Comet vs Hopsworks AI Lakehouse

Comet offers two product families on one platform: Opik for observing and evaluating LLM applications and agents, and MLOps for machine-learning workflows. Opik traces execution paths such as context retrieval, model responses and tool calls. Its evaluation tools include test suites, datasets, experiments, LLM-as-a-judge and heuristic metrics. The product lists integrations with more than 40 AI frameworks, model providers and gateways, including LangChain, OpenAI, Google ADK, LangGraph and CrewAI; OpenTelemetry support covers tracking applications in Ruby, Java and other languages. MLOps provides experiment tracking, dataset management, a model registry and production monitoring. Opik's open-source version can be self-hosted, and Enterprise offers flexible deployment options. Free Cloud allows up to 10 team members, 25k spans per month and 60-day data retention. Pro Cloud costs $19 a month and allows up to 50 team members and 100k spans monthly, with the same retention period. MLOps Free is also listed, while MLOps Pro costs $19 per user/month. Enterprise pricing is custom. Comet lists community support for all plans, email support for paid cloud Pro and Enterprise, and dedicated support and SLAs for Enterprise.

Hopsworks AI Lakehouse is a platform for building batch, real-time, and LLM AI systems using open standards. It supports Apache Iceberg, Delta Lake, and Apache Hudi, with streaming feature pipelines that feed a low-latency serving database and provide sub-second feature freshness. Its Query Service transfers data to Python clients using Arrow and supports temporal joins, filter pushdown, credential vending, and reproducible training data. For LLM workflows, teams can query lakehouse data from prompts, integrate vector search for retrieval-augmented generation, and create fine-tuning instruction datasets while maintaining data governance. Feature, training, and inference pipelines cover batch, streaming, real-time, and LLM work, with trained models sent to a model registry. Integrations include Airflow, Apache Flink, Databricks, Snowflake, AWS SageMaker, Spark, Vertex AI, and Weights & Biases. Deployment options include SaaS, managed Kubernetes on AWS, Azure, or GCP, and air-gapped data centres. The Free plan is free and includes one project, Feature Store, Model Registry, and community support; SaaS uses usage-based pricing, while Enterprise is custom-priced.