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Evaluates model performance, manages prompt versions, and monitors LLM behavior in production environments.
LLMOps Engineer
Each stage transforms your work — output of one feeds the next.
Input
Unorganized prompts scattered across app codebases
AI process
Evaluates outputs side-by-side using language models to auto-grade prompt changes
Output
A centralized, version-controlled prompt registry
Input
A centralized, version-controlled prompt registry
AI process
Executes test cases and scores outputs for accuracy, tone, and hallucination rates
Output
A detailed matrix of model performance and alerts
Input
A detailed matrix of model performance and alerts
AI process
Analyzes telemetry data to detect anomalies, categorize intent, and flag attacks
Output
Real-time dashboards pinpointing latency spikes
Vellum for iterates prompts across multiple foundation models·PromptLayer for logs, tracks, and manages prompt deployments
Developer platform to experiment with, evaluate, and deploy LLM prompts.
Starts around $99/mo depending on usage
Platform for prompt management, tracking, and collaboration for LLMs.
Free up to 10k requests/mo, paid plans scale with usage
Promptfoo for runs automated ci evaluations to catch llm regressions·Braintrust for performs evaluations using custom scoring criteria
Open-source CLI and library for evaluating LLM prompts and models.
100% Free and open-source
Enterprise-grade evaluation and prompt engineering platform.
Custom enterprise pricing
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LangSmith for traces complex agent workflows to debug steps·Langfuse for monitors token usage, latency, and user feedback·Helicone for tracks api costs and caches repetitive requests
Platform for debugging, testing, and evaluating LLM applications.
Free developer tier, paid tiers for teams
Open-source LLM engineering platform for traces, evals, and prompts.
Open-source/free tier available, cloud plans scale with usage
Open-source LLM observability platform with prompt tracking.
Free up to 100k requests/mo, Pro at $50/mo
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