In the past three years I’ve shipped AI systems that served 40ms fraud scores, refreshed a 2-billion vector index nightly, and survived a compliance audit that asked for column-level lineage across 11,000 tables. The lesson is unglamorous but liberating: AI succeeds or fails on data plumbing, not model theatrics. “Data Infrastructure for AI Success” is not a slogan; it’s a set of concrete latency budgets, storage formats, orchestration guarantees, and team contracts.
You’re here to rebuild systems and teams so AI can scale predictably. I’ll show what to prioritize—from databases to culture—using numbers, mechanisms, and short field-tested examples, plus the trade‑offs that will keep you out of expensive dead ends.
Workload-First Architecture, Not Tool-First
I start every AI initiative by classifying the workload because it dictates everything else. In practice, I see four archetypes with useful numbers: (1) offline training/ETL with hour-level SLAs and petabyte scans; (2) real-time scoring with p99 latency under 100ms and 99.9%+ availability; (3) retrieval-augmented generation (RAG) with end-to-end latency targets of 300–800ms and high recall; (4) monitoring/analytics jobs with daily cadence but strict reproducibility. If you don’t pin the target, you’ll buy the wrong database and chase phantom bottlenecks.
These archetypes translate directly into data requirements. Real-time scoring needs online features with freshness under 1–5 minutes, idempotent updates, and an offline–online consistency check that reports drift under 1%. RAG needs a vector index with dimension (e.g., 768 or 1536) and a reindex budget (e.g., 1–4 hours nightly or rolling micro-batches) plus a hybrid keyword+vector plan to keep precision on long-tail queries. Training pipelines need stable table formats, partition pruning, and reliable backfills; if backfills take over 10% of your compute budget, compaction and partitioning are usually wrong.
Map workload to latency budgets before touching tools. Example: a consumer app wanted “instant” recommendations. We defined p95 under 120ms, which forced (a) on-box caching of top-K per segment updated every 10 minutes, (b) a narrow online feature store (only six features) with batch backfill, and (c) a CPU-first model with vector retrieval limited to 10ms via HNSW at 0.9 recall. When latency crept up, we cut index size by sharding across two nodes and prefiltered with keyword—less elegant, measurably faster.
Storage And Compute Patterns That Don’t Melt Under Scale
For most organizations, a lakehouse-style core is the pragmatic backbone: object storage with open table formats to get ACID-like behavior, schema evolution, time travel, and efficient compaction. I’ve had success with file targets of 128–512MB, partitioning by event_date (daily) plus a high-cardinality secondary key handled via clustering or Z-ordering; this alone cut one client’s training scans by 38% by reducing small-file overhead. Compaction every 6–12 hours usually balances freshness with engine performance; less frequent compaction often creates a small-file storm that starves executors.
Streaming is where systems silently fail. If you need end-to-end freshness under 5 minutes, enforce event-time semantics, watermarks (5–15 minutes beyond observed skew), and idempotent writes keyed by a deterministic primary key. Deduplicate at the earliest stable boundary (typically at the stream sink) and capture a change reason code to avoid destructive merges. For RAG and semantic search, approximate nearest neighbor indexes matter: HNSW gives strong latency at high recall but needs more RAM; IVF-PQ reduces memory by 4–8x at the cost of recall and quantization error. For 768‑dimensional embeddings with 100M vectors, I budget 200–300GB RAM for high-recall HNSW or half that with IVF‑PQ plus a rerank step.
Compute is a cost gravity well. As a baseline, object storage on major clouds runs roughly $0.02–$0.03 per GB-month; cross-zone network runs around $0.01–$0.02 per GB; cross-region is higher. A single high-memory CPU node often handles 5–10k RPS for simple feature lookups, while GPU-hosted inference shines when batch sizes are >8 and models are large; otherwise CPU autoscaling with quantized models wins on price-performance. I’ve cut per-request cost by 40–70% with three levers: (1) response caching keyed by prompt+retrieval set for 15–60 minutes, (2) distilling large models into small ones for frequent intents, and (3) early-exit heuristics that short-circuit long reasoning chains when confidence clears a threshold. Always run a shadow month to measure real utilization before buying long-term commitments.
Operational Guarantees: Quality, Governance, Lineage
Data contracts prevent downstream chaos. I write them like APIs: column names and units, allowed null rates (e.g., <=0.5%), freshness SLOs (e.g., event-time lag <=10 minutes p95), and retention (e.g., 400 days for audits). Enforce at three gates: producer tests on publish, schema registry validation on ingest, and consumer-side assertions in pipelines. When a contract breaks, fail closed and emit a high-severity alert with a rollback plan (e.g., backfill from N-1 snapshot). A team that codified contracts across 37 producers cut incident count by 52% in two quarters because “fix at the source” became policy not suggestion.
Quality monitoring needs fewer metrics than most dashboards show, but they must be tied to decisions. I standardize on five: timeliness (lag), completeness (row/field coverage), validity (type/unit constraints), consistency (cross-table checks), and drift (population statistics or PSI). Thresholds depend on how errors propagate to money or risk: a 1% null spike in a discount feature cost one retailer $9k/day—justified a paging alert; a 3% increase in text field length rarely mattered. For models, log feature distributions online and offline and run nightly skew checks; if KL divergence for any feature exceeds 0.1 for two days, we investigate or trigger a targeted retrain.
Governance and security are not blockers; they are accelerants when baked in. Classify data at ingestion (public, internal, sensitive, restricted) and tag at column level; apply field-level encryption or tokenization for restricted attributes with deterministic tokens to preserve joins. Grant access by role with the principle of least privilege; log every read and keep audit logs for 12–24 months, depending on regulatory guidance. For privacy-sensitive AI, consider on-the-fly redaction before indexing text (e.g., mask names, emails) and keep the secret map in a separate enclave; the retrieval layer then reconstructs context only if the caller is authorized. This design let us serve rich RAG answers while keeping PII out of the vector index—crucial for incident containment.
Teams, Routines, And The Culture That Ships
“Data Infrastructure for AI Success” is a team sport. My default org pattern is a small central platform team (8–15 engineers) owning storage, compute, orchestration, observability, and security guardrails, plus federated domain teams that own use-case data and features. Ratios that have worked: one platform engineer per 5–7 product squads, one data SRE per ~40 critical pipelines, and one governance/metadata specialist per 500–1,000 tables. The platform team publishes paved roads: blessed table formats, an ingestion template, default monitoring, and a golden path for feature serving; deviation requires a review with a clear rationale.
Intake and stage-gates keep ambition honest. I run a simple intake form: user problem, measurable outcome (e.g., reduce handle time by 15%), target latency, data sources, privacy class, expected volumes, and an owner with on-call availability. Then a 90‑day plan: weeks 0–2 discovery and offline eval; 3–6 prototype with shadow traffic; 7–10 harden data contracts, lineage, security; 11–12 pilot with success metrics like weekly active users, p95 latency, cost per request, and operational toil (tickets, pages). If any metric regresses for two consecutive weeks, we pause and fix plumbing before adding features. This cadence consistently prevents the “demo forever, production never” trap.
Decide build vs buy with three questions: (1) Is this a differentiator for our business? (2) Can we meet SLOs with a managed service inside our data residency and security constraints? (3) Do we have the skills to run it 24/7? I buy for vector search at small scale, then build or self-host when QPS exceeds 5k and network egress or tenancy becomes the dominant cost. I build ingestion frameworks if we have >50 producers and wildly heterogeneous schemas because standardizing pays compounding dividends. I never build custom metadata catalogs unless requirements are exotic; adopt an open lineage spec and use the catalog as the single source of truth for ownership and SLOs.
Conclusion
If you’re starting tomorrow, do this: define your top three AI workloads and their latency/availability/cost targets; consolidate storage onto a lakehouse core with disciplined partitioning and compaction; implement narrow, contract-bound feature and vector serving for the first use case; wire five data quality checks tied to decisions; and establish a 90-day intake and stage-gate. Revisit costs monthly and automate what paged you twice. When in doubt, choose the option that reduces variance and clarifies ownership. That is the shortest path to durable Data Infrastructure for AI Success.