>bytevion_
private beta · v1.0

The optimization layer for agentic LLM workloads.

Sits between your app and OpenAI, Anthropic, Gemini, and 8 more. Routes each call to the cheapest model that can handle it. Caches what it can. Verifies what it can't.

> Request a keyRead the docs
root@bytevion:~
Your Application Layer
LLM Provider Layer
OpenAI · Anthropic · Gemini · Grok · +6 more
$

Backed by

░▒▓Production▓▒░

Three numbers, on production traffic

1M requests through the Render pilot. 743K through Uniphore APAC. Here is what changed.

+11.1%

Better Quality

Accuracy improvement through smart routing and verification

0.72 → 0.80 quality score, Render pilot

62.4%

Lower Latency

Faster responses via caching, context compilation, and routing

2,847ms → 1,038ms, Uniphore APAC

58.6%

Reduced Cost

Output reuse, context trimming, and budget-aware serving

~58.6% avg savings, production pilots

Routes to the cheapest model that fits

Every call is classified by task complexity and quality requirement, then routed to the smallest model that can handle it within your budget. No code changes; the client is a drop-in replacement for the OpenAI Chat Completions shape.

Smart model routingBudget aware servingWorkflow planner
routing-engine
$ bytevion route --analyze
▸ Request analyzed: coding/explain
complexity: low
cache_match: 87% similarity
budget: $0.001/req
▸ Decision: gpt-4o-mini (was: gpt-4o)
savings: ↓ 58% cost | ↓ 59% latency
accuracy: ↑ maintained

Trims the prompt before the call

Compiles redundant context out of prompts and reuses cached outputs when a semantically equivalent answer already exists. A 4,280-token prompt compresses to 2,433 tokens with zero semantic loss in the example below.

Context compilerDelta generationStrict cache revalidation
context-compiler
$ bytevion compile --diff
▸ Input analysis:
original tokens: 4,280
redundant: 1,847 tokens (43%)
cached segments: 2 matches
▸ Compiled output:
final tokens: 2,433 (↓ 43%)
semantic loss: 0.00%

Re-runs when confidence drops

Memory verification, consensus check, and evidence validation run before the response is returned. If confidence falls below your threshold, the call is escalated to a stronger model. Result includes a numeric confidence score, not just a pass mark.

Execution verified memoryConsensus verificationEvidence aware verification
verification-pipeline
$ bytevion verify --pipeline
▸ Pipeline running:
[1/4] Memory verification ✓ pass
[2/4] Consensus check ✓ 3/3 agree
[3/4] Evidence validation ✓ sourced
[4/4] Gap detection ✓ complete
▸ Result: VERIFIED confidence: 0.94
░▒▓Benchmarks▓▒░

Horizontal proof, not a single market

Two signed production pilots and four reproducible workloads. Includes the cases we lose.

Production

Render · production pilot

Render.com, mixed workloads

1,000,000 requests in 24 hours

$16,026 saved · 3.7 day payback
DirectBytevionDelta
Cost
$27,348$11,322-0.0%
Latency
baseline-62.4%-0.0%
Quality
+11.1% score, 68.3% fewer errors
0.720.80+0.0%

Production tabs use real customer pilot data. Synthetic tabs use OpenAI coding benchmarks and published per-request examples.

░▒▓Integrations▓▒░

Works With Your Stack

Drop-in support for 10+ LLM providers. Text, vision, document, and audio inputs, plus image generation, transcription, and more.

OpenAI
Anthropic
Gemini
Groq
xAI
OpenRouter
Ollama
Mistral
Cohere
Bedrock
Llama.cpp
Supported Inputs
TextVisionDocumentAudioImage GenTranscriptionSpeechModeration
░▒▓Integrate▓▒░

Three minutes to a routed call

Install. Set your provider keys. Done.

bytevion-cli

Subscription plus BCUs.
Provider costs pass through.

List price is $0.010 per Byte Compute Unit. Bring your own model keys, or let us handle procurement. Savings share stays an enterprise rider, not the default invoice line.

> roi_estimatordollar savings projection
$/ mo

A balanced production load across support, code, and documents.

monthly savings
$11,855(57.4% blended)
annualized
$142,260
recommended plan
Growth
direct spend (now)$25,000
bytevion total (estimated)$13,145
payback window6 days
> request a key
estimates use published per-request savings. real numbers depend on your traffic. enterprise contracts add a verified-savings rider.
> cost_estimator$0.010/BCU · multi-workload
workloads in your mix
1,000,000 req/mo

20K input, 2K output, large reusable context

quick volume
provider costs
workloads in mix1
total bcus930,000
planGrowth
included bcus350,000
overage bcus580,000
overage rate$0.008/bcu
platform fee$2,500
bcu overage$4,640
provider passthrough$17,500byok: not billed
monthly bill$7,140
vs direct path$90,000
you save$65,360 (-72.6%)
annual projection$784,320 saved / yr
bcu meter definitions are public. read them. negative savings are not clamped in invoice-grade ledgers.

Self-serve plans

Developer

$0/mo
Included
0
Overage
$0.012/BCU
  • ›Gateway access
  • ›Basic routing
  • ›7 day telemetry
  • ›Community support
Start free →

Team

$499/mo
Included
50,000 BCUs
Overage
$0.010/BCU
  • ›Dashboards and alerts
  • ›Cache policy
  • ›30 day telemetry
  • ›5 seats
Request access →

Growthrecommended

$2,500/mo
Included
350,000 BCUs
Overage
$0.008/BCU
  • ›Workload policies
  • ›Eval sampling
  • ›Optimization reports
  • ›90 day telemetry
Request access →

Scale

$10,000/mo
Included
1,800,000 BCUs
Overage
$0.006/BCU
  • ›SSO and RBAC
  • ›Advanced cache
  • ›Policy exports
  • ›Support SLA
Talk to sales →
Enterprise

Annual platform fee plus committed BCU drawdown.

For regulated buyers and high-volume production accounts. Optional verified savings share rider, with quality floor and confidence threshold agreed in writing before traffic begins.

> Talk to sales
Annual platform
$50K to $250K+
Committed BCUs
$100K+ annual drawdown
Committed BCU rate
$0.004 to $0.007 per BCU
Private deployment
$75K to $500K annual premium
Verified value share
5% to 15% of qualified savings
Professional services
$20K to $150K one-time

prices effective april 2026. volume discounts available on request.

░▒▓Notes▓▒░

Research and engineering notes

Surveys, deep-dives, and drafts. Ask if you want a long-form early.

all notes →
Research2026-04-10

Adaptive Semantic Caching: One Threshold Isn't Enough

A single global similarity cutoff is a blunt instrument across model families and workloads. A read of the recent literature on adaptive, per-embedding reuse bands: what the research suggests, and where one-size-fits-all thresholds tend to break.

Read note →
Engineering2026-04-02

KV Cache Quantization: A Tour of the Trade-offs

Angular coding, JL projections, residual coding. Each family of KV quantization codecs shines under different constraints and breaks under others. A survey of the landscape for anyone weighing options for long-context inference.

Read note →
Engineering2026-03-28

Prompt Module Drift: The Hidden Cost of Prefix Caches

Modular attention reuse is fast, until a module shifts by one token and silently poisons downstream completions. A look at the boundary-stability problem and what the research community has proposed for versioning reusable prefixes.

Read note →
Engineering2026-03-22

Smart Routing as a Classification Problem

Picking the right model per request is fundamentally a classification task, and the training signal is already sitting in your logs. A survey of approaches to learned routing and why response feedback tends to beat hand-tuned rules.

Read note →
Research2026-03-15

Schema-Safe Prompt Compression

Query-aware pruning works well on free text and degrades predictably on structured prompts. Notes on why entity and schema preservation often matter more than raw reduction ratios, and what the research suggests about measuring both.

Read note →
Product2026-03-08

Context Compilation: Framing the Problem

Trimming tokens without losing meaning is the core problem behind nearly every cost-reduction story in production LLMs. A high-level framing of why it's harder than it looks and which research directions we find most promising.

Read note →
Product2026-03-01

Selective Augmentation: Shipping RAG Without Silent Regressions

Retrieval rankings are noisy, and any filter that drops weak evidence can quietly drop the one critical document on a bad ranking day. A look at the research on selective compression for RAG and why evaluation harnesses matter as much as the filters.

Read note →
Research2026-02-22

Benchmarking LLM Pipelines Without Fooling Yourself

Small prompt sets mislead, replicates are usually missing, and dataset contamination is easy to miss. A methodology-focused write-up on how to compare direct API calls, native caches, and compiled pipelines in a way that survives scrutiny.

Read note →
Engineering2026-02-15

Rethinking KV Eviction for Attention Caches

LRU is the wrong default for attention caches. A read of the research on attention-aware eviction, and why preserving the tokens models actually look at can compound memory savings across decoder layers on long-context workloads.

Read note →
░▒▓Team▓▒░

Three people built this

Abhiraj Anil

Abhiraj Anil

Co-Founder & CEO

Sriharshitha Earavelly

Sriharshitha Earavelly

Co-Founder & COO

Bhumika Sharma

Bhumika Sharma

CTO