PeriFlow
generalOptimize large language model inference for cost and speed
llms 369
This category covers tools built around large language models: infrastructure for deploying, fine-tuning, evaluating, and monitoring LLMs in production. With 369 tools listed, it is one of the more technical categories on the site, aimed primarily at developers and ML engineers rather than end users.
Dstack
generalControl plane for GPU provisioning across clouds and clusters
Cognee
generalKnowledge graph platform for enterprises
UpTrain
generalEvaluate and monitor LLM applications
Gestell
generalConvert unstructured data into searchable databases for AI
LochBot
generalCheck LLM prompts against 31 jailbreak and injection patterns
AIWatch
generalReal-time status dashboard for 30+ AI services
Embedding Similarity Calculator
generalCompare vector embeddings using multiple distance metrics
Credyt
generalUsage-based monetization and billing for AI products
LLM tooling has exploded alongside the models themselves, and the category now spans several distinct problem areas. Deployment and serving tools like PeriFlow and Dstack help teams run models efficiently at scale. Evaluation and observability tools like UpTrain and AIWatch track model quality, drift, and cost over time. Memory and retrieval tools like Cognee add persistent context or RAG capabilities to LLM applications. When choosing, the key questions are infrastructure fit (cloud, on-prem, or hybrid), model compatibility (OpenAI-only vs. open-weight models), and whether the tool addresses your actual bottleneck, whether that is latency, cost, accuracy, or developer velocity. Pricing structures vary: some tools charge per token processed, others per seat or per API call. Open-source options exist across most sub-categories, which is worth considering for teams with engineering capacity to self-host.