Top AI Consulting Firms in 2026 (And How to Pick One)

Gartner projected in 2023 that more than 80% of enterprises would use generative AI APIs or run a GenAI-enabled application in production by 2026, up from under 5% in 2023. That is why the AI consulting market has split into distinct delivery models, and matching the model to the job matters more than picking a recognizable name. This list ranks by fit, not size.

**Key numbers**
- More than 80% of enterprises projected to use or deploy generative AI in production by 2026, up from under 5% in 2023 (Gartner, October 2023 projection).
- A loaded US senior AI engineer runs roughly $250,000 a year or more once you count salary, benefits, and recruiting.
- Pod-subscription pricing on this list ranges from $5,000 to $10,000 a month, month-to-month.
- Marketplace matching models on this list claim 48 hours (Toptal) to 4 days (Turing) to a matched engineer.

## How we picked this list

Four criteria decide where a firm lands, in this order.

**Delivery model.** Staff augmentation places an individual inside your team and your management chain. Project-based consulting scopes a fixed engagement with a statement of work. Subscription pods hand you a small team, a lead, and a weekly ship cadence for a flat monthly fee. Each model solves a different problem, and none of them is universally correct.

**Pricing transparency.** Can you find the number before a sales call, and does it stay flat once you sign, or does it grow through change orders and hourly overages.

**Code and IP ownership.** Does the work land in your repository and your cloud account from day one, with no license-back, or does the vendor retain some claim on what gets built.

**Compliance posture.** For healthcare, fintech, and public-sector builds, this means a real answer to what "HIPAA-compliant" means (there is no HIPAA certification to hold, only signed BAAs and HIPAA-aligned controls), whether a SOC 2 report exists, and whether the vendor can name a production system built inside that posture.

Score every firm below against those four axes before you read our verdict on it. The order is not a ranking of quality, since a firm built for a five-year enterprise program and a firm built for a six-week production build are not competing for the same job.

## 1. Globant

Globant is a global digital engineering and consulting company with more than 28,700 employees in more than 30 countries, naming enterprise clients including Google, Electronic Arts, and Santander in its own corporate boilerplate ([globant.com](https://www.globant.com/)). It sells project-based and staff-augmentation engagements across AI, cloud, and enterprise modernization, typically scoped through a statement of work with named workstreams and milestones. Since June 2025 it has also sold a subscription tier of its own, "AI Pods," which it describes as agentic AI supervised by Globant specialists on a monthly subscription with token-metered capacity, so a subscription model is no longer only a small-vendor idea.

**Delivery model:** project-based consulting and staff augmentation at enterprise scale, plus a token-metered AI Pods subscription launched in 2025.
**Pricing transparency:** not published; services are scoped per engagement and the AI Pods subscription is metered by token consumption, both quoted directly.
**IP ownership:** governed by the master services agreement negotiated per client; enterprise buyers should confirm ownership terms before signing.
**Compliance posture:** enterprise-grade, but specific to the contract negotiated.

**Where it fits:** a multi-year digital modernization program spanning several departments, where you need a firm with the bench depth to staff a 50-person initiative and the account structure to manage it. **Where it does not fit:** a founder or a Series B team that wants a small, named senior team shipping code into their own repository inside a month. The AI Pods subscription narrows the pricing gap, but it is sold as metered agentic capacity supervised by Globant, which is a different unit of work from a fixed human pod with a named lead, and the account structure around it is built for enterprise buyers.

## 2. Andela

Andela runs a talent-layer model: it sources, vets, and matches AI and application engineers into client teams. Its site advertises 17,000 certified AI-native engineers and names clients including Goldman Sachs, Capital One, Johnson & Johnson, and GitHub ([andela.com](https://www.andela.com/)); its 2023 platform-launch announcement claims a speed to hire "up to 70% faster than traditional recruiting" and a hiring process "30% to 50% more cost efficient." This is staff augmentation at scale, engineer by engineer.

**Delivery model:** individual staff augmentation, sourced through Andela's network.
**Pricing transparency:** rates are negotiated per engineer and per client; no public rate card.
**IP ownership:** the engineer works inside your existing infrastructure and reporting line, so ownership questions are largely governed by your own employment or contractor agreements.
**Compliance posture:** varies by the individual engineer's placement and your own internal controls, since Andela supplies the person, not the compliance program.

**Where it fits:** you need one or two more senior hands inside a team you already manage, and you want to skip the sourcing and vetting cycle. **Where it does not fit:** you do not have a technical lead to manage the placed engineer, or you need a compliance posture (BAAs, SOC 2, audit-grade code review) that ships with the team rather than being assembled around an individual. See our full breakdown of [what staff augmentation actually is](/blog/what-is-staff-augmentation) before comparing it against a managed team model.

## 3. Toptal

Toptal is a marketplace that matches freelance senior developers to client projects, with public positioning around an average time to match of under 24 hours and hiring "in about 48 hours" ([toptal.com/developers](https://www.toptal.com/developers)). There is no fixed public rate card; pricing is negotiated per engineer. It does publish a trial period of up to two weeks that you pay for only if you are satisfied, a real de-risking mechanism; Turing publishes a comparable risk-free trial, and the enterprise firms on this list generally do not.

**Delivery model:** individual freelance staff augmentation, marketplace-matched.
**Pricing transparency:** no published rate card; varies by engineer seniority and specialty.
**IP ownership:** typically assigned per the individual contractor agreement you sign with the matched developer.
**Compliance posture:** not a Toptal-owned program; whatever compliance exists is what you and the individual contractor establish.

**Where it fits:** a short, well-scoped task with a clear spec, where you need one strong individual contributor fast and you already have the technical management in place to direct their work. **Where it does not fit:** a build that needs a team, a shared code-review gate, or a compliance program that outlives any single contractor's engagement. We cover this comparison directly in [Asaasin vs. Toptal: which model fits your project](/blog/asaasin-vs-toptal).

## 4. Turing

Turing positions itself around speed of match for AI engineering roles specifically, with public copy citing "4 days to fill most roles" and a three-week risk-free trial ([turing.com/hire/ai-engineers](https://www.turing.com/hire/ai-engineers)). Like Toptal, it is a matching layer over a marketplace of individual developers, tuned toward AI and ML roles.

**Delivery model:** individual staff augmentation, marketplace-matched, AI-role focused.
**Pricing transparency:** not published as a flat rate card; negotiated per placement.
**IP ownership:** governed by the individual contractor agreement, same pattern as Toptal.
**Compliance posture:** not a platform-owned program; established per client and per engineer.

**Where it fits:** you need a specific AI or ML skill set (a particular model family, a particular inference stack) matched fast, and you have the internal structure to onboard and manage that person immediately. **Where it does not fit:** the same gap as Toptal, staff augmentation without a managed compliance or code-review layer built in, and a real difference between hiring a matched individual and getting a team that ships together from day one.

## 5. Asaasin

We run a different model: a subscription pod, not a placement. A [Builder Pod](/pods) is $5,000 a month, one active build track, a pod lead plus a two-engineer bench, weekly ship plus async updates. A Growth Pod is $10,000 a month, two concurrent build tracks, a pod lead plus a three-engineer bench, weekly ship plus a bi-weekly live strategy call, architecture planning, a hosting discount, and priority support. An Enterprise Organization Pod is custom, three or more parallel build tracks, a dedicated senior lead plus 3 to 8 engineers, executive roadmap reviews, hosting included, and priority SLA. Every tier is month-to-month with a 30-day cancellation notice, no per-hour billing, and full pricing detail is on our [pricing page](/pricing).

**Delivery model:** subscription pod, sized to the project, working inside your own repository and cloud account or VPC from week one, with full ownership of code, data, and IP and no license-back.
**Pricing transparency:** published, exact, month-to-month.
**IP ownership:** yours from day one; if we disappeared tomorrow, nothing in the system is licensed through us or calls an Asaasin-only service.
**Compliance posture:** SOC 2 Type II report available under NDA on request, BAAs signed on request, and two production HIPAA-aligned platforms shipped, a compounding-pharmacy routing and audit-log system and a Medicare/Medicaid medical-billing audit platform. We are explicit that there is no such thing as "HIPAA certified," and we do not claim it; the honest claim is a signed BAA and HIPAA-aligned controls, and our [security page](/security) states exactly what that covers.

**Where it fits:** a founder or a VP Eng who needs a senior team shipping into a real repo inside days, without running a 3-6 month hiring cycle, on a build that runs one to three months for a small project or three to twelve for a medium one. **Where it does not fit:** a single-product company that wants one full-time employee to own a codebase for the next five years (that is a hire, not a pod), or an enterprise modernization program spanning a dozen departments and hundreds of engineers, the scale Globant-sized firms are actually built for. We are not first on this list and we should not be: a pod is the right answer to a specific problem, not every problem. For a deeper look at what a generative AI vendor should be able to show you before you sign, see [how to choose a generative AI development company](/blog/generative-ai-development-company).

## Comparing the five models side by side

| Firm | Delivery model | Pricing | IP ownership |
|---|---|---|---|
| Globant | Project-based consulting, enterprise staff-aug, token-metered AI Pods subscription | Scoped per engagement, not published | Negotiated per MSA |
| Andela | Individual staff augmentation | Negotiated per engineer | Governed by your own contractor agreement |
| Toptal | Marketplace freelance matching | Negotiated per engineer, no rate card | Per individual contractor agreement |
| Turing | Marketplace matching, AI-role focused | Negotiated per placement | Per individual contractor agreement |
| Asaasin | Subscription pod | $5,000-$10,000/mo published, Enterprise custom | Client-owned from day one, no license-back |

## What this list does not cover

This is not a complete market map. Dozens of regional consultancies, boutique AI shops, and internal build-out firms exist that fit a given project better than anything named above, and a "top" list built around five names will always miss real options. That is the point of leading with criteria: delivery model, pricing transparency, IP ownership, and compliance posture apply to any firm you are evaluating, named here or not. Run any vendor through those four questions before a contract, not after.

If you are weighing a pod against building the role internally, [our hire-vs-pod cost breakdown](/blog/ai-engineer-cost-2026-hire-vs-pod) walks through the tradeoff in more detail, including the roughly $250,000-a-year-or-more loaded cost of a US senior engineer once benefits, recruiting, and ramp time are counted, a figure that varies with seniority and region but rarely moves below that floor.

## The short version

Pick the model before you pick the firm: staff augmentation for filling a seat inside a team you manage, project-based consulting for a scoped enterprise program, and a subscription pod for a senior team shipping into your own repository on a flat monthly fee with no hiring cycle. Score any vendor, listed here or not, against delivery model, pricing transparency, IP ownership, and compliance posture before signing. We fit the pod category at $5,000 to $10,000 a month, month-to-month, and we are honest that a five-year single-product hire or a Globant-scale modernization program calls for a different kind of firm entirely.