dstack SSH fleets vs Langfuse

Langfuse provides LLM observability, while dstack manages compute and job scheduling.

Side by side

What it is: dstack SSH fleets:dstack provides an open-source orchestration layer to manage AI workloads across GPU clouds, Kubernetes, VMs, and bare-metal via SSH fleets.; Langfuse:Langfuse is an open-source platform for tracing, evaluating and monitoring LLM applications and AI agents.
Best for: dstack SSH fleets:Unified AI workload orchestration; Langfuse:LLM observability & analytics
Who it’s for: dstack SSH fleets:AI engineers, data-center operators, and teams using heterogeneous on-prem or cloud GPU compute.; Langfuse:Developers building, debugging and scaling LLM-based apps and agents.
Pricing: dstack SSH fleets:Open source; Langfuse:Freemium
Plans: dstack SSH fleets:—; Langfuse:Hobby Free · Core $29 per month · Pro $199 per month · Enterprise $2499 per month
Open source: dstack SSH fleets:Yes, 2,261 GitHub stars; Langfuse:Yes, 35,097 GitHub stars
Works with: dstack SSH fleets:GPU clouds, Kubernetes, Slurm, bare-metal clusters; Langfuse:Python, TypeScript, Go, Java, .NET, Ruby, PHP, Swift
DevHunt upvotes: dstack SSH fleets:0; Langfuse:89
Launched on DevHunt: dstack SSH fleets:Jun 2025; Langfuse:Jan 2023

SSH Fleets. Connect and provision VMs or bare-metal servers over SSH for AI training and inference.
Kubernetes Backend. Integrate existing Kubernetes clusters to schedule containerized AI jobs.
Slurm Backend. Experimental support for Slurm clusters, submitting runs as Slurm jobs.
Tasks Scheduling. Schedule training and other AI jobs with first-class compute primitives.
Services. Cache-aware, PD-disaggregated inference serving with auto-scaling.
Presets. Agent-based optimization toolkit for workload configuration.
Projects. Tenant isolation and usage metering for multi-tenant AI labs.
Gateways. HTTPS endpoints with domain, rate-limit, and auto-scaling support.

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.

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