If your vector database costs are scaling in direct proportion to your data volume, you have hit the O(N) scaling ceiling. For most companies scaling beyond a few hundred million vectors, this is not a technical problem, it is a financial one.
The root cause lies in the underlying architecture: almost every major vector solution today, whether it’s a proprietary cloud service or a popular open-source index (like HNSW), is RAM-bound. The index structure must reside in expensive, high-availability memory to guarantee low latency.
The trade-off is unavoidable: as you ingest more customer data, video embeddings, or product catalogs, you are forced into disproportionately expensive cloud instance types. You are effectively paying a massive premium to store static vector data in RAM, simply to facilitate index lookups. This cost curve is financially unsustainable for any company whose profitability relies on infinite data growth (DoorDash, Plaid, Instacart, etc.).
Many platform engineering teams have tried to pivot away from high-cost proprietary solutions. The most common move is toward Postgres extensions like PGVector.
While this offers short-term cost relief, it forces unacceptable trade-offs for high-stakes enterprise applications:
Sacrificed Performance: You trade low-latency search for cost savings. At scale, the performance degradation is noticeable and compromises the user experience.
Missing Integrity: PGVector lacks the full ACID transactional guarantees (Atomicity, Consistency, Isolation, Durability) required for financial, regulatory, or complex analytics data. You cannot safely build mission-critical features on a system that sacrifices data durability.
The solution isn’t just cheaper vectors; it’s a vector database that is architecturally sound for enterprise scale and integrity.
The only way to break the RAM dependency and achieve financial sustainability is an architectural shift that decouples the vector storage layer from the expensive compute layer.
This requires two fundamental changes:
Memory-Mapped Storage (mmap): Instead of storing the vector index exclusively in RAM, we use mmap to push petabytes of embeddings onto cheaper, fast SSD/disk. This treats commodity disk space like virtual memory, allowing you to handle massive vector capacities without having to provision expensive, high-RAM instances.
Guaranteed O(k) Retrieval: The architecture must guarantee constant-time retrieval speed O(k) regardless of the index size. This is the ultimate promise: your query latency will not increase as your data grows from 100 million vectors to 10 billion vectors.
This shift moves your vector infrastructure from a variable-cost, resource-constrained model to a fixed-cost, linear-scaling asset.
The scaling problems hitting platforms like Coda and Instacart are structural, not incidental. The vector index has become a core piece of enterprise infrastructure, and it must be governed with the same ACID and cost principles as the main OLTP database.
This architectural shift ACID integrity, O(k) performance, and mmap storage—is no longer optional; it is the path to ensuring your AI features can grow indefinitely without compromising margin or reliability.
The scaling ceiling imposed by RAM-bound vector search is now a core enterprise infrastructure problem. We have published a technical specification detailing our O(k) architecture and mmap implementation. You can review the full SYNRIX specification and request a pilot here
https://ryjoxdemo.com/
We believe this is the only path to financially sustainable AI at massive scale.