AI Platforms: How to Choose
Blog post from DataStax
An AI platform is a unified technology stack for prototyping, developing, releasing, and maintaining GenAI-enabled applications, comprising several components such as general-purpose or bespoke LLMs, retrieval-augmented generation techniques like RAG, data ingestion, user-facing agents, request/response workflows, support for multiple LLMs, and abstraction on top of an LLM. While not necessary, using an AI platform can reduce the work of eliminating universal features, increase production readiness, increase the pool of GenAI app developers, provide advanced features for extracting data from structured, unstructured, and non-structured sources, offer integration with different LLMs, support for switching out LLMs mid-project, and enable composability and orchestration. A good AI platform should make developers more productive, reduce ramp-up time, support creating no-code and low-code solutions, provide a visual builder, and integrate with multiple systems.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 13 | 4,013 | 569 | 191 | -13% |
| RAG | 5 | 1,528 | 261 | 92 | -30% |
| Vector Search | 3 | 1,947 | 300 | 116 | -32% |
| Observability | 1 | 1,454 | 304 | 103 | +17% |
| Real-time | 1 | 3,875 | 964 | 250 | -11% |
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