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MLOps
Enterprise MLOps Stack
Track experiments, serve models at scale, and monitor LLM application quality: the infrastructure stack for teams shipping ML and AI to production.
Our Take
Built for an enterprise team running models in production with real uptime and compliance requirements, not a team still experimenting. Amazon Bedrock and Azure OpenAI are alternative model-hosting layers tied to whichever cloud your infrastructure already runs on, you'll pick one based on existing vendor commitments rather than run both. Weights & Biases and LangSmith cover experiment tracking and LLM-specific observability respectively, both are the kind of infrastructure you regret not having once something goes wrong in production.
True Cost & Exit
What this stack really costs to run, and how you'd leave it.
Monthly cost
from $99/mo fixed (Weights & Biases Pro + LangSmith Plus), plus usage-based model-hosting costs on Amazon Bedrock, Azure OpenAI, or Replicate that scale with inference volume and have no fixed floor
Lean alternative
Pick Amazon Bedrock or Azure OpenAI, not both, based on your existing cloud commitment; Weights & Biases and LangSmith are the fixed-cost pieces worth budgeting for, model-hosting cost scales entirely with inference volume.
Exit path
Experiment and trace history lives in Weights & Biases and LangSmith respectively, neither of which publishes a formal data-export policy in this catalog; model weights and fine-tuning artifacts on Bedrock or Azure OpenAI stay within that cloud provider's ecosystem, so factor cloud migration cost into any provider-switch decision, not just the AI tooling cost.
Tools in This Stack
Track, visualize, and compare machine learning experiments, with newer Weave and Inference tools for evaluating and monitoring LLM-based applications.
Debug, test, and monitor LLM applications and agents in production with LangChain's observability platform, billed by trace volume and seats.
Get API access to foundation models from multiple providers, plus fine-tuning and agent tools, without managing infrastructure. New Priority and Flex service levels added alongside On-Demand and Provisioned Throughput.
Access GPT and other OpenAI models through Azure with enterprise compliance, networking, and regional data controls. Now offers Global, Data Zone, and Regional deployment types.
Run and deploy machine learning models via API with per-second usage billing and no idle costs for public models.