Cost Math for Vector Databases at Scale: Storage, Compute, and Egress

What Costs Money in Vector DBs

Three lines:

• Storage (the vectors and the index)
• Compute (queries, inserts, indexing)
• Egress (data transfer out of the cloud)

Plus operational overhead: monitoring, backups, ops staff. At small scale these are noise. At 100M+ vectors they decide whether the project is viable.

The Storage Math

A 1024-dim float32 vector is 4 KB. With HNSW graph overhead (typically 2-3x the raw vectors):

• 1M vectors: ~12 GB
• 10M: ~120 GB
• 100M: ~1.2 TB
• 1B: ~12 TB

Quantization changes these:

• int8: divide by ~3
• binary: divide by ~30
• Matryoshka 512: divide by ~2

For a 100M-vector corpus with int8 quantization, you fit in 400 GB — manageable on a single beefy node.

The Compute Math

Vector queries are CPU/GPU-bound on the HNSW traversal. Cost depends on:

• Index size (in RAM)
• Query rate
• Top-K
• Reranking compute

For 1000 QPS on a 10M-vector HNSW index in 2026, a typical 16-core, 64GB-RAM instance suffices. Cost: hundreds of dollars per month on cloud, less on dedicated hardware.

For 10x QPS, you typically need horizontal scaling — replicas, not bigger nodes.

The Egress Math

Cloud providers charge for egress. If your vector DB is in cloud A and your application is in cloud B, every query result moves money.

Mitigations:

• Co-locate vector DB and application in the same region
• Use private connectivity (PrivateLink, Interconnect) for cross-region
• Process at the vector DB and return only summaries

For high-volume systems, egress can be 20-40 percent of vector DB costs.

Cost Curves by Scale

flowchart LR
    Small[1M vectors] --> Cost1[~50/mo cloud]
    Mid[10M] --> Cost2[~500/mo cloud]
    Large[100M] --> Cost3[~3-8K/mo cloud]
    XL[1B] --> Cost4[~30-100K/mo cloud]

Numbers vary widely by provider and configuration. The shape: cost scales roughly linearly with vector count when the index fits in RAM; jumps when you cross hardware boundaries.

Self-Hosted vs Managed

Managed vector DBs (Pinecone, Qdrant Cloud, Weaviate Cloud) are easy but more expensive at scale. The 2026 crossover for most workloads:

• Up to ~10M vectors: managed wins on ergonomics
• 10M-100M: depends on team capability
• 100M+: self-hosted typically substantially cheaper

Self-hosted requires monitoring, backup, and incident response — real ops cost.

Hidden Costs

Beyond the headline:

• Re-embedding when the model upgrades (compute + egress)
• Backups (storage cost ~ 1-3x of primary)
• Replicas (multiply primary cost)
• Multi-region (multiply primary cost; egress between regions)
• Compliance (BAA, residency, audits)

For a typical mid-sized deployment, hidden costs add 30-100 percent to the headline cost.

TCO Modeling

For a credible TCO model:

• Vector storage cost
• Index overhead (1-3x storage)
• Replicas (typically 2-3 for HA)
• Backup storage (1-3x primary)
• Compute for queries (peak QPS × hours)
• Egress (per-query × volume)
• Re-embedding per year (corpus size × frequency)
• Operational labor (10-20 percent of compute cost)

Forecast over 3 years for the right capex/opex picture.

Cost-Reduction Levers

• Quantization (4-30x storage reduction)
• Matryoshka truncation (2-4x reduction)
• Hot/cold tiering (cold tier on cheaper storage)
• Read replicas instead of larger primaries
• Co-location to eliminate egress
• Caching at the application layer (avoids repeated queries)

What CallSphere Spends

For our blog dedup system on pgvector with ~3K vectors, the cost is essentially zero (covered by the existing Postgres instance). For our agent memory layer at higher scale, we run Qdrant on a dedicated VM — costs in the low hundreds per month.

For the volumes most teams operate at, vector DB cost is a minor line item. It becomes major only at very large scale.

Sources

• Pinecone pricing — https://www.pinecone.io/pricing
• Qdrant Cloud pricing — https://qdrant.tech/pricing
• AWS S3 + EC2 calculators — https://calculator.aws
• "Vector DB cost analysis" — https://thenewstack.io
• "Cloud egress costs" — https://www.cloudflare.com/the-net

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