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Semantic Kernel (Microsoft)

Open Source

About Semantic Kernel (Microsoft)

Semantic Kernel is a development kit that lets developers build AI agents and integrate AI models into their codebase. It combines prompts with existing APIs to perform actions. It translates a model's request into a function call and passes results back to the model. Existing code can be added as a plugin and integrated with AI services through connectors.

Used for

  • Building AI agents that call functions to perform actions
  • Describing existing code to AI models so they can be called to address requests
  • Expanding chat-based APIs to support additional modalities like voice and video
  • Sharing extensions with other developers using OpenAPI specifications

Who it is for

  • Used by Microsoft and other Fortune 500 companies.
  • Used by developers working in C#, Python, or Java.
  • Used by pro or low-code developers within a company.

From Semantic Kernel (Microsoft)'s own website, read 2026-10-24. How we build listings.

How to evaluate LLM Infrastructure & APIs

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Semantic Kernel (Microsoft). It comes from our Generative AI & LLM Platforms buyer guide.

25%
Model Capability & Task Fit
Reasoning and instruction-following on your tasks (not public leaderboards), multimodal coverage (text, image, audio, video) where you need it, context-window length for your documents, breadth of the model menu (frontier, mid, small), and measured quality on a held-out set of your real prompts
20%
Deployment, Data Residency & Governance
Where inference physically runs (multi-tenant API, your VPC, on-prem, air-gapped), explicit no-training-on-your-data and retention commitments, regional residency, SOC 2 / ISO 27001 / HIPAA / FedRAMP and EU AI Act alignment, and DLP, content filtering, and full prompt/response audit logging
15%
Build & Customization Tooling
First-class retrieval-augmented generation and grounding, managed fine-tuning and (where relevant) continued pre-training, an integrated evaluation and regression-testing harness, prompt/version management, and native or tightly integrated vector search over your corpora
15%
Agentic & Orchestration Readiness
Reliable tool/function calling, support for open agent protocols (MCP, A2A), a managed agent runtime with memory and state, multi-agent orchestration, and guardrails — permissions, human-in-the-loop checkpoints, and step-level tracing for autonomous workflows
15%
Portability & Lock-in Risk
How model-agnostic the API is, whether multiple model families are reachable through one interface, the real cost of swapping providers (prompt, tool-schema, and eval rework), reliance on open standards, and whether you can take fine-tuned weights or your data with you on exit
10%
Cost, Throughput & Operability
Token economics at your projected volume, batch and provisioned-throughput options, prompt-caching support, rate limits and quota headroom for production, p95 latency under load, regional availability and uptime SLAs, and built-in usage, cost, and quality observability

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Quick Facts

learn.microsoft.com/en-us/semantic-kernel/overview
CategoryAI & ML Platforms
SubcategoryLLM Infrastructure & APIs
PricingOpen source

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