Aikar is an autonomous engineer built specifically for self-managed Spark on Kubernetes. Spark-application awareness meets K8s-native execution. We find the waste in your jobs, your storage, and your pod placement — and eliminate it. You only pay from the savings.
We don't try to optimize everything for everyone. We go deep on the platform other tools treat as an afterthought — self-managed Spark on Kubernetes, where the savings potential is highest and the existing tooling is weakest.
Data infrastructure costs are the fastest-growing line item at most engineering organizations. The waste is real and structural — but finding it requires deep expertise you can't easily hire, and fixing it requires touching production pipelines no one wants to risk breaking.
Hot data in Standard. Cold data in Standard. Forgotten data in Standard. Lifecycle policies that were never finished. Your S3 bill is paying premium for petabytes nobody has touched in months.
Default cluster configs sized for the worst-case job that ran in 2022. Skew nobody mitigated. Broadcast joins that should have been hash. You're paying for compute that finishes 10 minutes early on a 4-hour budget.
Your tables are fragmented across millions of tiny files. Every query reads metadata for an hour before touching real data. Compaction is on the backlog. It's been there for two quarters.
Other tools optimize K8s pods or Spark jobs or data storage. For self-managed Spark on Kubernetes, those three are inseparable — fixing pods without understanding shuffle gets you 20% savings; fixing all three together gets you 50%+.
Tier optimization, lifecycle automation, orphan detection, format conversion, partition layout, compaction strategy.
Spark configuration, skew remediation, join strategy, cluster autoscaling, query plan analysis, resource tuning.
Pod resource right-sizing, zone-aware placement, spot orchestration, bin-packing — all aware of how Spark actually runs.
Aikar is an autonomous loop, not a one-shot tool. It keeps optimizing as your data grows, your jobs change, and your costs shift. Every action it takes is logged, reversible, and tied to a measurable outcome.
Aikar hooks into your Spark history server, Kubernetes cluster APIs, cloud billing, and object storage metadata. We never need write access to start. Your security team will appreciate this.
Within 7 days you get a full assessment: every inefficient table, every over-provisioned job, every wasted dollar. Ranked. Quantified. Reproducible. This becomes your savings baseline.
For every optimization, Aikar shows the specific change, the expected savings, the risk profile, and a shadow-test result where applicable. Your team reviews and approves what to apply.
Aikar applies approved changes, monitors performance and parity, and reverts automatically if anything breaks. Every action is logged. Your savings are measured against the baseline, every month.
Aikar pilots are designed to produce measurable savings within the first billing cycle. Here's what our pilot customers see, on average, in the first 90 days.
No platform fees. No seat licenses. No annual contracts. Aikar takes a share of the measurable savings we deliver — verified against your baseline cloud bill.
Every action Aikar takes is shadow-tested, gated by your team's approval policy, monitored for drift, and reversible in a single click. Your production pipelines are not where we experiment.
Start with a read-only assessment. Nothing changes until you explicitly grant write access. Many customers stay in read-only for the first 60 days.
For compute optimizations, Aikar runs the new config in parallel with the old one and compares output parity before promoting the change.
If any optimized job exceeds defined performance or correctness bounds, Aikar reverts to the previous configuration automatically and alerts your team.
Every recommendation, approval, action, and rollback is logged with timestamp, actor, and diff. SOC2-ready out of the box. Your auditors will love it.
14-day assessment. Read-only. No commitment. We send you a baseline cost report with prioritized optimizations and projected savings. From there, you decide.