Search as a Service vs Build: a 3‑year TCO framework

Search as a Service is the default decision for product teams shipping search: you get relevance tuning, analytics, and SLAs without hiring an ops team. That convenience costs money, and that money compounds as query volume, index size, and custom ranking logic grow.

A fully loaded US backend engineer costs roughly $200,000/yr. A 3‑engineer squad running search for three years is therefore $1.8M in labor. By contrast, hosted search vendor plans run from about $12k/yr for small apps to $300k+/yr for enterprise tiers; self-hosted OpenSearch on AWS with HA typically sits between $24k–$120k/yr in infra plus 0.3–0.8 FTE for ops.

Your decision hinges on three numbers: queries per day (QPD), index size in documents, and required control over ranking or data residency. At 10k QPD and a 10M‑document index, a managed provider and a modest self‑hosted cluster both make sense for different reasons; at 1M QPD or when you need custom learning‑to‑rank pipelines, the math shifts toward build.

Direct answer: Buy Search as a Service if your product will do under 3 million queries/month, you value time-to-market, and you expect to spend less than $180k/yr on search SaaS; build or self-host once your annual SaaS bill approaches one senior engineer's loaded cost (~$200k/yr) or you need bespoke ranking, private embeddings, or sub-50ms P99 latency under heavy load. This rule of thumb keeps your 3‑year TCO comparisons simple and traceable.

Search as a Service economics

Algolia, Elastic Cloud, and Typesense Cloud sell low friction: dashboards, analytics, and instant relevance tuning. Algolia commercial tiers for production apps typically run $12k–$120k/year depending on requests and records. Elastic Cloud for text search ranges $18k–$240k/year depending on node sizing and snapshot retention. Vector-first hosted providers (Pinecone, Zilliz Cloud, Qdrant Cloud) add $6k–$60k/year for medium workloads with 1536‑dim embeddings and replicas.

Self-hosting OpenSearch/Elasticsearch on AWS shifts costs from license to infrastructure and people. A production three‑hot‑node OpenSearch cluster with 1 TB of EBS, replica shards, and moderate indexing throughput costs $2k–$8k/month in instance and storage charges — $24k–$96k/year. Add backups, monitoring, and egress: another $6k–$18k/year. Then add ops effort: 0.3–0.8 FTE (roughly $60k–$160k/yr loaded) for maintenance and incident response.

Performance and feature gaps matter. Managed providers advertise 10–50 ms median query latency; Algolia reports sub‑30ms for small records on SSD-backed infrastructure. Self-hosted systems often see 50–200 ms P50 and occasional P99 spikes above 500 ms without careful caching and provisioning. If you have a 50 ms P99 latency budget for search, your ops and infra costs jump — expect to double cluster size or add edge caching with CDN or Faiss‑based in‑memory layers.

Quality of relevance and custom ranking is a dollar vector too. A basic text‑only search from a hosted vendor will deliver 60–80% of ideal relevance in many e‑commerce or docs workloads. If you need learning‑to‑rank, personalized signals, and custom pipeline stages (feature stores, offline model training, shadow re-ranking), plan 1–2 senior ML/IR engineers for 12–18 months: $200k–$400k each per year, plus compute for offline training and A/B frameworks (~$10k–$60k/yr).

Buy search when the SaaS bill is less than one senior engineer per year; build when the vendor bill approaches a full FTE and you need control over ranking, latency, or data residency.

What this means for a CTO

You need a 3‑year TCO spreadsheet that lines up SaaS spend, infra, and labor. Populate three columns: vendor (Algolia/Elastic/Pinecone), self-host (OpenSearch/Elasticsearch on AWS), and build‑custom (relevance + infra + ops + ML pipelines). Use these inputs: engineer loaded cost $200k/yr; ops FTE for self-host 0.3–0.8 FTE; vendor SaaS $12k–$300k/yr depending on scale. Compare total at years 1 and 3, and perform a sensitivity sweep for ±50% query growth and 2× index size.

If you lead a B2B product with enterprise customers and contractual SLAs, start with a managed provider for months 0–12 to validate UX and ranking. Convert to self‑host only when you can itemize vendor costs that cumulatively exceed a single FTE plus 0.3 FTE ops and the migration cost. Migration itself is not free: snapshot export, mapping transform, and reindexing 10M documents will cost you developer time and roughly $3k–$20k in transient cloud compute depending on parallelism.

Build vs buy checklist

1) Run a cost baseline: record QPD, index size, average doc size, and retention window and get vendor quotes (Algolia/Elastic/Pinecone). 2) Quantify ops load: estimate 0.3–0.8 FTE for OpenSearch maintenance plus $24k–$96k/yr infra. 3) Feature delta: list features that only self-host or custom pipelines can deliver (L2R, private embeddings, custom tokenizers). 4) Run a latency test: if you require P99 <50 ms at peak, cost self‑host vs vendor for the required cluster sizing. 5) Migration cost: budget $20k–$120k for mapping, reindexing, and QA for 1M–100M documents.

You should treat hosted search as a staging and learning environment even if you plan to self-host later. Use it to collect query logs, relevance labels, and telemetry you will need to replicate relevance in a self-hosted stack. Those logs are also your bargaining chip: when a vendor charges $150k/yr, you can show precise query growth and negotiate enterprise terms or justify build.

If you decide to build, adopt these guardrails: allocate one senior infra engineer for the first 6 months specifically for index architecture and observability; set a 12–18 month horizon to reach parity with a managed provider for relevance features; and plan for ongoing 0.5 FTE for incident response. Those commitments convert a 'cheap infra' illusion into a predictable cost you can compare to vendor spend.

Final thesis: treat Search as a Service as a lever to validate product‑market fit and collect data; treat build as a business decision when vendor spend approaches a loaded engineer or when control over ranking, latency, or data governance materially affects revenue or cost. Executed badly, either choice costs you time and money — executed with the TCO framework above, you make the right trade at the right time.