Elasticsearch and OpenSearch are reasonable choices as a vector store when an organization already runs one of them for logging or full-text search, since both now support k-nearest-neighbor vector search alongside their mature keyword search and filtering capabilities, making native hybrid search straightforward to implement without adding a separate system. Their vector search performance and recall at scale have improved substantially in recent versions and are competitive for many enterprise workloads, particularly when the corpus is already indexed in one of these systems for other purposes and the team wants to avoid operating a second specialized database. Purpose-built vector databases like Qdrant and Milvus still tend to lead on pure vector search latency and memory efficiency at very large scale, and they offer more vector-specific features such as advanced quantization and multi-vector search, so a greenfield RAG project with no existing Elasticsearch or OpenSearch investment usually has less reason to default to them. The practical decision often comes down to operational simplicity: reusing infrastructure the team already knows how to run and monitor is frequently worth more than a marginal performance edge from a dedicated system. Nanobase AI, an NVIDIA Inception Program member, evaluates whether a customer's existing Elasticsearch or OpenSearch deployment can serve as the vector store before recommending a new system.
The decision usually comes down to reuse versus a performance ceiling
Elasticsearch and OpenSearch both support k-nearest-neighbor vector search well enough for many enterprise RAG workloads, so the question for a team already running one is rarely "does it work" but "does reusing it cost less than the performance and feature ceiling it accepts." That framing matters because a greenfield RAG project with no existing investment has little reason to default to a search platform over a purpose-built vector database, while a team with a mature Elasticsearch or OpenSearch deployment already running well is trading a real operational advantage for a smaller performance gap than it once was. Reusing an existing Elasticsearch or OpenSearch deployment trades a modest performance ceiling for meaningfully lower operational cost, which is often the right trade for a team that already runs one well.
Comparing the migration paths
| Factor | Reuse Elasticsearch or OpenSearch | Add a dedicated vector database |
|---|---|---|
| New infrastructure to operate | None if already running one | A new stateful service to deploy and monitor |
| Hybrid search maturity | Native, since keyword search is the platform's core strength | Requires combining two systems or a database with built-in hybrid support |
| Vector search performance ceiling | Strong but generally behind dedicated engines at very large scale | Higher ceiling, more vector-specific optimizations like advanced quantization |
| Licensing considerations | OpenSearch is Apache 2.0; Elasticsearch's license terms have changed over time and should be checked against current terms | Varies by product; most dedicated vector databases are permissively licensed |
| Team familiarity | High, if the team already operates the cluster | Requires new operational knowledge |
Key takeaway: reuse wins on operational simplicity and native hybrid search; a dedicated vector database wins on raw vector search ceiling at very large scale.
Checking whether your existing cluster can carry the load
- Measure current cluster resource headroom, specifically memory, since vector indexes for k-nearest-neighbor search are memory-intensive in a way that pure keyword search indexes are not, and an undersized cluster will show this as query latency degradation once vectors are added.
- Estimate the added storage footprint of vector fields at your actual embedding dimensionality and document count, since this is often larger than teams expect coming from a keyword-search-only deployment.
- Test query latency with a representative vector search workload against a non-production index first, rather than assuming keyword search performance predicts vector search performance on the same cluster.
- Confirm the cluster's current version supports the specific k-nearest-neighbor and hybrid scoring features needed, since capability and performance both improved significantly across recent major versions of both platforms.
- Plan for the operational overhead of re-indexing when embedding models change, which behaves the same way in Elasticsearch or OpenSearch as it does in any vector database, but competes for cluster resources with existing keyword search workloads if not scheduled carefully.
Key takeaway: validate memory headroom and query latency with a real vector workload before committing, since a cluster sized for keyword search alone can be undersized once vector fields are added.
Where a dedicated vector database still wins clearly
For a genuinely new RAG project with no existing search infrastructure, or one anticipating very large scale with tight latency requirements, purpose-built vector databases such as Qdrant or Milvus tend to offer a higher performance ceiling and more vector-specific features, such as advanced quantization schemes that reduce memory footprint at scale, that Elasticsearch and OpenSearch have not fully matched even with recent improvements. The decision should not default automatically to either option; it should follow from whether the operational savings of reuse outweigh the performance and feature gap for the specific scale and latency requirements at hand.
Key takeaway: greenfield projects anticipating very large scale have less reason to default to a search platform over a purpose-built vector database.
Frequently asked questions
Does adding vector search to Elasticsearch require a separate cluster?
Not necessarily; vector fields can often be added to an existing index or a new index within the same cluster, though teams anticipating heavy vector search load sometimes isolate it onto dedicated nodes within the cluster to avoid resource contention with existing keyword search workloads.
Is hybrid search easier to implement in Elasticsearch or OpenSearch than with a dedicated vector database?
Often yes, since hybrid search combining keyword and vector relevance scoring is native to how these platforms already rank results, whereas a dedicated vector database may need to be paired with a separate keyword search system or rely on its own less mature hybrid scoring implementation.
Should licensing terms factor into this decision?
Yes, they should be checked against current terms for the specific version and deployment model in question, since licensing has changed over time for these platforms and terms can affect self-hosting rights depending on how the cluster is deployed and used.
Can we migrate away from Elasticsearch or OpenSearch to a dedicated vector database later if we outgrow it?
Yes, this migration is generally feasible since the underlying embeddings and documents are portable; the main work is re-indexing into the new system and re-tuning hybrid search or reranking configuration, not the earlier architectural decisions in the pipeline.
How Nanobase AI helps
Nanobase AI, an NVIDIA Inception Program member, evaluates whether a customer's existing Elasticsearch or OpenSearch deployment can reliably carry vector search load before recommending a new system, avoiding unnecessary infrastructure when reuse is the better fit. See our vector database cost comparison or our solutions for the full evaluation approach.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.