Following all the hype and bluster with DeepSeek’s arrival in the AI landscape––and its ability to crash the poster child of AI’s share value overnight (Nvidia), we wanted to conduct a rigorous evaluation at Komodor. We tested DeepSeek’s models head-to-head against industry leaders in solving real-world Kubernetes challenges. The results were nothing short of fascinating and quite revealing, particularly regarding DeepSeek’s current capabilities in production environments. Just for some context on the experiment. For those unfamiliar, one of the areas we have been trying to solve at Komodor is truly practical AIOps that solves real problems, and isn’t just fluff. I’ve written about this extensively on LinkedIn and our blog. We have been investing many hours of research and development to build an AI troubleshooting agent, with a specific focus on streamlining root cause analysis. We have discovered that from version to version AI agents are becoming increasingly sophisticated, and are able to reason with greater nuance and deliver truly astounding results. Our evaluation in the Komodor Lab environment covered four key dimensions: Production Scenarios: Our benchmark included a few distinct Kubernetes incidents, scaling from basic pod failures to complex cross-service problems. Systematic Framework: Each AI model faced identical scenarios, measuring: Time to identify issues Root cause accuracy Remediation quality Complex failure handling Data Integration: The AI agent leverages a sophisticated RAG system accessing: Live Kubernetes cluster data Komodor's platform insights Cloud infrastructure states Structured Prompting: A context-aware instruction framework that adapts based on the environment, incident type, and available data, ensuring methodical troubleshooting and standardized outputs Models Evaluated To assess the effectiveness of AI-assisted Kubernetes troubleshooting, we selected models from different ecosystems: Claude 3.5 Sonnet v2 (via AWS Bedrock) LLaMA 3.3-70B (via AWS Bedrock) DeepSeek-R1 (via Hugging Face) DeepSeek-V3 (via Hugging Face) The testing scenarios ultimately caused DeepSeek to crash, and therefore it was excluded from more advanced test scenarios. Scenario 1: Configuration Validation Test Case: Invalid ConfigMap in Traefik deploymentIn this scenario, a service (Traefik) failed to start due to incorrect YAML indentation in a dependent ConfigMap. Observations from Each Model: Claude 3.5 and LLaMA 3.3-70B quickly identified the root cause: a malformed ConfigMap due to YAML syntax errors, pinpointing the faulty key indentation. DeepSeek-V3 failed completely, misdiagnosing the issue. DeepSeek-R1 could not complete the analysis, leaving the user without actionable insights. You can see in Scenario #1's image that DeepSeek completely fails, while Claude and LLaMA correctly diagnose the YAML issue. Claude gave a better recommendation, regardless. Takeaway: YAML validation is critical for Kubernetes debugging, and both Claude and LLaMA showed superior understanding. Scenario 2: Application-Level Diagnostics Test Case: Pods running, but the application is failingIn this test, the service deployed correctly, but the application kept throwing HTTP 400 errors due to missing request parameters. Observations from Each Model: Claude 3.5 and LLaMA 3.3-70B correctly analyzed pod logs, identified that the application was missing customer_id and operation_name in API calls, and recommended fixing request parameters. You can see in Scenario #2's image that Claude and LLaMA likewise diagnosed the missing parameters issue, with Claude being slightly more accurate. Scenario 3: Resource Management and Over-Provisioning Test Case: Deployment requests too many resources, leaving pods in a pending stateThis test involved a working deployment that was modified with extreme resource requests (1K CPU, 128GB RAM), making scheduling impossible. Observations from Each Model: Claude 3.5 and LLaMA 3.3-70B correctly diagnosed the problem, identified the excessive resource requests, and recommended rolling back to previous values. You can see in Scenario #3's image that Claude and LLaMA correctly diagnose excessive CPU/memory requests, although Claude gave a much better explanation and call to action Takeaway: Kubernetes resource scheduling issues are complex and not easy for AI to diagnose. Cost Analysis: Open-Source Disruption and the DeepSeek Factor One of the most compelling aspects of DeepSeek’s rise has been the open-source hype surrounding it. Unlike OpenAI’s proprietary models or Amazon’s tightly integrated Bedrock offerings, DeepSeek represents a growing movement toward fully open and community-driven AI. Many believe that models like DeepSeek could be the key to disrupting the AI ecosystem entirely, providing an alternative that is both accessible and cost-efficient. This argument isn’t just theoretical. Open-source AI has already changed the landscape in many ways, from Hugging Face’s democratization of LLMs to Meta’s release of LLaMA models, which pushed generative AI into enterprise and research settings without the need for locked-in vendor pricing. The idea that DeepSeek, an open-source model, could eventually outcompete OpenAI, Claude, and other closed solutions isn’t far-fetched. If it reaches performance parity, the advantages in customization, cost, and deployment flexibility would make it the default choice for many businesses. To evaluate this factor, we compared DeepSeek against other open solutions, particularly LLaMA 3.3-70B, which has already been widely adopted due to its favorable cost-to-performance ratio. A crucial consideration when deploying AI-powered troubleshooting at scale is pricing: Claude 3.5 Sonnet: $0.003 (input) / $0.015 (output) LLaMA 3.3 Instruct (70B): $0.00072 (both input/output) LLaMA delivers good performance at a fraction of the cost, making it an option for production-scale Kubernetes troubleshooting. However, DeepSeek, despite being open-source, did not demonstrate the same level of accuracy or troubleshooting capability in our benchmarks. Final Results: Who Came Out on Top? Claude 3.5 maintained its leadership position, delivering the most accurate and actionable results. LLaMA 3.3 showed impressive capabilities, nearly matching Claude in accuracy and efficiency. DeepSeek-V3 struggled significantly, failing most test cases. DeepSeek-R1 couldn’t complete the analysis, making it unusable for real-world Kubernetes troubleshooting. Key Takeaways The current DeepSeek implementations aren’t yet ready for production troubleshooting in Kubernetes, as they struggle to match the accuracy and reliability of more mature models. In contrast, LLaMA 3.3-70B has shown highly promising results, delivering near-parity performance with Claude at a significantly lower cost. AI-assisted troubleshooting is evolving rapidly, and as these models continue to improve, their impact on Kubernetes operations will only grow. At Komodor, we are constantly refining Klaudia’s AI-powered diagnostics to make Kubernetes troubleshooting faster, more reliable, and more cost-efficient. We’re curious to hear from others—what has been your experience with these models, and which AI assistant do you rely on for Kubernetes troubleshooting?
