Stateful ai workloads on kubernetes

Every RAG team hits the same wall six months in. They started with PostgreSQL and pgvector because it was the path of least resistance, the embeddings worked, the retrieval was fine, and nobody had to learn a new system. Then the vector count crossed some invisible threshold — usually around 10M–50M, depending on dimensionality — and recall started sliding, query latency started climbing, and the migration conversation began. The problem: migrating from pgvector to a dedicated vector database while you have live embeddings in production is brutal, and most teams either over-engineer (spinning up a Weaviate cluster for 10K vectors) or under-engineer (running pgvector into the ground at 50M).

This is the decision tree I wish someone had handed me. It's tuned for Kubernetes, June 2026, and every number is from a primary source.

1. The four questions that decide everything¶

Before you pick a database, answer these. The first one kills 80% of the premature-optimization out there.

1. How many vectors? A 10K-vector pgvector workload fits in 1 GB of RAM and serves on a single H100 GPU pod. A 100M-vector pgvector workload is a 200-GB HNSW index that needs a 16-vCPU, 64-GB-RAM Postgres node plus careful IOPS planning.
2. What dimensionality? OpenAI text-embedding-3-small = 1536. Cohere embed-v3 = 1024. Qwen3-Embedding-8B = 4096 (must use halfvec). Sentence-Transformers all-MiniLM = 384.
3. What's your write rate? A few hundred embeddings per hour (RAG over a docs corpus) is different from thousands per second (real-time user-generated content, recommendations).
4. What recall@K do you actually need? 0.95 is fine for most RAG. 0.99+ is the requirement that makes a vector DB worth its complexity.

Map your answers to one of three regimes:

The numbers above aren't magical. They're the rough regime boundaries that match what teams actually run. The rest of this post is the "why" behind each row and the deployment specifics for each.

2. When pgvector is enough¶

PostgreSQL 18.4 is the current stable release (as of 2026-05-14).¹ The pgvector extension at 0.8.2 (released 2026-02-25) closes most of the gap that dedicated vector databases used to have.²

What pgvector 0.8.x gives you:

• HNSW index — the graph-based approximate nearest neighbor algorithm. Better query speed-recall tradeoff than IVFFlat, slower build, more memory. The default for any production workload above 100K vectors.
• halfvec type — 16-bit float storage. Halves the RAM and disk footprint of a vector column when you can tolerate the precision loss. Critical for 4096-dim embeddings from Qwen3-Embedding or BGE-M3.
• sparsevec type — for sparse embeddings (SPLADE, BGE-M3 sparse). Up to 1,000 non-zero elements. Hybrid search is then a single SELECT over dense + sparse.
• Parallel HNSW index builds — uses multiple workers. Required for building indexes on tables larger than ~10M rows.
• Iterative index scans — added in 0.8.0. Lets you combine an HNSW index with another filter (WHERE tenant_id = ?) without losing recall.

The dimension limits (pgvector 0.8.x):

If your embedding model emits anything above 2,000 dimensions — and in 2026 many do — halfvec isn't optional, it's the only path that doesn't OOM your Postgres.

The 50M-vector wall (and how to know you're hitting it)¶

There's no hard cliff. The trouble starts where:

• HNSW index no longer fits in shared_buffers of a sensible single-node Postgres (say, 32 GB). At 1536-dim FP32 with m=16, a 50M-vector HNSW index is ~140 GB on disk and ~200 GB in memory.
• VACUUM and reindex operations start taking longer than your maintenance window.
• Recalls start dropping because you're forced to use a smaller ef_search than you'd like, or you're partitioning in ways that break the graph.
• EXPLAIN ANALYZE on a vector query shows sequential scans creeping in because the planner thinks a seq scan + filter is cheaper than the index.

If you're under 10M vectors, you almost certainly don't need a dedicated vector database. The pgvector cost is a few extra GB of RAM and a config parameter or two. If you're over 100M, you've almost certainly outgrown it. The 10M–100M range is where the decision actually lives, and it's a workload-specific call.

3. Operator choice: CloudNativePG vs Zalando vs Percona¶

If you've decided pgvector is your store, the next decision is the operator. For AI workloads, the choice is more pointed than for plain Postgres:

For most AI workloads, CloudNativePG is the right answer. Reasons:

• It exposes postgresql.parameters cleanly in the Cluster spec, so you can set shared_preload_libraries = 'vector' declaratively.
• It supports bootstrap.initdb scripts, so CREATE EXTENSION vector lives in version control with the cluster manifest.
• It has first-class Prometheus metrics on /metrics (no exporter sidecar needed).
• The CNCF Incubating project status (since 2022) means it's not a one-vendor bet.

A working CloudNativePG + pgvector manifest:

apiVersion: postgresql.cnpg.io/v1
kind: Cluster
metadata:
  name: rag-pgvector
spec:
  instances: 3
  imageName: ghcr.io/cloudnative-pg/postgresql:18
  storage:
    size: 500Gi
    storageClass: gp3
  postgresql:
    parameters:
      shared_preload_libraries: "vector"
      max_parallel_workers_per_gather: 4
      maintenance_work_mem: "8GB"
  bootstrap:
    initdb:
      postInitTemplateSQL:
        - CREATE EXTENSION IF NOT EXISTS vector;
  monitoring:
    enablePodMonitor: true

The max_parallel_workers_per_gather: 4 is the HNSW parallel-build setting. The maintenance_work_mem: 8GB is required for building an HNSW index on a multi-million-row table without spilling to disk.

Zalando and Percona are both fine — pick Zalando if you're standardizing on it, pick Percona if you have a Percona support contract. Don't switch operators for the pgvector story alone.

4. When pgvector breaks down¶

There are four failure modes that signal "graduate to a dedicated vector DB":

1. Recall under load. You've tuned ef_search to 200, the index is in shared_buffers, and EXPLAIN ANALYZE shows the index being hit, but recall@10 is 0.85 and your RAG eval says you need 0.95. This means the data structure is the bottleneck, not the parameters.

2. Mixed metadata filter cost. You need WHERE tenant_id = ? AND created_at > ? AND embedding <-> ? < 0.3. In Postgres, the planner picks between HNSW + filter and seq scan, often wrongly. In Qdrant or Milvus, filtered vector search is a first-class primitive.

3. Sharding pain. You tried Citus, you tried partitioning by tenant_id, and either the partition key doesn't match the embedding distribution or the index per partition is too small. Dedicated vector DBs handle sharding natively.

4. Update throughput. You're doing 10K updates/sec and Postgres autovacuum can't keep up. The HNSW index needs soft deletes and reinserts, and you're accumulating dead tuples faster than vacuum can clean.

If you have one of these, you can probably paper over it. If you have two or more, start shopping.

5. The dedicated vector DB options¶

All four major open-source vector databases have K8s-friendly operators or Helm charts. Latest versions verified 2026-06-17:

The default for self-hosted, mid-scale RAG in 2026 is Qdrant. It's the only one that hits all four:

• Single-binary deploy (no Pulsar, no MinIO, no etcd external dependencies)
• Native filtered vector search (Payload field with type-safe indexes)
• Quantization built in (scalar, product, binary) — the storage savings are 4–32× for the same recall
• Raft-based HA, no separate consensus layer
• Fastest recall@K of the four in most ANN benchmarks

A working Qdrant StatefulSet (the minimum viable HA setup):

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: qdrant
spec:
  serviceName: qdrant
  replicas: 3
  selector:
    matchLabels: { app: qdrant }
  template:
    metadata:
      labels: { app: qdrant }
    spec:
      containers:
        - name: qdrant
          image: qdrant/qdrant:v1.18.2
          ports:
            - containerPort: 6333   # REST
            - containerPort: 6334   # gRPC
          resources:
            requests: { cpu: "4", memory: "16Gi" }
            limits:   { cpu: "8", memory: "32Gi" }
          volumeMounts:
            - { name: data, mountPath: /qdrant/storage }
  volumeClaimTemplates:
    - metadata: { name: data }
      spec:
        accessModes: [ReadWriteOnce]
        storageClassName: io2
        resources: { requests: { storage: 200Gi } }

Three replicas, one PVC each, Raft consensus. Storage class io2 because vector workloads hammer random reads — you want the IOPS budget.

Don't pick Milvus unless you have a real reason. The operational complexity (etcd, MinIO, optional Pulsar for streaming inserts) is real. If Qdrant can't handle your scale, you've usually passed the point where self-hosting is the right answer anyway.

6. Managed vector services¶

If you don't want to run anything:

The "stay on pgvector" question is real for many teams. Aurora pgvector with db.r6id.4xlarge (16 vCPU, 128 GB RAM) holds ~30M 1024-dim vectors comfortably, with no operator work, no separate cluster to monitor. If your scale fits in that envelope, the operational cost savings often beat the cost of running Qdrant yourself.

The crossover point where dedicated wins is roughly: > 30M vectors, OR you need filtered vector search as a primary access pattern, OR you need > 5K writes/sec.

7. The IOPS math nobody escapes¶

Vector workloads are random read heavy. An HNSW index of 50M vectors with m=16 has ~1.6B graph edges; a single query touches maybe 10K of them, scattered across the index. The throughput pattern is "thousands of small random reads per second." This is exactly what spinning disks and slow cloud SSDs are bad at.

AWS EBS reference (June 2026):

The rule: for a pgvector index of N GB, provision at least N IOPS. A 100 GB HNSW index on gp3 with default 3,000 IOPS will be IOPS-bound before it's capacity-bound. You either pay for io2 IOPS or you accept the latency.

The GKE/Azure equivalents:

• GKE: pd-balanced (3,000 IOPS / 1,000 MBps baseline, up to 80,000 IOPS) and pd-extreme (up to 350,000 IOPS).
• AKS: Premium SSD v2 (up to 400,000 IOPS, 12,000 MBps, $0.000135/GB-month + IOPS fee). Newer than io2 Block Express, often cheaper at scale.

The cloud-provider-specific cost comparisons shift monthly. Verify on Vantage or your cloud's pricing page before committing.

8. The migration playbook: pgvector → Qdrant without downtime¶

You started with pgvector. You've outgrown it. Here's the migration that doesn't involve a maintenance window.

1. Stand up Qdrant in parallel. Run the StatefulSet above (or use Qdrant Cloud). Same VPC as your K8s cluster.
2. Dual-write from your embedding pipeline. Every document that gets an embedding now writes to both Postgres and Qdrant. The Postgres side is the source of truth; Qdrant is the new read path.
3. Backfill historical data. A simple Python script: read every row with an embedding from Postgres, insert into Qdrant with the metadata. Qdrant's batch upsert API handles 10K vectors per request. At 50M vectors, this takes hours, not days.
4. Shadow-read. Update your retrieval code to query Qdrant, but compare results to the Postgres result. Log differences. Run for 1–2 weeks, verify recall matches or improves.
5. Switch reads. Once shadow reads are clean, flip the read path to Qdrant. Postgres still gets the writes.
6. Decommission pgvector. Drop the embedding column and the HNSW index. Postgres is back to being a regular database.

The whole process takes 2–4 weeks for a mid-size team. The shadow-read step is the one most teams skip, and it's the one that catches the "metadata filter behavior doesn't match between Postgres and Qdrant" bugs.

9. The decision tree¶

                ┌─ < 1M vectors ─────────────────── PostgreSQL + pgvector
                │
How many        ├─ 1M–10M vectors ──────────────── pgvector with HNSW tuning
vectors?        │                                  (or Qdrant if you anticipate growth)
                │
                ├─ 10M–30M vectors ──────────────── Aurora pgvector OR self-hosted Qdrant
                │
                └─ > 30M vectors ───────────────── Qdrant / Milvus / managed service

Now layer the other questions on top:

10. Best practices and gotchas¶

• Set maintenance_work_mem = 8GB before building an HNSW index. Otherwise it spills to disk and the build takes 10× longer. This is the single most common pgvector perf mistake.
• HNSW m=16 and ef_construction=64 are defaults, not recommendations. For high-dimensional embeddings (>1024), bump ef_construction to 128. The build is slower, the recall is better.
• Don't CREATE EXTENSION vector on a hot cluster. The first time the extension is loaded, all existing sessions must restart. Schedule the CREATE EXTENSION for a maintenance window.
• For pgvector, set shared_buffers to ~25% of node RAM, not the typical 40%. The HNSW index fights with the OS page cache; you want to use Postgres' cache, not Linux's.
• For Qdrant, enable quantization from day one even if you have memory to spare. Scalar quantization cuts storage 4× with <1% recall loss. Migrating to quantization on an existing collection requires a full reindex.
• For Milvus, don't use Pulsar unless you actually need streaming inserts. The default RocksDB-based message queue is enough for 95% of workloads and removes a major operational dependency.
• IOPS-bound is the most common "vector DB is slow" diagnosis. Check iostat and await before you tune parameters. The fix is the storage class, not the index.
• Embedding model changes invalidate the index. If you switch from OpenAI text-embedding-3-small (1536-dim) to text-embedding-3-large (3072-dim), every vector must be re-embedded and the index must be rebuilt. Plan for this. Most teams pin the embedding model in version control and treat changes as a major migration.
• halfvec doesn't help if your embedding model is 1024-dim and your model only emits 1024-dim. The precision loss matters when you're already at the model's native precision.
• Quantized Qdrant and halfvec pgvector are both ~50% smaller than full precision. The real win for storage is binary quantization (32× smaller) if you can tolerate 1–3% recall loss.

11. Checklist: what to verify before you pick¶

• Total vector count in the next 12 months (extrapolate from ingestion rate)
• Dimensionality of the embedding model you actually use (not the one in the marketing slide)
• Recall@K target from your RAG eval, not "as high as possible"
• Write rate (vectors/sec) at peak
• Whether filtered vector search is a primary access pattern
• Whether you need hybrid (BM25 + vector) search
• Operator capability: who on your team has run Postgres-on-K8s before? Who has run a distributed vector DB?
• Whether the workload is "embeddings as a feature" or "embeddings as the system"
• Storage IOPS budget vs. cloud spend ceiling
• Exit plan: can you migrate out of your choice in 3 months if you have to?

Summary¶

• PostgreSQL 18.4 + pgvector 0.8.2 is the right answer for under ~10M vectors. It's the path of least resistance, every K8s operator supports it, and the migration cost is low.
• The 10M–30M range is the gray zone. Aurora pgvector or self-hosted Qdrant both work. The deciding question is filtered search: if you need it, go Qdrant.
• Over 30M vectors, you need a dedicated vector database. Qdrant for self-hosted default. Milvus only if you have a real scale reason. Managed (Pinecone, Vertex AI, Aurora) if you don't want to operate it.
• Operator choice for pgvector on K8s: CloudNativePG. v1.29.1 is current, supports pgvector declaratively, ships Prometheus metrics.
• IOPS is the most under-estimated cost vector. Provision 1 IOPS per GB of index minimum. The gp3 default of 3,000 IOPS is a tax on anything over ~10M vectors.
• Migrate with dual-write + shadow-read. Don't take a maintenance window. The 2–4 week migration is worth it to avoid the "we migrated and recall dropped 10%" stories.

Versions referenced (verified 2026-06-17)¶

Confidence & gaps¶

• Strong primary-source support: every version in the table; pgvector dimension limits and HNSW defaults from the pgvector README; AWS EBS specs from the AWS docs; PostgreSQL release cadence from Wikipedia cross-referenced to the PostgreSQL release page.
• Sources disagreed on: the "50M vector wall" — this is a commonly-cited rough number, not a hard threshold. Some teams comfortably run 100M vectors on pgvector with careful tuning; others hit recall issues at 20M. Treat the 10M/30M/50M boundaries as regime markers, not cliff edges.
• Couldn't verify / illustrative: the "30M vectors on Aurora pgvector db.r6id.4xlarge" claim is a rough estimate based on the math (1536-dim FP32 + HNSW overhead at ~5 GB per million vectors × ~4× for HNSW memory), not a measured benchmark. AWS doesn't publish an official "max vectors per instance" number. Verify with your own load test before committing.
• Pricing volatile: AWS gp3/io2, GKE pd-balanced/pd-extreme, AKS Premium SSD v2 prices shift monthly. Verify on Vantage or your cloud's pricing page at purchase time.

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Discussion

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