Cut compute and power consumption effortlessly

DS Pro runs silently in the background, slashing energy draw and computational overhead across enterprise infrastructure with zero code disruption.

Pricing

Intelligent background optimization

Modern hyperscale facilities and enterprise compute environments face soaring electricity rates and intense hardware thermal loads. DS Pro introduces autonomous low-overhead execution that streamlines CPU and memory resource scheduling in real time without altering your stack.

Product capabilities & core modules

DS Pro

DS Pro v8.2 — compute acceleration for AI data centers

We don't estimate.
We instrument.

DS Pro sits inside the retrieval, dedup, embedding-cache, log-intelligence, and GPU-shard-routing paths an AI data center already runs, and only takes the faster path when it measures one.

14/14 measured wins at data-center scale self-disabling on loss zero-install client package

Median gain, all 14 capabilities95.2%

Average gain, all 14 capabilities93.7%

CPU work cut per job (6 of 8 workloads)93–99.9%

Shard Router vs. FAISS at 100K records21x faster, same answers

Manual config requirednone

The proof log — real data-center scale

Fourteen capabilities, measured at real scale.

Full BEIR SciFact (1,000 docs) and CodeSearchNet (1,500 docs) corpora, 596,775 log lines from the public Alibaba GPU-cluster trace, a 3,000,000-row dataset, and 2,000 checkpoint files.

Measured 2026-09-20

Capability	Measured against	Baseline	DS Pro	Gain
RAG Index Reuse Layer	SciFact + CodeSearchNet (2,500 docs)	1.614 s	1.0 ms	99.9%
Data Pipeline Profiler	3,000,000-row dataset	24.413 s	19.4 ms	99.9%
Cluster-Wide DS Pro Agent	SciFact + CodeSearchNet (2,500 docs)	1.604 s	1.5 ms	99.9%
Training Log Intelligence	Alibaba GPU trace (596,775 lines)	5.324 s	13.6 ms	99.7%
GPU Starvation Detector	Alibaba GPU trace (596,775 lines)	3.472 s	14.5 ms	99.6%
Embedding / Vector Cache	SciFact + CodeSearchNet (2,500 docs)	2.527 s	94.0 ms	96.3%
Compressed Dataset Manifest	SciFact + CodeSearchNet (2,500 docs)	2.334 s	95.5 ms	95.9%
Hot Shard Cache	SciFact (1,000 docs)	0.713 s	39.3 ms	94.5%
Checkpoint Dedup / Cleanup	CodeSearchNet (2,000 checkpoints)	1.439 s	83.2 ms	94.2%
Model Artifact Lifecycle Manager	CodeSearchNet (2,000 checkpoints)	1.452 s	97.6 ms	93.3%
Dataset Deduplication Before Training	CodeSearchNet (2,000 checkpoints)	1.480 s	99.8 ms	93.3%
Pre-Tokenization Cache	SciFact (1,000 docs)	0.730 s	52.4 ms	92.8%
Bad Data Quarantine	SciFact + CodeSearchNet (2,500 docs)	1.146 s	0.252 s	78.0%
Training Data Integrity Gate	SciFact + CodeSearchNet (2,500 docs)	1.155 s	0.294 s	74.5%
Median / average gain, all 14				95.2% / 93.7%

Measured — every capability measured after its index exists, the state a running data center is in. Dataset filtering and signature scanning now reuse their work across runs too (see below), which is what moved Data Pipeline Profiler and the two integrity capabilities.

Measured compute savings

Less CPU work per job, measured by Windows itself.

CPU-seconds used by DS Pro and every worker it starts, per completed job. Three 20-second runs per mode, alternating order.

Workload	Time / job, normal	Time / job, DS Pro	CPU-s / job, normal	CPU-s / job, DS Pro	CPU work saved
Semantic search	1.826 s	3.0 ms	1.806	0.0029	99.8%
Embedding pipeline	2.670 s	0.114 s	2.556	0.1113	95.6%
Dedup backup	1.526 s	0.134 s	1.482	0.1057	92.9%
Dedup hash acceleration	1.584 s	0.122 s	1.530	0.0982	93.6%
Log analytics	3.753 s	25.9 ms	3.656	0.0172	99.5%
Signature scan	1.240 s	0.289 s	1.219	1.4466	4.3x faster, +19% CPU
Dataset filter (3M rows)	26.126 s	18.3 ms	25.773	0.0181	99.9%
GPU shard routing (2,500 vectors)	0.3 ms	0.5 ms	0.000	0.0006	No gain at this size

Measured — 2026-09-20, Intel i7-2600 / GTX 1660 SUPER. GPU draw stayed within 0.35 W of idle in both modes on every workload. GPU shard routing is measured on a 2,500-vector index, far below the size where its GPU path pays off.

Accuracy

Faster, with the same or better answers.

Public retrieval benchmarks: does the right document land in the top 10 (Hit@10)?

Benchmark	Docs / queries	Normal, median	DS Pro, median	Faster	Hit@10, normal	Hit@10, DS Pro
BEIR SciFact	1,000 / 20	0.810 s	5.2 ms	99.4%	80%	90%
CodeSearchNet	1,500 / 20	1.049 s	75.6 ms	92.8%	100%	100%

Measured — 2026-09-19.

Real-workload validation

370 real source files (Python, JavaScript, TypeScript, Markdown) from a live multi-project codebase, 30 real developer queries.

0.456 s

Normal, median per query

2.5 ms

DS Pro, median per query

100%

Same top result as the trusted index (30 / 30)

0 / 370

Source files changed (SHA-256 before and after)

Measured — 2026-09-19.

What's inside

Power-saving features and data-center safeguards.

Semantic power gravity placement

Groups documents by topic into shards, so a query touches only the shards it needs instead of the whole index.
Predictive semantic pre-wake

Learns which query usually follows which, and readies the next shard before the follow-up arrives.
Semantic query dedup

Reuses a just-computed result for a near-identical query over the same data, instead of recomputing it.
Compute fidelity budgeting

Trims scoring work only for query shapes with a proven track record, and never for latency-critical queries.
Digital RAM

One managed cache pool for every task's indexes and stores, sized automatically from 1 to 64 GB as load grows.

Integrity guard

SHA-256 of every file before and after each data-center job. Proven with 8 simultaneous jobs: 160 / 160 file records kept.
Pressure throttle

Backs off on storage latency over 250 ms, network latency over 150 ms, I/O queue depth over 64 per node, object-store errors over 3%, or retries over 8%. Busy CPUs alone never trigger it.
Locked-down API

API key on every call, local-only by default, 10 MB request limit, and malformed input rejected with a clear error.
Pattern-attack protection

Search patterns known to hang regex engines are refused before they run, and log searches run under a hard time limit.

Power features, benchmarked

300 queries shaped like real RAG traffic over 400 documents in 10 topics.

Scenario	Median / query, before	Median / query, DS Pro	Results reused	Pre-wake hits / misses	Storage power saved
Bursty, repeated queries	37.2 ms	1.02 ms	212 / 300	0 / 0	71.2%
Sequential follow-up queries	—	1.34 ms	230 / 300	39 / 0	76.7%

Measured query times and counts Modeled storage power saved — 2026-09-20.

GPU Shard Router

Confidence-gated routing over a GPU-resident sharded index.

When one shard is confidently closest, DS Pro searches only that shard. Otherwise it searches them all.

Picks CPU or GPU by live measurement

Times both paths on every index build and runs on whichever is faster.
No GPU? Falls back cleanly

Runs on CPU automatically and reports it in its status.
Sized to the machine it runs on

Vectors stay in RAM while they fit and move to disk when they don't; each GPU takes as many shards as its free memory holds, and the rest are searched from host memory. A 250,000-vector index builds in 100 MB of RAM when only 64 MB is free.
Routing never changes the answer

A shard is skipped only when its best possible score can't reach the top of the results.
Uses only real data

Searches DS Pro's own embedding index, built from the client's files.
Zero manual configuration

A background watcher finds running AI services and their data, and gates all 9 tasks automatically.

Self-protection, proven live

Test scenario80 queries, 20,000 records

Avg. routed time3.07 ms

Avg. exhaustive time0.70 ms

Resultrouting was slower

auto_routing_enabledFalse

It measured itself losing and turned itself off.

Deployment

One file a client can actually run.

Fully self-contained

Python, numpy, and CUDA-enabled torch ship inside the package. Nothing to pre-install.
Live power tracker

Shows estimated CPU and GPU energy saved, from each job's measured time saved.
Per-pilot hardening

Every build carries a unique fingerprint and refuses to run under a debugger.
Works from any folder depth

Deep install folders and data paths past Windows' 260-character limit are fully supported.

Live launch, 2026-09-21

PlatformsWindows + Linux

Tasks completed9 / 9

License / tamper checkpassed

Automated tests201 / 201 passing

One launcher file. Double-click it.

How it compares

Head-to-head against the standard tools.

Same machine, same data, same queries.

GPU Shard Router vs. FAISS

Records	Engine	Hardware	Technique	ms / query
4,000	FAISS	CPU	Flat (exact)	0.31
	FAISS	CPU	IVF, nprobe=1 (64% top-scoring)	0.03
	DS Pro Shard Router	CPU (self-selected)	exhaustive (exact)	0.27
	DS Pro Shard Router	CPU (self-selected)	confidence-gated (exact)	0.30
100,000	FAISS	CPU	Flat (exact)	15.54
	FAISS	CPU	IVF, nprobe=1 (79% top-scoring)	0.84
	DS Pro Shard Router	GPU (self-selected)	exhaustive (exact)	0.72
	DS Pro Shard Router	GPU (self-selected)	confidence-gated (exact)	0.66

Measured 300 queries over real DS Pro vectors. Exact against exact: DS Pro beats FAISS-Flat at both scales, by 1.1x at 4,000 records and 21x at 100,000. FAISS's approximate IVF search is quicker at 4,000 records, but returns a top-scoring document only 64% of the time there and 79% at 100,000; every DS Pro number in this table is the exact answer.

Semantic Search vs. rank_bm25

Engine	ms / query
rank_bm25 (standard library)	3.14
DS Pro semantic_search	2.35

Measured 1,200 documents, 100 queries run alternately against both engines, each measured after its index was built: median 2.35 ms against rank_bm25's 3.14 ms (1.3x). Both found the target document in the top 10 for every query.

Dataset Filtering vs. DuckDB

Trial	DS Pro (ms)	DuckDB (ms)	Winner
1	9	206	DS Pro
2	10	188	DS Pro
3	10	202	DS Pro
4	10	177	DS Pro
5	10	190	DS Pro

Measured 200,000 rows of quoted, multi-line CSV, identical results every trial. DS Pro averages 10 ms against DuckDB's 193 ms (20x), answering from its dataset index. Building that index on first sight of the file takes 0.67 s; DuckDB re-reads the CSV on every query.

Signature Scanning vs. YARA

Engine	Seconds (20,000 files)
DS Pro signature_scan, first scan	4.98
DS Pro signature_scan, rescan of unchanged files	2.07
YARA (yara-python)	5.60

Measured 20,000 files, 6 signatures, 315 findings — identical to YARA's. First scan reads every file (1.1x YARA); a rescan re-reads only files whose size, modification time or change time moved (2.7x). That change time is the one the operating system controls - the inode change time on Linux, the NTFS change time on Windows - so an edit cannot hide behind restored timestamps, and a file altered while it was being read is scanned again next time.

Vector database platforms

Platform	Model	GPU indexing	Typical cost
Pinecone	Managed only, no self-host	Not exposed to users	Usage-based managed pricing
Weaviate	Cloud or self-host	Not a headline feature	Cloud from ~$25/mo
Qdrant	Self-host (single binary) or cloud	GPU-accelerated HNSW builds (v1.14+)	Cloud from $0.014/hr/node; self-host ~$96/mo
Milvus	Self-host or Zilliz Cloud (managed)	GPU_CAGRA index, built for 100M+ vectors	Self-host ~$300–600/mo; Zilliz Cloud ~$80–150/mo per 1M vectors

Researched

FinOps / cost-optimization pricing models

Tool	What it does	Pricing model
CAST AI	Automated rightsizing / Spot management	~15–20% of realized savings
Vantage	Cloud cost visibility + Autopilot optimization	Free–$250/mo flat, Autopilot at 5% of savings
Kubecost	Cost attribution / chargeback	Scales with monitored vCPUs (~$680/mo at 200 vCPUs)

Researched

Getting it running

Product	What it takes to get running
DS Pro	One launcher file. Double-click it. No account, no config, no separate Python/CUDA/torch install.
Pinecone	Create an account, provision an index, write ingestion code
Qdrant	Run the binary or a container, then write client code to create a collection and load vectors
Weaviate	Docker Compose deployment, then configure a schema and write ingestion code
Milvus	Requires standing up a Kubernetes cluster
CAST AI	Account + `kubectl` + `helm`, then run a cluster-onboarding script
Vantage / Kubecost	Install an agent into your Kubernetes cluster, connect cloud billing accounts
FAISS / DuckDB / YARA / rank_bm25	Libraries — you build the application and deployment around them

Researched

Data-center simulations

Today's measured gains, run through a full data-center model.

Both simulations take the 14 measured results above as input, with job mixes shaped by the public Alibaba GPU-cluster trace.

Result	Full AI data center	Initial-plan data center
Total runtime gain	64.9%	72.0%
Storage I/O reduction	59.2%	72.8%
Storage queue wait reduction	—	79.5%
GPU wait reduction	none	87.6%
Cache hit rate	94.0%	67.6%
Repeated work share	89.0%	74.3%

Modeled — 2026-09-21, from the 14 measured results above.

Automated tests

201 automated tests, all passing.

Area	Tests
Index, cache, and store integrity (embedding store, dedup manifest, log index, dataset index, Digital RAM, result freshness)	50
Reliability (deep paths, locked files, concurrent jobs, integrity guard, power tracker, task routing, busy port, pilot clock)	37
Accelerated results match normal results (dataset, logs, dedup, scan, embed, search ranking and ties)	35
Autonomous watcher and process discovery	29
Regression tests for fixed issues	19
Security and input safety (pattern attacks, request limits, fuzzed input, license protection)	18
GPU Shard Router	13
Total	201 / 201

Measured — 2026-09-21, plus live runs of all 9 tasks on Windows and Linux.

What it costs, what it's worth

The numbers behind the value.

Modeled monthly savings, by data-center scale

"Eligible share" is the part of spend on workloads DS Pro accelerates.

Monthly cloud bill	Eligible share	Est. bill savings	Est. $ saved / mo
$250,000	10%	4.2%	$10,546
$1,000,000	25%	10.5%	$105,462
$5,000,000	25%	10.5%	$527,312
$20,000,000	40%	16.9%	$3,374,800

Modeled — from the 2026-09-21 full data-center simulation (64.9% workload gain, 65% confidence adjustment).

If priced at 20% of savings delivered

Customer bill / mo	Modeled savings / mo	Revenue / mo @ 20%	Illustrative ARR
$250,000	$10,546	$2,109	$25,311
$1,000,000	$105,462	$21,092	$253,110
$5,000,000	$527,312	$105,462	$1,265,550
$20,000,000	$3,374,800	$674,960	$8,099,520

Illustrative

The evolution of sustainable compute

DS Pro was created to solve one of the technology sector's most pressing crises: skyrocketing power requirements. By operating at the foundational runtime layer, we transform existing server infrastructure into lean, green compute engines without forcing hardware upgrades.

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Sunday: Closed

Direct inquiries

Support@Dsprosuite.com