Retrieval-augmented generation answers a question by embedding it, searching a vector store, and generating from retrieved context — and each step costs tokens or query units. The provider bill aggregates with no client breakdown. Keito attaches RAG spend to the client, project, and task it served so retrieval-augmented work becomes a billable, reviewable record.
Pre-billing review
Agent cost this period
$12.1k
Active work
24
Reviewed
91%
Ready to bill
$18.4k
Time captured with context
rag pipeline cost tracking
Billing review complete
Client and project checked
Invoice evidence ready
Approved summary prepared
rag pipeline cost tracking needs more than a timer. The billing record has to keep client, project, approval, and invoice context together before the work reaches finance.
Record token fees, subscription usage, and compute costs against the client project they serve as the agents run — not reconstructed at billing time from company card statements.
Combine AI agent cost records with human billable hours in one review so the total delivery effort is visible before the invoice cycle starts.
Use reviewed human and agent cost data to prepare client summaries and invoice backup that reflect how the work was actually delivered.
Retrieval and generation cost
A RAG pipeline looks simple from the outside — ask a question, get an answer grounded in your documents — but each query quietly spends in three places: embedding the query and the documents, searching the vector store, and generating the final answer with a model. Indexing a large corpus adds an up-front embedding cost, and a chatty user or an agent that re-queries repeatedly multiplies the generation spend. For an internal tool that is fine; for client delivery it blocks accurate billing, defensible recharge, and any honest read of which engagement is profitable, because the provider and vector-store bills arrive as totals with no client breakdown. Keito treats each retrieval-augmented workload as billable work. The run is recorded against a client, project, and task, the cost is captured, and it routes through a review step before it reaches an invoice or client report. Because the same workspace holds the human billable hours for that engagement, retrieval spend and labour sit in one reviewed billing record, so a RAG feature becomes a per-client line you can explain rather than an opaque platform overhead.
Attribute embedding, vector-search, and generation spend to client and project
Separate one-time indexing cost from ongoing per-query spend
Review RAG cost alongside billable hours before client reporting
Workflow fit
Keito keeps rag pipeline cost tracking connected to client, project, billable status, approval, and invoice context before the work reaches finance.
Attribute embedding, vector-search, and generation spend to client and project
Separate one-time indexing cost from ongoing per-query spend
Review RAG cost alongside billable hours before client reporting
Keito tracks AI agent costs against the same client and project structure as human billable hours so agent spend is never invisible overhead. Agent sessions land as source-tagged time entries via the CLI, API, or Agent Skill, with LLM token costs logged as expenses.
See total delivery cost — human hours and AI agent costs together — by client and project so pricing, margins, and billing decisions reflect the real cost of work.
Keito flat pricing means adding AI tracking capacity to the billing workflow does not create a per-seat cost spike as more people and more agents are involved in delivery.
The difference is not just recording time. It is whether the record can support billing, project decisions, and client conversations.
Aggregate token and query bill vs per-client attribution
Keito keeps rag pipeline cost tracking tied to clients, projects, billable status, approvals, and billing summaries in one workspace.
Typical setups capture time in one tool and rebuild the billing explanation later from exports, comments, or spreadsheet cleanup.
Review before invoicing
Managers review entries before they become invoice evidence, so missing context is fixed internally rather than during a client dispute.
Raw timer exports usually reach finance before delivery leads have confirmed whether the work is billable, complete, or client-ready.
Predictable team pricing
Flat-rate plans let delivery staff, reviewers, contractors, and finance users participate without per-seat pricing friction.
Per-seat time trackers make teams choose between clean billing participation and controlling tool spend.
The best way to manage RAG pipeline cost tracking is to capture work at source, attach it to the right client and project, review it before invoicing, and use the reviewed record as billing evidence. Keito is built around that workflow so time, approvals, and invoice context stay connected.
Yes. Keito helps with RAG pipeline cost tracking by tracking work by client, project, task, person, billable status, and review state, then turning approved records into client-ready summaries. That makes the data useful for billing, profitability, and client reporting rather than just attendance.
Keito is different because it treats time as billing evidence, not just duration. A generic timer records how long something took; Keito records who did the work, where it belongs, whether it was reviewed, and how it should appear in client billing context.
Yes, RAG pipeline cost tracking can support billing clients for AI work when agent sessions, token costs, compute spend, and human review time are attributed to the right client project. Keito keeps AI costs and human time together so teams can explain total delivery effort before invoicing.
A client-ready RAG pipeline cost tracking report should include the client, project, task, contributor, billable status, approval state, and a concise explanation of the work completed. Keito keeps those details connected so reports can answer client questions without exposing internal delivery noise.
Solo includes 1 licensed user, unlimited AI agents, Mac desktop and iOS apps, Stripe payments, and standard CSV and Excel exports. Pro adds your team. Business adds integrations, planning, advanced reporting, and stronger controls.
1 licensed user
For independent consultants, freelancers, and small studios running work with AI agents.
Team collaboration
For teams that need shared workspaces, approvals, and team-level project visibility.
Advanced operations
For organizations that need integrations, planning, advanced reporting, and stronger controls.
AI agents do not require licenses on any plan.
Solo, Pro, and Business can use API keys for agent workflows.
Solo, Pro, and Business include standard CSV and Excel data export.
Start with Solo, add people on Pro when you need reviewers or collaborators, and see how Keito turns tracked effort into clearer reports.