Compare · AI development companies
AISD vs. LeewayHertz
LeewayHertz is a well-established AI development company with 200+ engineers and enterprise-scale delivery. AISD is an AI-native shop built for speed, transparency, and senior-only teams. Different buyers, different strengths. Here is an honest comparison.
Updated · 2026-05-02 · 5 min read
Side-by-side
The comparison
| Factor | AISD | LeewayHertz |
|---|---|---|
| Team size | Senior-only. Every engineer ships code from day 1. No bench, no juniors backfilling | 200+ employees. Mixed seniority. Account managers layer between you and the engineer |
| Speed | Working agent in 14 days, MVP in 6 weeks. Hard timelines in every proposal | Enterprise timelines. Typical engagements 3-6 months before production |
| Pricing transparency | Public pricing. Discovery sprints from $8K. MVPs from $50K. Staff aug from $75/hr | Hidden pricing. Contact sales for a quote. Typical projects $100K+ |
| AI-native depth | Born AI-native. Every service is AI-first: agents, RAG, fine-tuning, eval harnesses | Full-service IT. AI is one of 20+ service lines alongside blockchain, IoT, metaverse |
| Engagement flexibility | Fixed-price projects, discovery sprints, fractional teams. No long-term contracts required | Enterprise contracts. Minimum engagement typically 3+ months |
| Process | The AISD Build Loop: scope tight, pattern-pick, build fast, secure by default, hand off with runbook | Traditional SDLC with discovery, design, build, test, deploy phases |
| Case studies | Numbers + architecture diagrams. 70% time reduction, $340K saved, 47% docs time cut | Logo-driven. Client names and industry verticals, lighter on quantified outcomes |
| Best for | Series A-C SaaS, mid-market, ops-heavy SMBs. Teams that need to ship fast with senior talent | Enterprise with large budgets, multi-year digital transformation programs |
The honest take
When LeewayHertz is the better choice
LeewayHertz has more engineers, more case studies, and more enterprise logos than AISD. If you are running a multi-year digital transformation that spans blockchain, IoT, and AI simultaneously, their breadth is an advantage.
They also have a deeper bench for industries where regulatory compliance requires on-site teams or government clearances. AISD operates remote-first with distributed senior engineers.
When AISD is the better choice
If you need to ship a production AI agent in weeks rather than quarters, AISD's model is purpose-built for that. Every engineer is senior, every engagement has a hard timeline, and pricing is public before the first call.
AISD is also a better fit if your problem is specifically AI-native: agent orchestration, RAG pipelines, LLM eval, workflow automation. We don't dilute focus across 20 technology categories.
Pricing detail
Concrete pricing bands, not "contact us for a quote."
AISD publishes engagement prices. LeewayHertz does not. Here's what each engagement type typically costs based on public market data and observed engagements:
| Engagement type | AISD (public) | LeewayHertz (typical) |
|---|---|---|
| AI agent / MVP (6-8 wk fixed) | $40k–$120k | $80k–$250k+ |
| Staff augmentation (per engineer, monthly) | $14k–$22k | $16k–$28k |
| Discovery sprint (2 weeks) | $12k–$20k | $25k–$50k |
| Multi-quarter modernization | $200k–$500k | $500k–$2M+ |
Numbers reflect 2026 US market rates for senior AI engineering. See our pricing page for the live AISD bands and engagement-model detail.
Decision matrix by use case
Which one to pick, by what you're building.
| If you need to build | Better fit | Why |
|---|---|---|
| Production AI agent in 6-8 weeks | AISD | Fixed-scope agent model, senior-only team, public timelines |
| Multi-year enterprise digital transformation | LeewayHertz | More engineers, broader tech surface (blockchain + IoT + AI) |
| AI MVP for a Series A-C startup | AISD | Sub-$120k fixed-price ceiling, 4-8 week ship |
| RAG pipeline + LLM eval harness | AISD | AI-native specialty, eval discipline as default practice |
| On-prem deployment with on-site team | LeewayHertz | Larger on-site capacity, more government-cleared engineers |
| Embed AI into existing SaaS / mid-market product | AISD | AI Modernization focus, faster integration cycles |
| Cross-disciplinary build (AI + blockchain + IoT) | LeewayHertz | Broader practice surface |
Common mistakes
Four mistakes buyers make choosing between firms like these.
- 01
Optimizing for headcount over delivery model.
"They have 700 engineers" feels safer than "they have 50." But the AI specialist count and the delivery cadence move outcomes — not the bench size.
- 02
Accepting opaque pricing because the brand is bigger.
"Contact us for a quote" usually means a multi-stage sales process where the price gets set by what the firm thinks you can afford. Public bands force commitment.
- 03
Confusing "AI capability" with "AI specialty."
Most agencies now ship AI features. Few do AI-native engineering as their primary practice. Eval rigor, prompt-injection defense, and agent observability are the diagnostic markers.
- 04
Skipping the discovery sprint to save money.
$15-50k of paid discovery usually saves $100k+ of mid-engagement scope changes. Both firms offer it. Don't optimize away the cheapest insurance you can buy on the project.