providers through extensible model implementations.

Source
Traceloop: Is OpenLLMetry open-source?

Yes, OpenLLMetry is Traceloop's open-source SDK built on OpenTelemetry standards. It allows teams to instrument LLM applications with just 2 lines of code and send data to 25+ observability platforms.

Source
Semantic Kernel: Can I run Semantic Kernel locally?

Yes. Semantic Kernel supports local models through Ollama, LMStudio, and ONNX for complete data control and offline operation.

Source
Traceloop: What is the impact of ServiceNow acquisition?

Traceloop is joining ServiceNow, representing a strategic acquisition that will broaden enterprise adoption and integration capabilities. Current operations continue with free and enterprise options available.

Source
Semantic Kernel: Is Semantic Kernel free?

Yes. Semantic Kernel is MIT-licensed open source and completely free. You only pay for external LLM APIs you use.

Source
Traceloop: How many LLM providers and frameworks does Traceloop support?

Traceloop supports 20+ LLM providers including OpenAI and Anthropic, and integrates with frameworks like LangChain and LlamaIndex. It can send data to 25+ observability platforms.

Source
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