Top AI Engineer Staffing Companies

Quantiphi vs Index.dev: full comparison for 2026

Quick verdict

Quantiphi (4.3/5) edges ahead of Index.dev (4.2/5) overall. Quantiphi is the better choice for enterprises that need many AI roles filled at once by one AI-only supplier. Index.dev is the stronger option for startups that want a 30-day refundable trial before committing to a remote ML engineer. The right choice depends on your project size, budget, and required tech stack.

Quantiphi vs Index.dev: head-to-head summary

Criterion Quantiphi Index.dev
Founded 2013 2019
HQ Marlborough, Massachusetts, USA London, United Kingdom
Team size 3,000–4,000+ 30,000+ network
Rating 4.3 / 5 4.2 / 5
Primary differentiator The biggest AI-only bench here, sold through a named staffing program with AWS A 30-day trial with full refund on every placement
Pricing model Elastic Staffing billed per specialist; consulting quoted separately; rates on request Monthly rate per engineer; direct-hire fee option; 30-day trial with refund; rates on request
Min. engagement Not published Not published
Primary tech stack Python, TensorFlow, PyTorch Python, PyTorch, TensorFlow
Industries served Healthcare, Financial services, Energy, Retail, Media SaaS, Fintech, E-commerce, Healthcare, AI labs

Quantiphi vs Index.dev: overview

Quantiphi

Quantiphi, based in Marlborough, Massachusetts and founded in 2013, is the largest company on this page that works only on AI and data, with directory estimates between 3,000 and more than 4,000 people. Its Elastic Staffing program, built with AWS, places generative AI and ML specialists into client teams. That scale is the reason it ranks here: no other AI-only supplier can staff ML, MLOps, data and LLM roles in parallel. Google Cloud named it 2025 AI Partner of the Year for North America. The cost is attention, since staffing is one product inside a large consulting business.

Index.dev

Index.dev is a London company, founded in 2019 according to its own job ads, trading under the legal name Index Soft Limited. It calls itself an AI-first engineering talent platform. Its main network has more than 30,000 engineers in Latin America and Central and Eastern Europe, and a separate AI unit supplies Master's and PhD-level people for LLM fine-tuning and RAG work. Every engineer goes through five stages of vetting run by people, including a live screening call and technical validation, and Index.dev says about 1% are accepted. All placements come with a 30-day trial and a full refund if the match fails.

Services and capabilities: Quantiphi vs Index.dev

Capability Quantiphi Index.dev
ML engineers ✓ ✓
LLM / GenAI engineers ✓ ✓
AI agent developers ✗ ✗
MLOps engineers ✓ ✗
Computer vision engineers ✓ ✗
NLP engineers ✗ ✗
Data engineers ✓ ✗
Engineer-led technical screen ✗ ✗
Fractional / part-time experts ✗ ✗
Trial before commitment ✗ ✓
Nearshore time-zone overlap ✗ ✓
Direct hire option ✗ ✓

Tech stack comparison: Quantiphi vs Index.dev

Framework / platform Quantiphi Index.dev
PyTorch ✓ ✓
TensorFlow ✓ ✓
LangChain N/A ✓
Hugging Face N/A ✓
OpenAI N/A ✓
AWS ✓ ✓
Azure N/A N/A
Google Cloud ✓ ✓
Databricks ✓ N/A
Kubernetes ✓ N/A

Pricing comparison: Quantiphi vs Index.dev

Criterion Quantiphi Index.dev
Minimum engagement Not published Not published
Engagement models Dedicated engineer, Dedicated team, Project delivery Dedicated engineer, Dedicated team, Direct hire, Trial period
Rate transparency Not public Not public
Price tier Mid-market Mid-market

Target audience comparison: Quantiphi vs Index.dev

Dimension Quantiphi Index.dev
Best company size Startup to mid-market Startup to mid-market
Best industries Healthcare, Financial services, Energy SaaS, Fintech, E-commerce
Best use cases Staffing eight GenAI specialists into an enterprise program, Adding Vertex AI or SageMaker engineers for a cloud ML migration Trialling a Latin American ML engineer for a month before committing, Hiring a PhD-level specialist to fine-tune an open-source LLM
Typical project type Dedicated engineer Dedicated engineer

Quantiphi vs Index.dev: pros and cons

Quantiphi
+ Can staff several AI specialties in parallel, which no other AI-only firm here can
+ Top partner tiers with Google Cloud and AWS help on cloud-specific ML roles
+ A named staffing product makes procurement simpler
- Requests for one or two engineers compete with large consulting programs
- Rates appear only after scoping
- Headcount estimates vary widely between sources
Index.dev
+ The 30-day refundable trial is the most generous published trial on this page
+ A separate AI unit for advanced work such as fine-tuning
+ Clients can hire directly as well as contract
- Vetting is human-led but not specifically run by ML engineers
- Network figures and acceptance rates are company claims
- Founding date only appears in job listings

Who should choose Quantiphi?

A typical fit: staffing eight GenAI specialists into an enterprise program.

The biggest AI-only bench here, sold through a named staffing program with AWS. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Financial services, Energy, Retail, Media.

Who should choose Index.dev?

A typical fit: trialling a Latin American ML engineer for a month before committing.

A 30-day trial with full refund on every placement. Minimum engagement is not publicly disclosed. Works best with clients in SaaS, Fintech, E-commerce, Healthcare, AI labs.

Decision matrix: Quantiphi vs Index.dev

Your situation Recommended choice
You want a working engineer, not a recruiter, to run the technical screen Neither documents an engineer-led screen; run your own technical interview
You need one specialist for a few days a week Neither advertises part-time experts; ask about reduced hours
You need several engineers working as one team Quantiphi
You want to test an engineer before committing Index.dev
Your budget is at the lower end Compare: Quantiphi (Not published) vs Index.dev (Not published)
Your team works U.S. hours Index.dev
You may want to hire the engineer permanently later Index.dev

Use case fit: Quantiphi vs Index.dev

Use case Quantiphi fit Index.dev fit Winner
Staffing eight GenAI specialists into an enterprise program Strong Limited Quantiphi
Adding Vertex AI or SageMaker engineers for a cloud ML migration Strong Limited Quantiphi
Trialling a Latin American ML engineer for a month before committing Limited Strong Index.dev
Hiring a PhD-level specialist to fine-tune an open-source LLM Limited Strong Index.dev

Verdict: Quantiphi vs Index.dev

Quantiphi (4.3/5) is the stronger overall choice for most AI Engineer Staffing projects. The biggest AI-only bench here, sold through a named staffing program with AWS.

Index.dev (4.2/5) is worth a look if you need hiring a PhD-level specialist to fine-tune an open-source LLM. If your situation matches that, Index.dev is a competitive option.

Related comparisons

Quantiphi vs Index.dev FAQ

Is Quantiphi better than Index.dev?

Quantiphi (4.3/5) scores higher overall, but "better" depends on your use case. Quantiphi's strongest advantage: can staff several AI specialties in parallel, which no other AI-only firm here can. Index.dev's strongest advantage: the 30-day refundable trial is the most generous published trial on this page.

How do Quantiphi and Index.dev differ in pricing?

Quantiphi uses elastic staffing billed per specialist; consulting quoted separately; rates on request pricing. Index.dev uses monthly rate per engineer; direct-hire fee option; 30-day trial with refund; rates on request pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.

Which is better for enterprise: Quantiphi or Index.dev?

Quantiphi is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.

What are the main differences between Quantiphi and Index.dev?

Quantiphi's primary differentiator is: the biggest AI-only bench here, sold through a named staffing program with AWS. Index.dev's primary differentiator is: a 30-day trial with full refund on every placement. They also differ in team size (3,000–4,000+ vs 30,000+ network), minimum engagement (Not published vs Not published), and primary industries served (Healthcare, Financial services vs SaaS, Fintech).

Verify all details directly with each company before making a decision.