Find the AI workloads you can run for less.
Recurring jobs create recurring costs. Start with work that can use available capacity across your company’s machines, then compare the results against your current provider.
Lower the cost of AI that runs every day.
Agents
Research, enrichment and document-processing agents make many model calls per run. Test suitable steps on supported local models, and keep demanding steps on the model they require.
First pilot: one scheduled agent with clear acceptance criteria.
Measure: cost per successful run, completion time, failure and retry rate, output quality.
- Pick one agent that runs unattended: a nightly researcher, a triage bot, an enrichment step.
- Point its client at the kibbu endpoint, key and a supported model. Same SDK, same prompts.
- Keep model and response-time requirements explicit for each step, and route demanding ones to cloud.
- Compare cost per successful run against your current provider, then move the next agent.
Evals
Use idle machines for repeated open-model inference and supporting evaluation tasks. If you are testing a specific proprietary model, its calls still need to reach that model.
First pilot: a fixed test set on a supported open model.
Measure: cost per completed suite, reproducibility, completion time.
- Point the eval harness at kibbu in your CI environment variables.
- Keep calls to the models under evaluation intact, and run the supported open-model grid on the fleet.
- Run the suite off-hours so more machines are idle and plugged in.
- Compare cost and wall-clock time against your last cloud-only run. A change of judge model is not an identical evaluation.
Embeddings
Use available hardware for bulk embedding generation and planned re-indexing. Choose the embedding model deliberately and validate retrieval performance.
First pilot: one corpus with a retrieval test set.
Measure: cost per indexed document, indexing time, retrieval quality.
- Choose a supported embedding model and pin it for the job. A model change means re-embedding the corpus.
- Swap the endpoint and key in your indexing script. Batch sizes stay as they are.
- Queue the backlog and let the fleet drain it when machines are available.
- Check retrieval quality against your test set, then re-index on the same schedule.
Synthetic data generation
Training rows, eval cases and test fixtures are produced in bulk and nobody waits on the response. Generate them on supported open models, and check the licence covers the use you have in mind before the output becomes a dataset.
First pilot: one generation run against a seed set you already have.
Measure: cost per accepted record, acceptance rate after filtering, duplication, downstream results.
- Pick one set you generate today: eval cases, test fixtures, augmented training rows.
- Point the generation script at the kibbu endpoint, key and a supported open model whose licence covers the use.
- Run it in batches when machines are idle, with your existing dedupe and validation filters on the output.
- Compare cost per accepted record and downstream results against your current provider, then generate the next set.
Batch processing
Run extraction, tagging, classification and summarization jobs on idle machines. Match schedules to measured availability and required completion times.
First pilot: one repeatable document-processing batch.
Measure: cost per accepted output, throughput, retries, deadline completion.
- Start with one job: extraction, tagging or a summarization pass.
- Change the endpoint and key in the job config. The schedule stays as it is.
- Run it when suitable machines are available and the deadline allows.
- Compare cost per accepted output against your current provider, then move the rest of the batch.
Start with a workflow you already run.
All four speak OpenAI. Test one workflow with kibbu, compare output quality, runtime and spend, then roll out.
Databricks
Reduce inference spend in suitable data-processing jobs. Start with a notebook or pipeline that can call a configurable model endpoint.
The change: Point the serving endpoint or notebook client at the kibbu base URL. The job definition stays as it is.
n8n
Lower the model cost of recurring automations. Test one workflow with kibbu before applying the configuration more widely.
The change: Edit the OpenAI credential once. Every workflow using it follows.
Dify
Explore lower-cost inference for supported app and agent workflows. Choose the model and validate the capabilities your app uses.
The change: Add kibbu as an OpenAI-compatible provider, then pick it per app.
Langflow
Test a lower-cost model endpoint in an existing flow. Compare output quality, runtime and spend before rollout.
The change: Set the base URL and key on the OpenAI component. The rest of the flow is untouched.
Find your first repeatable saving.
Bring one workflow and its current costs. We’ll help you evaluate its fit for your existing hardware.
