Comparison
kubeshark vs Awesome-LLMOps
Verdict
Pick kubeshark if kubeshark is an eBPF-powered network observability tool for Kubernetes that offers full L4/L7 traffic indexing and TLS decryption without needing keys; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Markdown twin · kubeshark alternatives · Awesome-LLMOps alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | kubeshark | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (5d since push) As of 4w · github_public_v1 | Slowing (91d since push) As of 3d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4w · github_public_v1 | Not a fork · Organization account As of 3d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- kubeshark
- eBPF-powered network observability for Kubernetes
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- kubeshark
- 12k
- Awesome-LLMOps
- 5.9k
Forks
- kubeshark
- 542
- Awesome-LLMOps
- 993
Open issues
- kubeshark
- 141
- Awesome-LLMOps
- 247
Language
- kubeshark
- Go
- Awesome-LLMOps
- Shell
Adopt for
- kubeshark
- Kubeshark is an eBPF-powered network observability tool for Kubernetes that offers full L4/L7 traffic indexing and TLS decryption without needing keys.
- Awesome-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- kubeshark
- -
- Awesome-LLMOps
- -
Runtime
- kubeshark
- -
- Awesome-LLMOps
- -
License
- kubeshark
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- kubeshark
- Jul 20, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- kubeshark
- Evaluation & Observability
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- kubeshark
- Very active (96%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- kubeshark
- 5d
- Awesome-LLMOps
- 91d
Open issues (now)
- kubeshark
- 141
- Awesome-LLMOps
- 247
Stars delta
- kubeshark
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- kubeshark
- Unknown
- Awesome-LLMOps
- +66 (30d)
Full report
- kubeshark
- Trust report
- Awesome-LLMOps
- Trust report
Choose kubeshark if…
- kubeshark is primarily Go; Awesome-LLMOps is Shell.
- License: kubeshark is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to kubeshark: cloud-native, devops, docker, ebpf.
- - When you need to perform deep inspection of Kubernetes traffic on both layer 4 (transport) and layer 7 (application), with contextual information from the Kubernetes environment
When NOT to use kubeshark
- - If your primary focus is on a different cloud platform as Kubeshark is deeply integrated with Kubernetes and provides its full context along side traffic observations
- - When the system does not support eBPF, which is critical for Kubeshark's operation
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; kubeshark is Go.
- License: Awesome-LLMOps is CC0-1.0, kubeshark is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (kubeshark/kubeshark) · observed Jul 26, 2026
- GitHub forks (kubeshark/kubeshark) · observed Jul 26, 2026
- Last push (kubeshark/kubeshark) · observed Jul 20, 2026
- License file (Apache-2.0) · observed Jul 26, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: kubeshark 12k · Awesome-LLMOps 5.9k (synced Jul 26, 2026).
Common questions
- What is the difference between kubeshark and Awesome-LLMOps?
- kubeshark: eBPF-powered network observability for Kubernetes. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose kubeshark over Awesome-LLMOps?
- Choose kubeshark over Awesome-LLMOps when kubeshark is primarily Go; Awesome-LLMOps is Shell; License: kubeshark is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to kubeshark: cloud-native, devops, docker, ebpf; - When you need to perform deep inspection of Kubernetes traffic on both layer 4 (transport) and layer 7 (application), with contextual information from the Kubernetes environment.
- When should I choose Awesome-LLMOps over kubeshark?
- Choose Awesome-LLMOps over kubeshark when Awesome-LLMOps is primarily Shell; kubeshark is Go; License: Awesome-LLMOps is CC0-1.0, kubeshark is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid kubeshark?
- - If your primary focus is on a different cloud platform as Kubeshark is deeply integrated with Kubernetes and provides its full context along side traffic observations - When the system does not support eBPF, which is critical for Kubeshark's operation
- When should I avoid Awesome-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is kubeshark or Awesome-LLMOps more popular on GitHub?
- kubeshark has more GitHub stars (12,014 vs 5,915). Stars measure visibility, not whether either tool fits your constraints.
- Are kubeshark and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (kubeshark: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to kubeshark or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at kubeshark alternatives and Awesome-LLMOps alternatives (kubeshark markdown twin, Awesome-LLMOps markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, kubeshark or Awesome-LLMOps?
- kubeshark: Very active. Awesome-LLMOps: Slowing. 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 kubeshark and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: kubeshark trust report; Awesome-LLMOps trust report.