Side-by-side comparison of AI visibility scores, market position, and capabilities
Data platform for security and observability acquired by Cisco for $28B in March 2024. Used by 90 of Fortune 100; 7,500+ enterprise customers globally; flagship SIEM and Splunk SOAR power enterprise security operations centers.
Splunk is a data platform for security and observability founded in 2003 in San Francisco, built on the idea that machine-generated data — logs, events, metrics, traces — contains the intelligence organizations need to detect threats, investigate incidents, and ensure digital systems stay available. The company's core technology indexes and searches massive volumes of machine data in real time, enabling security and IT operations teams to answer complex questions across their entire data estate without predefined schemas.\n\nSplunk's flagship product is its SIEM (Security Information and Event Management) platform, used by 90 of the Fortune 100 to detect and respond to security threats. Its broader portfolio includes Splunk Observability Cloud for infrastructure monitoring, Splunk SOAR for security orchestration and automated response, and Splunk IT Service Intelligence for IT operations. The platform's schema-on-read approach and SPL query language give analysts flexibility to investigate novel threats and operational issues that structured databases cannot accommodate.\n\nSplunk was acquired by Cisco for $28B in March 2024, one of the largest cybersecurity acquisitions in history, and has been integrated into Cisco's AI-driven security portfolio. The combination of Cisco's network telemetry and global customer relationships with Splunk's data analytics depth creates a powerful full-stack security and observability offering. Under Cisco, Splunk is adding AI-native features — including AI Assistant for SPL and automated threat detection — to maintain its leadership position as the SIEM market evolves toward AI-augmented security operations.
Serverless GPU cloud platform for AI/ML with Python-native deployment and per-second billing; developer-favorite scaling from zero competing with Replicate and Beam for AI compute.
Modal is a serverless cloud computing platform purpose-built for AI and machine learning workloads — providing on-demand GPU compute that scales instantly from zero with per-second billing, container management, distributed training support, and a Python-native developer experience that makes running ML workloads in the cloud feel as simple as running code locally. Founded in 2021 in New York City and backed by Redpoint Ventures and other investors, Modal has grown rapidly as AI development has accelerated demand for flexible, developer-friendly GPU infrastructure.\n\nModal's developer experience is its primary differentiator — engineers write Python functions decorated with @modal.function() and deploy them to the cloud with a single command, with Modal handling container building, GPU provisioning, auto-scaling, and execution. The platform supports training jobs that need distributed compute across multiple GPUs, model serving endpoints that scale to zero when unused (eliminating idle GPU costs), and batch inference jobs that process large datasets. The per-second billing model means developers pay only for actual compute time, not provisioned instances.\n\nIn 2025, Modal competes in the AI infrastructure market with Replicate, Beam, Banana, and major cloud providers' managed ML services (AWS SageMaker, Google Vertex AI, Azure ML) for serverless GPU compute. The market for AI-specific cloud infrastructure has grown dramatically as the number of ML engineers deploying models to production has expanded — traditional cloud providers require significant DevOps expertise to use GPU instances effectively, while Modal's Python-native approach reduces the barrier to entry. Modal has attracted a strong developer following among AI researchers and ML engineers building production AI applications. The 2025 strategy focuses on growing the developer community, adding enterprise features (dedicated GPU capacity, private networking, compliance), and expanding the hardware options available (H100 GPUs, custom accelerators).
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