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Get 50% Databricks bills on same TCO with zero migration
No Engineering Effort
No Data Movement
Pay for vCPUs you use
Plug a compute acceleration and cost-saving layer. No re-tooling or SQL rewrites.
Deploy across multi-cloud, multi-region, and on-prem
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Why Do Databricks Cost Optimizations Hit a Ceiling?
The cost optimizations rely on step sizing and scaling on VM-centric, monolithic architecture that's inefficient for the highly concurrent & unpredictable SQL+AI workloads of today
Scales up/down at cluster-level with step-jumps. Even Auto-Scaling is reactive & coarse-grained
Requires over-provisioning or queuing that add up the costs.
High operational overhead for cluster tuning, sizing, ETL etc
ETL latency and Formatting overhead. Storage adds up to the bill.
Egress and Network costs for un/loading data
(DBUs consumed × DBU rate)Cloud infrastructure cost
How e6data Pushes Beyond These Limits
The cost optimizations rely on truly distributed, Kubernetes-native architecture with granular sizing and scaling (~vCPU level), purpose-built for concurrent & unpredictable SQL+AI workloads of today
Scales up/down at vCPU-level with guardrails in place
Designed for independently scaling micro-services
Negligent due to automated cluster management, provisioning, & cost guardrails.
Compatible to all formats & catalogs, be it Iceberg or Delta lake
Location-aware execution cuts down data movement and egress costs
Pay only for vCPUs you use
Databricks X e6data
How It Works
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Plug e6data. Swap your expensive SQL Analytics + AI workloads.
Pay only for vCPUs you use
See proof of savings within 6-8 weeks
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Cut Your Costs Across Query, ETL, and Ingest
Atomic Scaling
No idle costs. Ever
In-Place Execution
Good-bye to latency or storage overhead
Converged Compute
One engine for SQL+AI.
Policy-Informed Guardrails
Block bad jobs early
Cut Down Infra-Costs
No sync or format conversions. Fewer pipelines
No ETL Jobs
Cut duplicate streaming or transform
ACID & Exactly-Once Delivery
Avoid corrective compute
No Storage & Indexing
Write once. Query directly on lakehouse
No Always-on Streaming
Compute spins only when needed
Single Copy of data
Parallel computing doesn't need parallel copies
Minimal Orchestration
Zero schedulers or retries
Uniform Governance
Avoid costly penalties
Near-Zero Egress Fees
No cross-region or cross-cloud data movement
Lower Storage & Replication Overhead
No data duplication
Eliminate Wasted Compute
Location-aware compute requires minimal data transfers
The Use-Case Basis Impact on Your Data Spend
Run heavy transforms directly on your lake—no extra clusters, no staging copies, no idle compute.
Ingest events once and query them instantly. No stream processors, no duplicate pipelines.
Handle sudden query spikes by scaling only what’s needed, without paying for peak capacity 24×7
Search and analyze logs without data movement or infrastructure sprawl.
Analyze data where it lives and avoid costly replication and cloud egress fees.
Run large one-off queries without increasing your regular cloud costs.
Use fresh data directly from the lake for models and analysis without building separate serving layers.
Run security analytics without duplicating sensitive data or scaling up compute
Search embeddings without without new infrastructure or data copies.
Check the difference before you commit
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FAQs
$1M
savings per quarter
Run anywhere
Public cloud, private cloud, hybrid
Agent-native
by design
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