--- title: "databuff vs gpu-telemetry" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/databufflabs-databuff-vs-last9-gpu-telemetry" tools: ["databufflabs-databuff", "last9-gpu-telemetry"] --- # databuff vs gpu-telemetry *GraphCanon updated Sep 20, 2026* ## Verdict Pick databuff if dataBuff is an AI-native open-source APM software that integrates OpenTelemetry standards to offer full-chain monitoring, service topology analysis, and AI assistance in problem-solving for cloud-native scenarios; pick gpu-telemetry if gpu-telemetry provides comprehensive GPU observability in Kubernetes and Slurm environments by tying hardware metrics to the workload causing them. [databuff](https://databuff.ai) reports 665 GitHub stars, 130 forks, and 11 open issues, last pushed Sep 10, 2026. [gpu-telemetry](https://last9.io/gpu-observability/) has 66 stars, 8 forks, and 5 open issues, last pushed Aug 2, 2026. Figures are from public GitHub metadata via [databuff's repository](https://github.com/databufflabs/databuff) and [gpu-telemetry's repository](https://github.com/last9/gpu-telemetry). | | [databuff](/tools/databufflabs-databuff.md) | [gpu-telemetry](/tools/last9-gpu-telemetry.md) | | --- | --- | --- | | Tagline | AI-native OpenTelemetry APM with multi-agent root-cause analysis | GPU Observability with Workload Attribution | | Stars | 665 | 66 | | Forks | 130 | 8 | | Open issues | 11 | 5 | | Language | Java | Python | | Adopt for | DataBuff is an AI-native open-source APM software that integrates OpenTelemetry standards to offer full-chain monitoring, service topology analysis, and AI assistance in problem-solving for cloud-native scenarios. | gpu-telemetry provides comprehensive GPU observability in Kubernetes and Slurm environments by tying hardware metrics to the workload causing them. | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0 | MIT | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [databuff](/tools/databufflabs-databuff.md) | [gpu-telemetry](/tools/last9-gpu-telemetry.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 39d | | Open issues (now) | 11 | 5 | | Stars delta | +138 (30d) | +9 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/databufflabs-databuff/trust.md) | [trust report](/tools/last9-gpu-telemetry/trust.md) | ## Decision facts: databuff - **Hosting:** self hosted - **Pricing:** freemium - Open-source under the AGPL-3.0 license, no cost for use but with obligations. - **Adopt for:** DataBuff is an AI-native open-source APM software that integrates OpenTelemetry standards to offer full-chain monitoring, service topology analysis, and AI assistance in problem-solving for cloud-native scenarios. - **License detail:** AGPL-3.0 ## Decision facts: gpu-telemetry - **Adopt for:** gpu-telemetry provides comprehensive GPU observability in Kubernetes and Slurm environments by tying hardware metrics to the workload causing them. ## Choose when ### Choose databuff if… - databuff is primarily Java; gpu-telemetry is Python. - License: databuff is AGPL-3.0, gpu-telemetry is MIT. - Pricing: Open-source under the AGPL-3.0 license, no cost for use but with obligations.. - Tags unique to databuff: ai, aiops, apm, devops. - Use DataBuff when you need AI-driven root-cause analysis capabilities across traces, metrics, and service topologies. ### Choose gpu-telemetry if… - gpu-telemetry is primarily Python; databuff is Java. - License: gpu-telemetry is MIT, databuff is AGPL-3.0. - Tags unique to gpu-telemetry: amd, gpu-monitoring, intel-gaudi-base-operator, kubernetes. - When monitoring NVIDIA, AMD, or Intel Gaudi GPUs in Kubernetes clusters. ## When NOT to use databuff - DataBuff may not be suitable when you require real-time eBPF APM capabilities, as this feature is still under development. - Do not use DataBuff if your monitoring requirements do not involve the use of AI to handle multiple agents and their coordination for complex problems. - If your project prefers proprietary observability solutions over open-source alternatives that enforce AGPL-3.0 licensing terms, DataBuff might not align with your project's goals. ## When NOT to use gpu-telemetry - If your infrastructure is not based on Kubernetes or Slurm. - When you prefer tools that do not require per-node OTLP agents. - For environments without support for NVIDIA, AMD, or Intel Gaudi GPUs. ## Common questions ### What is the difference between databuff and gpu-telemetry? databuff: AI-native OpenTelemetry APM with multi-agent root-cause analysis. gpu-telemetry: GPU Observability with Workload Attribution. See the comparison table for live GitHub stats and shared categories. ### When should I choose databuff over gpu-telemetry? Choose databuff over gpu-telemetry when databuff is primarily Java; gpu-telemetry is Python; License: databuff is AGPL-3.0, gpu-telemetry is MIT; Pricing: Open-source under the AGPL-3.0 license, no cost for use but with obligations.; Tags unique to databuff: ai, aiops, apm, devops; Use DataBuff when you need AI-driven root-cause analysis capabilities across traces, metrics, and service topologies. ### When should I choose gpu-telemetry over databuff? Choose gpu-telemetry over databuff when gpu-telemetry is primarily Python; databuff is Java; License: gpu-telemetry is MIT, databuff is AGPL-3.0; Tags unique to gpu-telemetry: amd, gpu-monitoring, intel-gaudi-base-operator, kubernetes; When monitoring NVIDIA, AMD, or Intel Gaudi GPUs in Kubernetes clusters. ### When should I avoid databuff? DataBuff may not be suitable when you require real-time eBPF APM capabilities, as this feature is still under development. Do not use DataBuff if your monitoring requirements do not involve the use of AI to handle multiple agents and their coordination for complex problems. If your project prefers proprietary observability solutions over open-source alternatives that enforce AGPL-3.0 licensing terms, DataBuff might not align with your project's goals. ### When should I avoid gpu-telemetry? If your infrastructure is not based on Kubernetes or Slurm. When you prefer tools that do not require per-node OTLP agents. For environments without support for NVIDIA, AMD, or Intel Gaudi GPUs. ### Is databuff or gpu-telemetry more popular on GitHub? databuff has more GitHub stars (665 vs 66). Stars measure visibility, not whether either tool fits your constraints. ### Are databuff and gpu-telemetry open source? Yes - both are open-source projects on GitHub (databuff: AGPL-3.0, gpu-telemetry: MIT). ### Where can I find alternatives to databuff or gpu-telemetry? GraphCanon lists graph-backed alternatives at [databuff alternatives](/tools/databufflabs-databuff/alternatives) and [gpu-telemetry alternatives](/tools/last9-gpu-telemetry/alternatives) ([databuff markdown twin](/tools/databufflabs-databuff/alternatives.md), [gpu-telemetry markdown twin](/tools/last9-gpu-telemetry/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/databufflabs-databuff-vs-last9-gpu-telemetry.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, databuff or gpu-telemetry? databuff: Very active. gpu-telemetry: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for databuff and gpu-telemetry? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [databuff trust report](/tools/databufflabs-databuff/trust); [gpu-telemetry trust report](/tools/last9-gpu-telemetry/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=databufflabs-databuff`](/api/graphcanon/graph?tool=databufflabs-databuff) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_