How much does an enterprise RAG project cost in 2026? Real ranges, from POC to 100 million vectors
An enterprise RAG is committed step by step, without ever signing a big budget blind: a short fixed-price pilot tested on your own questions (2 to 3 weeks), then an IgnitionRAG subscription. If you host the system yourself, add the line that derails budgets: recurring infrastructure, from $25 to over $5,000 per month depending on architecture. A line-by-line breakdown, a 3-year TCO of 9 vector databases, and the three scoping decisions that bound the cost. For SMB leaders, CTOs and CIOs.
Transparency note. This article follows the IgnitionAI editorial policy. Infrastructure costs come from the vendors' official pricing pages (accessed May 2026) and community benchmarks. The TCO scenarios are IgnitionAI estimates with explicit assumptions.
An enterprise RAG project is committed step by step, without ever signing a big budget blind. At IgnitionAI, you start with a fixed-price pilot, tested on your own questions: 2 weeks for support and knowledge, 2 weeks for marketing, 3 weeks for dev, with an exit option after week 1. After a support or marketing pilot, you move to an IgnitionRAG Team subscription. If you build and host the system yourself, add $25 to over $5,000 per month of search infrastructure, plus LLM inference. You decide that recurring cost at scoping time, and it is the one that derails budgets.
Here is the full breakdown, with our IgnitionAI estimates.
The four cost lines of a RAG
| Line | When | Range | Nature |
|---|---|---|---|
| Pilot | 2 to 3 weeks | On quote, depending on the pilot | One-off, fixed price |
| Subscription (after a support or marketing pilot) | Monthly | IgnitionRAG Team (or Solo if only one person uses it), on quote | Recurring, fixed price |
| Search infrastructure | Recurring | $25-245/month at 1 million vectors, $300-5,000+/month at 100 million | Recurring, scale-sensitive |
| LLM inference and operations | Recurring | Driven by traffic and model choice (method below), plus 0.25 FTE of operations for self-hosted | Recurring, traffic-sensitive |
The pilot and subscription price is set on quote, after a first conversation. These first two lines are known before you start. With IgnitionRAG, the AI is managed and credits are included in the subscription: the last two lines matter mostly if you build and host the system yourself. They run for the life of the system, and their trajectory depends on decisions you make before the first line of code. To tie these items to a test protocol rather than an isolated estimate, see our guide to evaluating a RAG in production.
The pilot is a stopping point: it is tested on your own questions, and at the end, you decide whether to continue.
The underestimated line: vector infrastructure at scale
At POC scale, the market's vector databases all cost about the same: between $25 and $50 per month for one million vectors. Services billed per fixed capacity unit are the exception ($100-245). That is what makes the POC misleading. We documented this mechanism in the 5 decisions your first POCs hide from you.
At 100 million vectors, the gap between solutions reaches a factor of 17.
| Solution | ~1M vectors | ~100M vectors | Scale factor |
|---|---|---|---|
| Self-hosted PostgreSQL extension (ParadeDB, pgvector) | $30/month | $300/month | ×10 |
| Managed OpenSearch (AWS) | $104/month | $1,200/month | ×12 |
| Azure AI Search (S1) | $245/month | $4,900/month | ×20 |
| Managed Milvus (Zilliz) | $35/month | $800/month | ×23 |
| Qdrant Cloud | $25/month | $600/month | ×24 |
| Weaviate Cloud | $30/month | $3,000/month | ×100 |
| Pinecone Serverless | $43/month | $5,000+/month | ×116 |
IgnitionAI estimate consolidated from the vendors' official pricing pages (May 2026), for a hybrid-search workload. Prices change: check the current grids at scoping time.
Two readings of this table.
The "serverless tax". Serverless offerings billed per read/write unit are unbeatable in operational simplicity: zero infrastructure to manage. But their cost grows faster than the corpus. At 20 million vectors, a typical serverless deployment lands around $2,500/month, where the same load on an equivalent self-hosted instance stays under $100/month of raw infrastructure. The gap pays for the 0.25 FTE of operations that self-hosting requires, several times over.
The cost floor of capacity-unit services. Services billed per fixed search unit create a high monthly baseline even at low traffic: relevant if you consume the capacity, penalising for a pilot.
3-year TCO: add who operates the system
Raw infrastructure cost misses one variable: who operates the system. Here are our 3-year TCO scenarios for a typical deployment of 10 million vectors and 50,000 queries per day in hybrid search.
| Scenario | Infrastructure, 3 years | Operations, 3 years | 3-year TCO |
|---|---|---|---|
| Self-hosted PostgreSQL extension | ~$3,600 | ~$18,000 (0.25 FTE) | ~$22,000 |
| Self-hosted Qdrant | ~$3,500 | ~$18,000 (0.25 FTE) | ~$21,500 |
| Qdrant Cloud | ~$6,800 | ~$9,000 (support) | ~$16,000 |
| Weaviate Cloud | ~$13,100 | ~$9,000 (support) | ~$22,000 |
| Pinecone Serverless | $7,000-29,000 depending on traffic | included | $7,000-29,000 |
| Azure AI Search (S1 + semantic reranker) | ~$40,700 | ~$9,000 (support) | ~$50,000 |
IgnitionAI estimate. Assumptions: 10M vectors, 50,000 queries/day, hybrid search. 0.25 FTE of operations valued at ~$6,000/year for self-hosted, vendor support contract for managed. Your volumes and internal day rates move these lines; the order of magnitude holds.
Remember the mechanics rather than a winner: TCO comes down to three variables (infrastructure, operations, corpus trajectory), and none of the three shows up in a POC. A managed service 2-3× more expensive in infrastructure can be the right choice if you have no operations capacity. Self-hosting can divide the bill by 5 if you already run a platform team.
A word on latency, the sector's favourite sales argument: community benchmarks place the market's solutions between 12 and 80 ms p99 on corpora of 1 to 10 million vectors. In a RAG, LLM generation takes 500 to 3,000 ms. A 40 ms infrastructure difference is invisible to your users, short of a pure-search use case. Don't pay a premium for it.
Infrastructure by company profile
The pilot price is fixed and announced before you start. What varies strongly by profile is the search infrastructure when you host the system yourself.
| Profile | Typical corpus | Search infrastructure | Watch out for |
|---|---|---|---|
| Pilot or first SMB RAG | < 1M vectors | $25-50/month | Solutions all look alike at this scale: choose for the trajectory, not for the POC |
| First RAG in production | 1-10M vectors | $100-600/month | The self-hosted vs managed trade-off is decided here: do you have 0.25 FTE of operations? |
| Several use cases | 10-100M vectors | $300 to $5,000+/month | The scale factor (×10 to ×116) outweighs the rest: project the corpus at 24 months before signing |
IgnitionAI estimates consolidated from the vendors' official pricing pages (May 2026), like the vector database table above.
LLM inference: the method rather than a fake number
Inference cost depends on four variables to price at scoping: queries per day, size of the context sent to the model (the retrieved chunks), size of the answers, price of the chosen model. The formula is simple:
monthly cost ≈ queries/day × 30 × (input tokens × input price + output tokens × output price)
At 50,000 queries per day, inference runs to thousands of euros per month on a frontier API. It often exceeds the search infrastructure cost over time. Three levers keep it under control: complexity-based routing (a small model for simple questions), caching frequent answers, and self-hosting an open-weights model when volume justifies it. We publish current prices at scoping time rather than in this article: those grids change several times a year.
On operations, budget 0.25 FTE for a self-hosted system (monitoring, upgrades, reindexing, de-facto on-call), near zero for managed serverless. That is the real price of serverless simplicity, and it is legitimate to pay it.
The three scoping decisions that bound the bill
Budget is only one output of scoping. Before comparing options, also document the use case, the corpus, access rights, evaluation and go/no-go conditions: see our guide to scoping an enterprise RAG project.
1. Self-hosted or managed: decide on operations capacity, not on the price grid. The criterion that matters: who operates the system in 18 months. An existing platform team absorbs self-hosting and divides TCO by 2 to 5. Without one, managed comes out cheaper in full cost, despite the higher infrastructure bill.
2. The corpus trajectory at 24 months: choose for it, not for the POC. A corpus that stays under 10 million vectors allows almost anything. If it is heading for 50 or 100 million, rule out ×100 scale-factor architectures from the start. And if your production PostgreSQL already hosts the business data, look at hybrid-search extensions inside PostgreSQL. They remove the synchronisation between the transactional database and the search index, a hidden recurring cost teams forget to budget.
3. The compliance level: design it in from the start, not as a retrofit. Permission-aware filtering (the 5 architectures compared), logging (Article 12 of Regulation (EU) 2024/1689), sovereign hosting where required: built in at design time, these choices weigh a few percent of the build. Retrofit them after an incident and you face a full reindexing and several weeks of downtime. Our approach is detailed on our governance page.
FAQ: RAG project costs
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How much does a RAG pilot cost?
At IgnitionAI, a pilot is fixed-price, set on quote: 2 weeks (support and knowledge, or marketing) or 3 weeks (dev, with an exit option after week 1). It is tested on your own questions, with a stopping point. Then IgnitionRAG Team takes over on a subscription. For a quote, let's talk about your project. If you host a pilot yourself, the infrastructure stays under $50/month.
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What is the monthly infrastructure cost of a RAG in production?
From $25 to $245/month for a one-million-vector corpus depending on the solution, and from $300 to over $5,000/month at 100 million vectors. The gap between solutions reaches a factor of 17 at scale: your corpus trajectory at 24 months should drive the choice, not the POC price.
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Managed or self-hosted vector database: how do you decide?
On operations capacity, not on the price grid. Self-hosting requires about 0.25 FTE (monitoring, upgrades, reindexing) and divides infrastructure cost by 5 to 25 at scale. Managed removes that burden but its cost grows with the corpus, up to a ×116 factor for serverless offerings. Under 10 million vectors, both options remain reasonable.
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Does LLM inference cost more than the search infrastructure?
Often yes, as soon as traffic is sustained. At 50,000 queries per day, inference on a frontier API runs to thousands of euros per month. Well-sized search infrastructure stays under $600/month at 10 million vectors. Complexity-based routing, caching and self-hosting an open-weights model are the three control levers.
Sources and methodology
Infrastructure pricing: official vendor pricing pages, accessed May 2026: Qdrant, Pinecone, Weaviate, Zilliz / Milvus, Elastic, AWS OpenSearch, Azure AI Search, ParadeDB. The table figures are consolidated, rounded orders of magnitude: the grids change several times a year, re-check at scoping time.
Latency benchmarks: public benchmarks (ANN-Benchmarks, BEIR) and vendor publications, May 2026. Latency varies with hardware, vector dimensions and load: measure on your own data before deciding.
Regulatory framework: Regulation (EU) 2024/1689 on artificial intelligence, Article 12 (logging for high-risk systems), EUR-Lex.
IgnitionAI offers (subscription): on quote, see the contact page. The TCO scenarios are IgnitionAI estimates, see our editorial policy.
Related IgnitionAI articles:
- Enterprise RAG in production: 5 critical decisions your first POCs hide from you
- Access control in an enterprise RAG: 5 architectures compared
- The European AI Act: what mid-market CTOs must prepare after the Digital Omnibus
Sources last reviewed: 2026-06-12. Pricing grids and latency benchmarks are revisited at each article revision.
Preparing a RAG project and want to know where to start? A 30-minute call is enough to choose your pilot: you leave with its scope, duration and fixed price. Request a conversation.