Your finance team is probably finalizing 2026 AI budgets this week. Most look at Q3 pilot costs; say, $8K spent on OpenAI API calls over three months and budget $35-40K for the whole year.
That’s the line item they’ll approve.
What doesn’t make the spreadsheet is the infrastructure stack.
- The vector database that hosts your embeddings ($950/month, whether you query it or not),
- the observability tools that trace AI-specific metrics like prompt latency and model drift ($300-500/month),
- the staging environments that need production-scale data to test accuracy (often doubling your infrastructure footprint), and
- the monthly operations to refresh embeddings as documents update.
Finance sees the $40K model budget. The actual run rate is $65K. The model accounts for 30% of the bill, while the infrastructure around it accounts for 70%.
By Q2, teams hit spending alerts. You either cut scope or go back to finance with an emergency increase. Neither looks good.
In this edition, I’ll break down four infrastructure costs that rarely show up in initial AI budgets and what to forecast instead.
Vector databases cost more than the models they support
Teams budget for OpenAI API costs; usage-based, scales with queries. They forget that vector databases are fixed costs that run 24/7, whether you query them or not.
In one enterprise deployment, the vector database (Pinecone Pro) ran $8,500/month while OpenAI API calls for embeddings and queries cost $2,000/month. Vector index hosting was over four times as expensive as model usage.
Even staying in Azure’s ecosystem doesn’t help. Teams start on the Basic tier ($73/month) for POCs. Scaling to production, indexing 10 million documents with semantic search, requires Standard S1 with the replicas and partitions needed for that volume, running about $981/month. That’s a 13× increase from the Basic tier most teams don’t forecast.
What you can do
Budget for the Standard tier upfront if indexing over 1M documents. Use Azure’s pricing calculator with production data volumes, not pilot scale, to avoid mid-year surprises.
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AI observability requires its own budget line
Traditional APM tools track latency and errors. They don’t trace multi-step agent workflows, log every prompt/response, or detect model drift. AI needs specialized observability, and it costs more.
Over half of IT organizations now use AI monitoring features in their observability platforms, up from 42% a year prior. AI systems demand monitoring for model confidence scores, data drift detection, and compliance logging for each prediction.
One analysis found the annual bill for an AI observability tool can rival or exceed the LLM API spend for a mid-market deployment, especially when retaining extensive logs for compliance.
Logging every prompt and completion generates massive amounts of data. Observability vendors charge by the gigabyte ingested, and those costs scale with usage.
For example, ingesting 10 GB/day into Azure Log Analytics at ~$2.30/GB comes to about $700/month just for raw log ingestion, before analysis, retention, or alerting.
What you can do
Budget AI observability as a separate line item. Estimate log volumes based on expected query frequency. Implement retention policies early; you don’t need detailed prompt logs indefinitely.
Dev/test environments need production-scale infrastructure
Traditional software can test with sample data, maybe 10% of production volume. AI systems need the whole corpus to validate accuracy.
You can’t test RAG quality with 50 documents when production uses 10 million. You can’t validate embedding retrieval without realistic data volumes.
That means maintaining a staging Azure AI Search service with a complete index, adding hundreds to thousands per month that weren’t in the pilot budget.
For traditional apps, dev/staging typically accounts for about 20% of the production infrastructure cost. For AI systems, practitioners report that it often accounts for 50-100% of production costs during active development.
You’re running duplicate vector databases, complete embedding pipelines, and full-scale indexes in non-production environments.
What you can do
Budget for multiple full environments from day one (production + staging). During development, assume 50-100% of the production infrastructure cost, not the 20% traditional software uses.
Maintaining embeddings requires monthly operational spend
Teams budget for “initial data ingest and embedding generation” as a project cost. They miss that maintaining embeddings requires continuous spending as documents update, models upgrade, or retrieval quality degrades.
Every time a document changes, you regenerate embeddings. When you upgrade to a newer model version, embeddings often become incompatible, requiring you to re-vectorize the entire corpus.
Azure bills vectorization operations per 1,000 tokens. Embedding 10 million tokens costs roughly $10 per run. Done monthly, that’s $120 annually, but scale it to an enterprise corporation, and it multiplies.
Storage costs compound, too. A million embeddings requires about 6 GB. As your index grows, you may upgrade Azure AI Search tiers just to accommodate size, jumping from $250/month to $1,000/month.
One case study showed ongoing re-indexing and monitoring costs reaching 50% of monthly AI operating expenses in production, with model inference accounting for the other half.
What you can do
Budget 10-15% of the initial setup cost monthly for maintenance. Schedule re-embedding for content older than 90 days. Factor in model upgrade costs when planning multi-year investments.
The teams that avoid budget surprises in Q2 are the ones who map their full AI infrastructure stack before finance locks the forecast.
Azure Cost Management’s resource tagging lets you attribute every dollar to vector storage, observability, staging environments, and data operations for specific AI projects before they go live.
You see where your $40K model budget actually needs to be $65K, with line-item breakdowns that finance can approve upfront.
Want to see what your AI infrastructure will actually cost? Get a free consultation.
