Built on Best-in-Class Partners

KubeNiche operates multi-model deployments across frontier and open-weight providers — OpenAI GPT-4o, Anthropic Claude 3.5, Google Gemini 1.5 Pro, AWS Bedrock, and Azure OpenAI — unified behind a LiteLLM routing layer. Every model call is policy-governed, cost-attributed per team or agent, and subject to hard token budget enforcement. Model selection is driven by task profile and cost-per-token targets, not vendor lock-in.

KubeNiche designs, deploys, and operates autonomous AI agent systems on Kubernetes using LangGraph for stateful multi-agent workflows, CrewAI for role-based agent crews, and AutoGen for conversational agent loops. Every agent deployment includes GitOps-managed rollout via ArgoCD, per-agent token cost attribution, observability hooks, and SLA-backed uptime. We treat agents as production services — not experiments.

The Model Context Protocol (MCP) is the emerging standard for giving AI agents governed, auditable access to tools, APIs, and data sources. KubeNiche deploys and operates MCP servers as managed Kubernetes services — with Kong AI Gateway or Portkey as the policy enforcement and rate-limiting layer in front. Every tool call is logged, attributed, and subject to per-agent access controls. This is the infrastructure layer that makes agentic AI safe to run in production.

KubeNiche deploys and manages purpose-built vector databases for Custom RAG AI Agents — Qdrant and Weaviate on Kubernetes for self-hosted workloads, Pinecone for serverless retrieval at scale, and pgvector for teams already on PostgreSQL. Every deployment includes automated backup, index monitoring, embedding pipeline management, and cost attribution back to the consuming agent or team. RAG infrastructure is treated as a first-class managed service, not a side project.

KubeNiche deploys and operates LangSmith for LLM trace capture and evaluation, OpenLLMetry for OpenTelemetry-native LLM spans, and Arize AI for model performance monitoring — all feeding into Flexera One's AI Cost Management module and our FinOps CoPilot. The result is a single pane of glass: every token, every trace, every dollar of AI spend attributed to a team, product, or agent. Weights & Biases covers experiment tracking for teams iterating on fine-tuned or custom models.