LangSmith vs Routerly

LangSmith is a commercial agent-engineering platform for building, evaluating, deploying, monitoring, and improving AI agents. It supports offline evaluations on curated datasets and real-time scoring of production interactions. Evaluation methods include human review, heuristic checks, LLM-as-judge evaluators, pairwise comparisons, and custom Python or TypeScript evaluators. Annotation queues route runs to subject-matter experts for feedback that can calibrate evaluations, improve prompts, or augment datasets. The Playground compares prompt versions or model providers, and LangSmith chat can auto-improve prompts. Observability includes agent and retrieval tracing, token and general cost tracking, trajectory monitoring, online evaluations, and webhook or PagerDuty alerts. Traces can arrive through SDKs or an API from LangGraph, custom Python, or other stacks; SDKs are listed for Python, TypeScript, Go, and Java. Hosting ranges from cloud to hybrid and self-hosted Enterprise options. The Developer plan costs nothing, Plus costs $39 a month, and Enterprise pricing is custom.

Routerly is a self-hosted gateway for directing application requests to AI providers, tracking usage costs, and applying budgets. Its routing engine can use up to nine configurable policies, covering model choice, cost, provider health and performance, capability and context, budgets, rate limits, and fairness. It supports OpenAI and Anthropic wire formats, including streaming, so compatible clients can connect by changing the base URL. Listed providers include OpenAI, Anthropic, Google Gemini, Ollama, Mistral, Cohere, xAI, and custom HTTP endpoints. Per-token costs can use built-in pricing presets or custom model prices. Budgets can measure cost, calls, or token usage globally, per project, or per token, over rolling or calendar windows. Projects can have separate bearer tokens, model pools, routing configurations, and budget limits. Routerly stores configuration and usage records in JSON files without requiring an external database. It can be deployed with Docker or an installer. The self-hosted plan costs nothing, carries the AGPL-3.0 license, and adds no markup to API calls.