Retrieval, fine-tunes, multi-model serving, real quality checks. We've shipped production Bedrock workloads (not just demos), wire up Claude on Bedrock, and unlock the GenAI credit track most founders don't hear about. Stop paying OpenAI direct out of runway. $0 to you.
Three patterns from the AI-team inquiries we route. The shape repeats: the demo was easy, the production cliff is hard, and the credit money would have helped if anyone had told them.
You're paying retail per token. Billing changes hit without warning. There's no second model wired in. When the demo went viral your bill 5x'd, and the board asked why you're not on Bedrock yet.
It's storage + a wish + a Notion doc. Production AI needs real pipelines, real quality checks, real ops. We've shipped data + AI before, not learned it on your build.
You like Claude for reasoning, Llama for cheap classification, Mistral for European data residency. You want them under one account, one bill, one network, with a switching cost of "change a string." Bedrock does this; we set it up.
Most founders see one credit number on the public page. Bedrock + Claude builds have their own track — separate ceiling, stacks on top of base credits. We've seen it tip $150K total for a single AI-native startup.
Inference is the line item that scares CFOs. $100K of credits gives a typical Series-A AI team 12–18 months of runway on Bedrock, depending on traffic. Long enough to find product-market fit before the bill matters.
Direct API is faster to start, but you eventually want one account, one bill, one network, fallback models, and credits. Bedrock gives you all five. Claude is on Bedrock — same model, AWS plumbing.
Tell us what you're building. We figure out which credit programs you qualify for and submit the applications. No technical knowledge needed.
Series-A AI legal-tech, 8 engineers, $2K/mo OpenAI bill, 6 weeks to investor demo
OpenAI direct burning runway. No fallback model. No quality pipeline. Series-B story needed "we're on AWS, multi-model" by demo day. Internal team had never used Bedrock.
Multi-model setup (Claude + Llama 3) on Bedrock in 3 weeks. Quality checks + prompt regression catches by week 5. $100K in credits secured to absorb the next 12 months of inference. OpenAI direct: turned off.
We use this to route you to the right partner — and to flag credit eligibility before the discovery call. Form fields are kept; you're not in a CRM the moment you start typing.
Direct is faster to start, but Bedrock gets you (a) one bill, one account, one network, (b) easy fallback between Claude, Llama, Mistral, Amazon Nova, (c) credit-eligible spend, (d) prompt caching baked in. Most production teams end up multi-model on Bedrock; we set up the abstraction so you can swap.
It's a separate AWS credit pool from base credits. We file an application based on your AI workload — model choice, projected inference, retrieval / fine-tune scope. AWS approves a credit pool sized to the workload. Inference, storage, and supporting services burn against it. Stacks on top of any base credits you have.
No. The credits hook is most useful for funded startups, but bootstrapped teams with revenue work too. The form asks funding stage so we know which credit track applies — not as a filter.
Different specialty — fine-tuning, custom training, model registry, batch inference. Tell us in the form notes and we handle it accordingly. We do both; most engagements pick one as the primary.
Bedrock has Claude available in most regions on day 1 — no provisioning. Wiring it into your stack with proper access control, prompt caching, and quality coverage is typically a 2–3 week engagement. Demo-quality is faster; production-quality includes the quality checks and fallback work.
Bedrock supports EU regions (Frankfurt, Ireland) for Claude. We set up region pinning, encryption with your own keys, and the legal artifacts (Bedrock DPA covers most cases). Tell us in the form if you need EU-only — we have the residency experience.