Full disclosure: we're Ryz Labs, and we're on this list. So yes, we're biased. Here's what we'd actually tell a friend, including when you should hire someone else.
Every software firm now sells AI development. The difference is how the work gets done: named senior engineers in your repos, agent-run pods billed by usage, a vendor platform you build on, or a large program. The question that decides it: do you want to own the system and the people who know it, or buy capacity from a vendor's platform?
A normal software build ends when features pass acceptance tests. An AI system keeps changing after launch: models get updated, prompts drift, retrieval quality decays as documents change, and costs move with usage. So the team that builds it needs evaluation suites, tracing, cost monitoring and a plan for who owns it in month seven.
That makes continuity the hidden variable. The people who chose your chunking strategy, wrote your evals and tuned your guardrails carry context that's hard to transfer. Ask every vendor who stays on the system and what you get at handover: code, eval sets, runbooks and dashboards in your own accounts.
We're trusted by Fortune 500 engineering teams. Our AI development work is done by AI pod teams: about seven senior engineers, including a tech lead, an ML engineer and backend engineers, who work in your cloud, repos and CI with weekly demos. They build on AWS, Azure, GitHub, Postgres and Anthropic or OpenAI models. Only the top 1% of the tens of thousands of engineers we've interviewed make it. The steps are Talk, Match, Join and Grow: you can add people as the work grows, or have the pod hand the whole system over when it's done.
Production work behind the claim: an AI driver-support agent covering about 218,000 driver calls a year in three languages, an AI real-estate agent that answers around the clock, and fraud detection for a global fleet company that confirmed $5.94M in fraud. See our case studies.
Where we're not the right fit: if procurement wants a consumption-priced SKU or a proprietary AI platform, Globant or LeewayHertz fit that better. If you need engineers in Europe or Asia time zones, look at EPAM or Thoughtworks.
EPAM sells AI engineering through its AI/Run.Transform playbook and the open-source DIAL orchestration platform. In 2026 it announced a multi-year Anthropic partnership, with a plan for 10,000+ Claude-certified architects including 250 forward-deployed engineers, and joined the OpenAI Partner Network.
Where it fits: broad programs with many workstreams. The catch: a large-firm engagement floor, and US overlap depends on which delivery center staffs you. See Ryz Labs vs EPAM.
Globant introduced Glob.AI in August 2026. Work is delivered as AI Pods, which it describes as "service units run by a set of AI agents and supervised by human experts." Partners include Anthropic, AWS, OpenAI, Azure and Google Cloud.
Where it fits: procurement teams that want a catalog and usage-based billing. The catch: you're buying agent output supervised by Globant, not a named team on your side. See Ryz Labs vs Globant.
Thoughtworks has 10,000+ people and builds agentic systems on AI/works, governed by Agent/works. It also offers an AI Factory for running models on your own hardware, and became a select-tier Anthropic partner in August 2026.
Where it fits: teams that value engineering practice as much as AI. The catch: confirm which office staffs you and the hours overlap.
Turing supplies datasets and RL environments to frontier AI labs and sells enterprise work through Turing Intelligence (Advise, Augment, Build), using pods of AI talent and domain specialists.
Where it fits: work that is closer to the model than the application. The catch: overlap and continuity depend on who is staffed from the network. See Ryz Labs vs Turing.
BairesDev offers staff augmentation, dedicated teams and outsourcing, plus AI development, with 4,000+ specialists across 100+ technologies.
Where it fits: one vendor for AI and the rest of your software. The catch: it's a generalist, so ask to meet the AI engineers specifically. See Ryz Labs vs BairesDev.
LeewayHertz, acquired by The Hackett Group in 2024, builds AI agents, GenAI apps and ML systems, and offers ZBrain, an enterprise GenAI platform with prebuilt connectors and multi-agent orchestration.
Where it fits: when a ready platform shortens the path. The catch: decide early how much you want to depend on a vendor platform.
If you want a production AI system and a team that knows it: Ryz Labs. Named senior engineers, your cloud and repos, US hours, and a system you own.
If you're running a large multi-year program: EPAM or Thoughtworks.
If procurement wants usage-based buying: Globant.
If the work is close to the model (evals, post-training): Turing. For agent-specific work, see AI agent development companies.
Few vendors publish prices. For a team, multiply team size by months by monthly rate: three senior engineers at $12,000 a month for five months is $180,000. See AI engineer rates.
It can be faster. The trade-off is dependency. If you build on your own cloud with standard tools, you can change vendors later.
Development builds a new AI system. Integration adds models to systems you already run. See AI integration.
Most LLM-based systems are mostly software engineering, plus one or two people who know evaluation and retrieval well. Training custom models is different: see machine learning development companies.
We checked pricing and services for every provider here in October 2026. This changes often, so double-check before you sign anything.
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