Dnyana.dev

Ship GenAI faster. Pay less. Observe everything.

The developer-first GenAI platform

Welcome. Dnyana.dev is a GenAI infrastructure platform built for developers who ship fast in the rapidly evolving AI landscape.
Market context: Global GenAI market reached $67B in 2024, projected to hit $207B by 2030 (42% CAGR). Enterprise adoption accelerating—89% of organizations now experimenting with GenAI (Gartner, Oct 2024).
We solve three critical problems exacerbated by this rapid adoption: cost unpredictability, integration complexity, and observability gaps.
This deck is for technical founders and engineering leads navigating the post-GPT-4 era where LLM infrastructure has become table stakes.
Recent trend: 67% of startups now use multiple LLM providers (up from 34% in 2023) to avoid vendor lock-in and optimize for cost/performance—this is exactly the orchestration challenge we solve.

The Problem: GenAI Integration Reality

💸

Cost Unpredictability

LLM costs vary 10–100× between models (GPT-4o: $2.50/1M input tokens vs Llama 3.1: $0.10/1M). Without centralized control and caching, monthly bills can balloon from $5K to $50K+ with no warning. No per-user/per-project quotas means finance discovers overruns weeks after they happen.

⏱️

Latency Variance

P95 latency can spike from 200ms to 3-5 seconds during provider throttling or model overload. No automatic fallbacks means users hit loading spinners and abandon flows. Streaming helps but doesn't solve the root issue: single points of failure with no intelligent routing.

🔀

Vendor Sprawl

Teams integrate OpenAI (Python SDK), Anthropic (REST), Cohere (Go client), Mistral (JS)—each with different auth patterns, error codes, rate limits, and retry logic. Maintenance burden grows exponentially. Provider migrations take weeks. Vendor lock-in is real.

🕵️

Opaque Usage

No per-request tracing = debugging black box. Which user sent what prompt? What did it cost? Was PII included? Logs scattered across 5 vendor dashboards. Security audits become nightmares. Compliance teams can't certify what they can't see. Data lineage is impossible.

These are real pain points documented across 100+ customer conversations in 2024, with severity increasing as adoption scales.
Cost: One YC company went from $5K to $80K/month in 6 weeks—no visibility until AWS bill arrived. Industry trend: Average AI spend per company increased 340% year-over-year (2024 vs 2023). Without controls, runaway costs are the #1 blocker to production deployment.
Latency: Another startup saw 40% user drop-off during OpenAI's major outage (June 2024, 3.5 hours)—no fallback to Anthropic. Recent incidents: Claude outage (Sept 2024, 2 hours), Cohere degradation (Aug 2024, 5 hours). Multi-provider failover is no longer optional—it's infrastructure hygiene.
Vendor sprawl: Engineering teams waste 20-30% of time managing different LLM integrations. New challenge in 2024: OpenAI deprecated 5 legacy models, Anthropic changed rate limit headers, Google reorganized Gemini API pricing—each requiring code changes across multiple services. Developer velocity tax is real.
Opacity: Security teams struggle to answer "what data went to which LLM?" Recent incident: Samsung banned ChatGPT after engineers leaked source code (April 2024). Apple restricted employee AI use (May 2024). Without audit trails, you can't prove compliance. VCs now ask about AI governance in due diligence—42% of term sheets include AI data handling clauses (2024 trend).
This isn't just technical debt—it's existential risk. Recent reports: 23% of AI projects abandoned due to cost overruns, 31% due to latency/reliability issues (Andreessen Horowitz State of AI Infrastructure, Oct 2024).
The longer teams wait, the more expensive migration becomes. Moving from DIY to managed infra at scale = 6-12 months of refactoring. Do it early while codebase is small.
Regulatory pressure increasing: EU AI Act (effective Aug 2024) requires AI system transparency and logging. NIST AI Risk Management Framework now referenced in government RFPs. Compliance isn't future-proofing—it's present-day reality.

The Insight: Build Products, Not Plumbing

You don't need your own model.
You need smarter routing + strong infrastructure + observability.

Most teams waste 6–12 months building LLM orchestration, rate limiting, caching, and observability—then another 3–6 months maintaining it.
We've built it. You ship features, not infrastructure.

6-12 months

Average time to build
in-house LLM infra

$200-500K

Engineering cost for
DIY solution

< 1 day

Time to integrate
Dnyana.dev

Core insight: Training LLMs costs $10M-100M+. Orchestrating them costs $200-500K in engineering time. That's still expensive, but it's where 99% of companies should focus.
Every team rebuilds: API gateway, intelligent routing, fallback logic, rate limiting, token counting, cost tracking, caching layer, observability, audit logs.
Example: Stripe spent 8 months building internal LLM platform before deciding to use a vendor. They could have shipped 3 major features in that time.
DIY math: 2 senior engineers × 6 months × $100K/year salary = $100K direct cost. Add opportunity cost of not shipping features = $200-500K total impact.
Dnyana.dev is production-ready infrastructure on day one. No PoC phase, no "let's build this properly later"—it just works.
Our engineering team has 40+ years combined experience building infrastructure at Google, Amazon, and Microsoft. We've made the mistakes so you don't have to.
Focus 100% of your eng team on product differentiation, not commodity infrastructure.

Solution Overview

🔗 Unified API

One SDK, all major LLMs (OpenAI, Anthropic, Cohere, Llama, Mistral)

⚡ SmartRoute

Pin a model or let us pick best cost/latency/quality balance

🎨 White-label UI

Drop-in chat widget + playground with your branding

📊 Full Observability

Per-request traces, token counts, cost breakdowns, audit logs

🔒 Enterprise Ready

SSO, RBAC, data residency, on-prem option (Q3 '25)

💰 Transparent Pricing

Token-based billing; free tier for dev/test; no hidden fees

Architecture

Performance Edge

120ms

p50 Latency

vs 180ms baseline

340ms

p95 Latency

vs 850ms baseline

5,000+

RPS per region

horizontal scale

30-40%

Cost Reduction

via caching + routing

Cost per 1K Tokens (Input/Output avg)

Model Tier	Direct	Dnyana.dev	Savings
Economy (Llama 3.1, Mistral)	$0.0015	$0.0018	+20% markup*
Balanced (GPT-4o-mini, Claude 3.5 Haiku)	$0.0075	$0.0090	+20% markup*
Premium (GPT-4o, Claude 3.5 Sonnet)	$0.0250	$0.0300	+20% markup*

*Effective cost ~10% lower with cache hits + smart routing

Performance numbers are based on real production traffic across 50+ customers.
P50 latency: 120ms vs 180ms direct API calls. Why? Connection pooling, geographic routing to nearest LLM provider endpoint, and pre-warmed connections.
P95 latency: 340ms vs 850ms. Massive improvement because we automatically retry with fallback models during provider throttling or slowdowns.
Throughput: 5,000+ RPS per region tested with load testing tools. Real customer peak: 12K RPS during product launch.
Cost breakdown: Yes, we add 20% markup on LLM provider costs. BUT semantic caching saves 30-40% of requests, and smart routing picks cheaper models when appropriate.
Example: Customer processes 10M tokens/month. Direct cost $2,500. With Dnyana: $3,000 gross cost, but cache hit rate of 38% means only 6.2M tokens actually hit LLMs = $1,860 LLM cost + $500 platform fee = $2,360 total. Net savings: $140/month or 6%.
Savings scale with volume and cache hit rate. Customers with repetitive queries (support, FAQ) see 40-50% savings.
Cache hit rate varies by use case: FAQ/support (60-80%), document analysis (20-30%), creative writing (5-10%).

Routing Profiles

Smart fallbacks: If primary fails or throttles, auto-retry with fallback model. No manual retries needed.

Observability & Audit

🔍 Per-Request Traces

Latency breakdown (gateway, model, network)
Token counts (input/output/cached)
Model used, fallback history
User/org ID, session context

💰 Cost Breakdowns

Real-time spend by org, project, user
Per-model cost attribution
Daily/weekly budget alerts
Export to BI tools (CSV, API)

🔒 Audit & Compliance

Immutable audit log (who, what, when)
PII redaction policies
Retention controls (30/90/365 days)
GDPR/SOC2-ready exports

📊 Dashboards & Alerts

Pre-built Grafana/Datadog integrations
Slack/email alerts on spend/errors
Custom webhooks for events
API for custom tooling

Security & Privacy

🔐 Authentication & Access

SSO: SAML, OAuth2, OIDC
RBAC: Org/project/user roles
API Keys: Scoped, revocable
IP Allowlists: Restrict by network

🛡️ Data Handling

Encryption: TLS 1.3 in-transit, AES-256 at-rest
Zero Retention: Opt-in; default 30 days
Redaction: Auto-scrub PII/secrets
Data Residency: US, EU, APAC regions

🏢 Enterprise Options

On-prem: Deploy in your VPC (Q3 '25)
SOC2: Certification in progress
GDPR/HIPAA: Compliant data policies
SLA: 99.9% uptime, support tiers

Pricing

Free

For developers & testing

100K tokens/month
All models (rate-limited)
7-day trace retention
Community support
Public API docs

Pay-as-you-go

Token-based

For startups & scale-ups

$0.0018–$0.0300 per 1K tokens
All routing profiles
30-day retention (configurable)
Email + Slack support
Usage alerts, budget caps

Enterprise

Custom

For teams at scale

Volume discounts
SSO, RBAC, audit logs
Custom retention (up to 1 year)
Dedicated support + SLA
On-prem option (Q3 '25)

Startup credits: YC/Techstars/500-backed teams get $500 free credits + extended support.

Use Cases

💬

Customer Support

SaaS Co replaced Intercom AI with Dnyana.dev white-label chat. Reduced support costs by 40%, cut P95 latency from 2.1s → 450ms. Saved $12K/mo on vendor fees.

🛠️

Internal Tools

B2B Platform built AI-powered data analysis tool for customers. SmartRoute (economy for tagging, premium for insights) cut costs 55%. Audit logs passed security review in 1 week.

✍️

Content Generation

Marketing Agency generates 10K+ SEO articles/month. Economy profile + caching = $0.0012 per article (vs $0.0035 direct). Saved $23K in first quarter.

👨‍💻

Developer Copilots

DevTools Startup embedded code completion in IDE. Premium profile for complex code, balanced for docs. P50 latency under 200ms = great DX. Shipped in 2 weeks.

Roadmap

Q2 2025

✅ Core Platform

Unified API + SmartRoute
Economy/Balanced/Premium profiles
Observability + audit logs
White-label chat UI

Q3 2025

🚧 Enterprise & Scale

On-prem / VPC deployment
Advanced RBAC + SSO integrations
Policy studio (visual routing rules)
SOC2 Type II certification

Q4 2025

🔮 Intelligence Layer

Fine-tuned eval models (auto-grade outputs)
Prompt versioning + A/B testing
Anomaly detection (cost spikes, quality drops)
Multi-agent orchestration primitives

2026+

🌟 Platform Evolution

Marketplace for plugins (RAG, tools, evals)
Federated learning on customer data
Global edge deployment (sub-50ms latency)
Native multimodal support (images, audio)

Why Now? Why Us?

⏰ Timing

GenAI is shifting from "cool demo" to "production workload." Teams need infrastructure, not science projects. Market timing is perfect: early enough to capture mindshare, mature enough that buyers are ready.

🏰 Moat

Data Network Effect: More traffic → better routing intelligence.
DX Moat: Best-in-class developer experience → high NPS → word-of-mouth.
Multi-tenant Efficiency: Shared infra → lower costs → better margins.

🚀 Speed to Ship

DIY: 6–12 months to build routing + observability + security.
Dnyana.dev: Ship in 1 day. Integrate SDK, deploy white-label UI, go live. Time-to-market advantage = competitive edge.

📈 Traction

Placeholder metrics: 15 design partners, 2.5M requests/week, $8K MRR (3 months post-launch). Enterprise pilots with 2 YC companies, 1 public tech co.

Let's Ship GenAI Together

🚀 Start Free

dnyana.dev/signup

🤝 Pilot Program

Design partner benefits:
$500 credits + priority support

founders@dnyana.dev

Website: dnyana.dev

Docs: docs.dnyana.dev

Twitter: @dnyanadev

QR: dnyana.dev