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groundcover vs Langfuse

Langfuse targets LLM app analytics, whereas Groundcover provides full-stack cloud observability for any workload.
groundcoverObservability, for the Cloud.LangfuseOpen Source Observability & Analytics for LLM Apps 🕵️‍♂️

Side by side

What it is
groundcover:Groundcover provides full-stack cloud observability with logs, metrics, traces and Kubernetes events in a single platform.
Langfuse:Langfuse is an open-source platform for tracing, evaluating and monitoring LLM applications and AI agents.
Best for
groundcover:Full-stack observability with BYOC architecture
Langfuse:LLM observability & analytics
Who it’s for
groundcover:Site reliability engineers, DevOps teams and cloud engineers needing unified observability.
Langfuse:Developers building, debugging and scaling LLM-based apps and agents.
Pricing
groundcover:Freemium
Langfuse:Freemium
Plans
groundcover:Free $0 · Pro $30 per host, per month · Enterprise $35 per host, per month · On Premise $50 per host, per month
Langfuse:Hobby Free · Core $29 per month · Pro $199 per month · Enterprise $2499 per month
Open source
groundcover:—
Langfuse:Yes, 35,097 GitHub stars
Works with
groundcover:OpenTelemetry, Kubernetes, AWS, Google Cloud Platform, Microsoft Azure, Slack, Linear, RUM
Langfuse:Python, TypeScript, Go, Java, .NET, Ruby, PHP, Swift
DevHunt upvotes
groundcover:39
Langfuse:89
Launched on DevHunt
groundcover:Feb 2024
Langfuse:Jan 2023

groundcover features

  • BYOC Architecture. Deploys inside your VPC so all telemetry stays private and secure.
  • eBPF Sensor. Zero-instrumentation data collection across infrastructure, apps and AI workloads.
  • Agent Mode. AI-driven assistant that runs queries, builds dashboards and automates actions.
  • Plain-language Explorer. Search and investigate data using natural language without query syntax.
  • AI Observability. Dedicated view for LLM calls and agent pipelines with cost and performance metrics.
  • Integrated Alerts & Workflows. Monitors, Slack and Linear connectors route alerts and create tickets automatically.
  • Cost-predictable Pricing. Per-host pricing based on average Kubernetes nodes, no ingestion fees.
  • Multi-cloud Support. Runs on AWS, GCP and Azure marketplaces with optional on-prem deployment.
groundcover on DevHunt →Alternatives

Langfuse features

  • Hierarchical Traces. Capture every LLM call, tool invocation and retrieval step with filters for user, session, cost and latency.
  • Prompt Management. Version, fetch, release and cache prompts separately from code with one-click deployments.
  • Evaluation Engine. Run LLM-as-judge, heuristic or human-review evaluations on production data or experiments.
  • Experiments & Datasets. Define test cases, run experiments and create golden datasets for continuous improvement.
  • Dashboards & Alerts. Monitor cost, latency and quality via custom dashboards and automated alerts.
  • Human Annotation. Collaborative human-in-the-loop workflows with annotation queues.
  • Extensive Integrations. Supports Python, TypeScript, Go, Java, .NET, Ruby, PHP, Swift and 100+ agent frameworks and model providers.
  • Self-hosted & Cloud Options. Available as hosted SaaS or self-hosted under MIT license.
Langfuse on DevHunt →Alternatives

Based on each tool's website and DevHunt data. Details may change; check the official sites.