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.
Getting a generative AI demo working takes a week. Getting it right 95 times out of 100 on your own documents, at a cost you can defend, takes real engineering. The question that decides it: are you building on a vendor's GenAI platform, or on your own cloud with models you can swap?
The model is the easy part. Most of the work in a GenAI system is retrieval (chunking, embeddings, re-ranking, keeping the index fresh), evaluation (test sets built from real questions, graded automatically and by people), and cost control (caching, routing cheap requests to cheaper models). A vendor that can't show you its evaluation approach is selling demos.
The other difference is change. Model versions change every few months, and your prompts and evals have to move with them. If the system sits on a vendor platform, those upgrades happen on the vendor's schedule. If it sits on your cloud, they happen on yours. Neither is wrong, but decide which you want. Our RAG vs fine-tuning and Bedrock vs Azure OpenAI guides help with the early choices.
We're trusted by Fortune 500 engineering teams. Our generative AI development is done by AI pod teams of senior engineers working in your cloud, repos and CI, on AWS, Azure, GitHub, Postgres and Anthropic or OpenAI models. A typical pod is about seven people, including a tech lead, an ML engineer and backend engineers, with weekly demos. Only the top 1% of the tens of thousands of engineers we've interviewed make it.
One example: a marketing-compliance system for a global capital management firm that reviews 8,000+ documents and cut review time from days to hours. See our case studies. Need a specialist instead of a pod? See RAG engineers or RAG development.
Where we're not the right fit: if you want a proprietary GenAI platform with prebuilt connectors, IBM or Quantiphi bring one. If you need a management-consulting program or Europe and Asia time zones, look at Accenture or EPAM.
Accenture's generative AI practice covers enterprise-wide scaling and LLM refinement, alongside data services, responsible AI and its AI Refinery platform. Its partner list includes Anthropic, AWS, Google Cloud, Microsoft and OpenAI.
Where it fits: many use cases, many functions, one partner. The catch: one well-scoped system may not need a program of this size.
IBM Consulting delivers GenAI through IBM Consulting Advantage and watsonx, with governance and a catalog of prebuilt agents (Enterprise Advantage).
Where it fits: IBM-heavy estates that want platform and governance from one vendor. The catch: check how portable the result is if you change models later.
EPAM maintains DIAL, an open-source GenAI orchestration platform, and in 2026 announced partnerships with Anthropic and OpenAI. See Ryz Labs vs EPAM.
Where it fits: multi-workstream programs. The catch: a large-firm engagement floor.
Thoughtworks offers generative AI and ML delivery, agentic builds on AI/works, and an AI Factory for hosting models on your own hardware to avoid "spiraling API token bills."
Where it fits: high-volume workloads where self-hosting pays off. The catch: confirm which office staffs you. See Ryz Labs vs Thoughtworks.
Quantiphi builds generative, conversational and document AI, plus data and cloud work, with alliances including Google Cloud, AWS, Azure and NVIDIA. It also offers accelerators such as baioniq and Dociphi.
Where it fits: buyers who want a cloud partner's specialist. The catch: decide how much accelerator code you want in your system.
Tribe AI maps, builds and drives adoption with forward-deployed engineers, and says it is SOC 2 Type II certified. See Ryz Labs vs Tribe AI.
Where it fits: a scoped use case where adoption is the risk. The catch: ask who maintains the system after launch.
If you want a GenAI system in your own cloud, with models you can swap: Ryz Labs.
If you're scaling GenAI across a global enterprise: Accenture.
If you're committed to IBM or a specific cloud partner: IBM Consulting or Quantiphi.
If token costs are your biggest worry: Thoughtworks' self-hosting approach is worth a look. For agents that take actions, see AI agent development companies.
Usually retrieval systems over your documents, copilots for internal teams, content and document processing, and agents. The engineering is mostly retrieval, evaluation, integration and cost control.
Few vendors publish prices. For a team, it's size times months times monthly rate: four engineers at $11,000 a month for three months is $132,000, plus model usage. See LLM engineer rates.
Build so you can switch. Many teams use more than one. See OpenAI vs Anthropic for enterprise.
Usually not at first. Good retrieval and prompts solve most problems. Fine-tuning helps with format, tone or narrow tasks at high volume.
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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