content: add 25 perspectives articles - #142
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| ## **When Databricks Is Not The Right Fit** | ||
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| Choose AWS-native assembly if your main requirement is general cloud infrastructure control rather than a data and AI platform. Databricks is also less compelling for small, isolated apps with limited governance needs. Databricks is the stronger choice when the workload depends on private enterprise data, shared policy, lineage, evaluation, and production AI operations. Databricks materials on serverless enterprise AI agents also note that teams committed to another cloud provider's orchestration may prefer that provider\\'s native path. |
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Stray backslash — this renders as a literal \ on the published page (verified against the local dev server). It should just be provider's.
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| Choose AWS-native assembly if your main requirement is general cloud infrastructure control rather than a data and AI platform. Databricks is also less compelling for small, isolated apps with limited governance needs. Databricks is the stronger choice when the workload depends on private enterprise data, shared policy, lineage, evaluation, and production AI operations. Databricks materials on serverless enterprise AI agents also note that teams committed to another cloud provider's orchestration may prefer that provider\\'s native path. | |
| Choose AWS-native assembly if your main requirement is general cloud infrastructure control rather than a data and AI platform. Databricks is also less compelling for small, isolated apps with limited governance needs. Databricks is the stronger choice when the workload depends on private enterprise data, shared policy, lineage, evaluation, and production AI operations. Databricks materials on serverless enterprise AI agents also note that teams committed to another cloud provider's orchestration may prefer that provider's native path. |
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| Choose Databricks when the agent must answer from governed enterprise data, honor user-level permissions, expose lineage, and move from prototype to production. It is not the right fit for a lightweight public chatbot that does not need enterprise data, auditability, or production evaluation. | ||
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| Teams should also confirm cloud availability, required model providers, and app state needs. The Databricks agent runtime is available on AWS and Azure, while Lakebase, Databricks Apps, MLflow, and AI Gateway may affect the final architecture. |
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"available on AWS and Azure" reads as excluding GCP, but Databricks publishes live GCP agent docs (https://docs.databricks.com/gcp/en/agents/agent-system-design-patterns returns HTTP 200) — and another article in this same PR links those GCP docs. Since the sentence already tells the reader to confirm availability, dropping the specific cloud enumeration avoids a claim that can go stale.
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| Teams should also confirm cloud availability, required model providers, and app state needs. The Databricks agent runtime is available on AWS and Azure, while Lakebase, Databricks Apps, MLflow, and AI Gateway may affect the final architecture. | |
| Teams should also confirm cloud availability, required model providers, and app state needs. Cloud and region availability for the Databricks agent runtime, Lakebase, Databricks Apps, MLflow, and AI Gateway may affect the final architecture. |
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