What does a production readiness review of an LLM feature cost?
Updated: 2026-10
The cost of reviewing an LLM feature depends on how many features are reviewed, whether they only generate text or act through tools, how sensitive the data is, whether tests and an evaluation set already exist and whether the feature falls under AI Act obligations. A fixed scope after a short introduction is common.
A review before launch checks what a demo does not show: prompt injection attacks, quality against a fixed evaluation set, data flows and contracts, cost per use, monitoring and AI Act classification. Effort follows the feature’s complexity, not the size of the product.
A simple assistant summarising text is reviewed faster than an agent that calls tools, accesses customer data and triggers actions.
What drives the cost
Number of features
Each LLM feature has its own inputs, data sources and risks. Several features sharing an architecture can be reviewed more efficiently than as many independent ones.
Tool access and agents
Features that query data or trigger actions through tools require a much deeper review of permissions and attack surfaces than text-only features.
Data sensitivity
Personal data, confidential business data or data from several tenants raise the requirements for data flow, contracts and tenant isolation.
Existing tests
If tests and an evaluation set already exist, the review is shorter. If not, building them is part of the scope.
AI Act relevance
Features with transparency obligations or possible high-risk relevance need more careful classification and documentation.
Implementation support
Whether the team implements measures itself or wants support determines scope after the review.
How we price
- 01
Fixed-scope review
Introduction, analysis, measurement and a prioritised action plan at a price agreed in advance.
- 02
Review with implementation support
Additionally, support with architectural measures, evaluation and cost control.
- 03
Repeat on change
Re-measurement and review after model changes or major changes to prompts and tools.
We deliberately publish no online prices: every case differs. After a short first conversation you receive a binding fixed price or clear frame.
Parent service: SaaS Product Development
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Production readiness review for LLM features
The review checks an LLM feature before launch for what demos do not show: prompt injection attacks, quality measured against a fixed evaluation set, data flows and contracts with model providers, cost per use, monitoring and AI Act classification. The result is a prioritised action plan.
From AI or no-code prototype to a production web app
AI app builders and no-code tools produce convincing prototypes quickly, but rarely a foundation for operations. We analyse the prototype, take its flows, screens and learnings as a specification, and rebuild the application on Next.js and Supabase with proper permissions, tests and data protection.
FAQ
Is a review worth it for a small feature?
If it processes customer data, is externally visible or triggers actions, yes. For purely internal experiments the production readiness checklist is often enough.
What is not included?
A full application penetration test is a separate service from specialised providers. The review focuses on LLM-specific risks and architecture.
Do we keep the evaluation set?
Yes. The set and evaluation scripts are yours and can be adopted into your own pipeline.