1. Data Indexing: Splunk primarily indexes and searches log files and other types of machine-generated data, while Vespa focuses on real-time indexing and serving of structured and unstructured data, making it more versatile for a broader range of applications.

2. Query Language: Splunk uses its proprietary search processing language (SPL) for querying data, whereas Vespa provides its own query language (Vespa Query Language) that is optimized for handling complex hierarchical data structures.

3. Scalability: Splunk is known for its scalable architecture, but Vespa is specially designed to handle massive data sets and high-traffic applications, making it an ideal choice for businesses with demanding scalability requirements.

4. Open Source: Splunk offers both open-source and enterprise versions, while Vespa is entirely an open-source project. This difference can impact the cost and level of customization available to users.

5. Deep Learning Capabilities: Vespa integrates deep learning models directly into its serving infrastructure, enabling real-time machine learning in production systems, whereas Splunk relies more on integration with external machine learning frameworks for similar functionalities.

6. Ecosystem: Splunk has a rich ecosystem of plugins, apps, and community support, while Vespa, being a more specialized platform, has a smaller but growing ecosystem that caters to specific use cases such as recommendation systems or content serving.

In Summary, when comparing Splunk and Vespa, key differences include data indexing focus, query language usage, scalability capabilities, open-source availability, deep learning integration, and ecosystem support.