Let's cut through the marketing. You're asking for a semantic search solution for a hybrid sub-100 user environment. This is a minefield of hidden complexity, and most platforms are built for a pure, cloud-native reality. Your hybrid setup means you have data sources on-prem, likely in a private data center or even a co-lo, and some in one or more public clouds. The "under 100 users" part is a red herring; your problem is data gravity, latency, compliance, and egress costs, not user scaling.
Relevance AI is a strong contender, but you need to be brutally honest with yourself about your data logistics. Their platform is API-driven and expects your data to be pushed to them or accessible from the public internet. If you're thinking of running their entire stack self-hosted in your hybrid environment, you're looking at a significant engineering lift that negates the "managed service" benefit. Their strength is in the agentic workflows, but the core semantic search relies on you getting the embeddings and vectors to the right place.
Here's the real breakdown of what you must consider, in order of pain:
* **Data Location & Egress:** Moving terabytes of text data from on-prem storage to a cloud-based Relevance AI project for embedding generation will cost you in bandwidth and time. If your data can't leave the private environment, you must self-host the embedding models and the vector database. Relevance supports this, but you're now managing ML Ops.
* **Vector Database Deployment:** This is your critical hybrid component. You'll need something like Weaviate, Qdrant, or PostgreSQL with pgvector deployed where it can be accessed by both your on-prem apps and your cloud apps with acceptable latency. This often means running it in the cloud edge location nearest to your data center, which is still "hybrid" but introduces network hops.
* **The Embedding Model:** If you self-host this (e.g., a SentenceTransformers model), you need GPU resources in your hybrid environment. If you use OpenAI or Cohere APIs, all your data passes through them. Choose your poison.
A pragmatic, but not simple, architecture for a true hybrid setup with Relevance could look like this:
```yaml
# Conceptual flow, not direct config
1. On-Prem Data Source -> (Trigger) -> Your Ingestion Service (runs on-prem)
2. Your Ingestion Service -> Calls LOCALHOST Embedding Model (self-hosted BERT) -> Generates Vectors
3. Your Ingestion Service -> Securely Pushes Vectors ONLY to Cloud-Hosted Vector DB (e.g., Weaviate Cluster)
4. Relevance AI Platform -> Configured to use your Cloud-Hosted Vector DB as its 'datasource'
5. Your Application (hybrid) -> Queries Relevance AI API -> Relevance queries your Vector DB -> Returns results.
```
This keeps your raw data on-prem, pushes only vectors to the cloud, and lets Relevance handle the query orchestration and agentic layers. But you are building and maintaining the ingestion pipeline (steps 1-3).
The alternatives? If you can relax "hybrid" to mean "multi-cloud," it gets easier. If your search can be async and high-latency, it gets easier. But if you need real-time semantic search across a true on-prem/cloud divide with under 100 users, you are looking at a disproportionately high infrastructure-to-user ratio. Relevance AI can work at the core, but you will be doing the heavy lifting on the data plane. Don't underestimate the ongoing cost of monitoring and tuning that bespoke pipeline. The semantic search is the easy part; the hybrid data pipeline is the 80% project.
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Been there, migrated that