AI Cost Optimizer

Cut AI cost by splitting retrieval from
generation.

Split the RAG pipeline to slash costs. AI Cost Optimizer
runs embedding and retrieval on economical models,
reserving premium LLMs strictly for answer generation.
Get major token savings without losing quality.

Trusted by Content-Critical Businesses Worldwide

KEY BENEFITS

Lower the bill, keep the quality

Major token savings

Right-size each stage to slash
cost on large data pools.

Premium where it counts

Reserve expensive models for the
generation step only.

Hybrid AI search

Combine semantic and keyword retrieval for accuracy at lower cost.

Optimized input management

Set fragment size and volume
limits to balance cost and quality

Choose your engines

Pick vectorization tools that fit language, performance and budget.

Predictable budget

Make AI spend plannable and scalable as data grows.

Enterprise-Grade Security & Full Compliance

Built on a robust infrastructure compliant with global security standards.

Unified Governance

Same governance and permission controls across both stages.

Data Flow Logging

Full audit logging of AI data flows and queries.

Hybrid Deployment

Works with local and cloud deployments

Framework Alignment

GDPR, HIPAA, ISO 27001 and NDMO alignment

Native Ecosystem Integration

Connect AI Cost Optimizer directly to your current data sources without changing your workflow or migrating your files.

USE CASES

Where cost control unlocks AI

High volume AI cost control

High-volume AI cost
control

Large corpus knowledge bases

Make organisation-wide RAG economically viable.

Mixed model strategy

Cheap embedding plus
premium generation, applied
across teams.

Scale without scaling costs

Grow data pools without
proportional token bills.

HOW IT WORKS

The right sized model for every stage

AI Cost Optimizer splits the pipeline so each stage uses the most cost-effective model for the job.

Cost-Efficient Indexing

Embed and index large
data pools with costefficient models.

Hybrid Search Retrieval

Retrieve the most
relevant fragments
using hybrid (vector +
content) search.

Compute Limit Control

Control input limits
fragment size and volume to manage compute.

Premium Model Generation

Generate the final
answer on your preferred premium
model, local or cloud.

Token Cost Optimization

Pay premium token
rates only at the generation step, not across the whole pool.
Ready to cut your AI bill?

Right-size every stage and make AI spend predictable with AI Cost Optimizer.

Frequently Asked Questions

Embedding and retrieval over large pools run on cheap models; only the final generation uses a premium model — so you pay top rates on a fraction of the tokens.

No. Generation still uses the premium model you select; only the lower-stakes retrieval stage is right-sized.

Yes. Use any preferred local or cloud LLM for the generation step.

Limits on the size and volume of fragments sent to the LLM, to balance cost and quality.

Yes. Both stages can run locally, in the cloud, or in a mix.