Amazon Q is two products with two separate price lists. Q Developer is Free vs Pro at about $19 per user per month. Q Business is Lite at about $3 and Pro at about $20 per user per month, plus a separate index/storage charge that most teams forget to model. This page lays out exactly what each tier includes, where usage charges appear on top of the seat, a worked per-team cost example for both products, and how Q's price compares to GitHub Copilot, ChatGPT Enterprise, and Microsoft 365 Copilot. Figures are representative as of 2026 — confirm current rates on the AWS pricing page.
The single most common pricing mistake is treating "Amazon Q" as one SKU. It is two products — Q Developer and Q Business — with separate tiers, separate billing mechanics, and separate things you can overspend on. Get the product right first; the numbers follow.
Amazon Q is AWS's brand for generative-AI assistants, and it covers two products that share almost nothing except the name and the security posture. Amazon Q Developer is an AI coding assistant for engineers — it lives in the IDE, the CLI, and the AWS console. Amazon Q Business is an enterprise assistant that answers employee questions over your own company data via retrieval-augmented generation (RAG). Different buyers, different surfaces, and — the reason you are on this page — different pricing. If you are evaluating budget, the first question is always "Developer or Business?" because the price lists do not overlap.
Both products are per-user, per-month subscriptions billed through your AWS account, and both have a low-cost or free entry point so you can pilot before committing seats. Q Developer offers a genuinely free tier for individuals; Q Business starts at a few dollars per user. Beyond the seat, each product has a different "second meter": Q Business charges separately for the index that stores your data, while Q Developer can add usage charges for heavy agentic actions on top of the Pro seat. Those second meters are where real bills diverge from back-of-envelope estimates.
A note on accuracy that applies to this entire page: the figures below are representative as of 2026. AWS adjusts tiers, limits, and per-unit rates periodically, and exact prices vary by Region. Use these numbers to build a model and understand the shape of the bill — then confirm the live rates on the AWS Amazon Q Developer and Amazon Q Business pricing pages before you commit a budget. We flag every place where a number is approximate.
One more clarification that saves confusion: the "Q" capabilities embedded inside other AWS services — Amazon Q in QuickSight, Amazon Q in Connect — are generally bundled into those services' own pricing rather than billed as a standalone Q seat (with the exception that Amazon Q in QuickSight is unlocked by the Q Business Pro tier). This page focuses on the two licensed products you actually buy seats for.
Q Developer: Free / Pro (~$19 per user/mo) + possible usage on heavy agent actions. Q Business: Lite (~$3 per user/mo) / Pro (~$20 per user/mo) + a separate index/storage charge. Same brand, two completely separate bills.
Q Developer uses a clean two-tier model: a Free tier for individual developers and a Pro tier at about $19 per user per month for teams. The complexity is not in the tiers — it is in the per-month limits and the usage that can sit on top of a Pro seat.
The Free tier is aimed at individual developers and is genuinely usable for real work, not a crippled trial. You sign in with a free AWS Builder ID — no AWS account required — and get inline code completion across many languages, the chat assistant, and a capped monthly allowance of the higher-cost capabilities: a limited number of agent interactions (the /dev feature-builder and /transform code upgrades), a limited number of security scans, and a cap on AWS-account chat usage. For a solo developer or someone trialing Q, the Free tier often suffices. The constraint is throughput, not capability — hit the monthly caps and you either wait for the reset or move to Pro.
The Pro tier is about $19 per user per month and is the team plan. It raises or removes the Free-tier caps, but its real value is governance and scale: organization-wide license management through IAM Identity Center, centralized policy controls (which features are enabled, IP-based access, reference tracking for code suggestions), higher limits on agent runs and security scanning, and access to the more capable agentic workflows at production volume. If you are rolling Q Developer out to a team, Pro is effectively mandatory because the Free tier is per-individual and has no central administration.
Here is the line item teams miss: certain heavy agentic actions can carry usage-based charges beyond the Pro seat. The clearest example is large-scale code transformation — running /transform to migrate a big codebase across language or framework versions (for instance a Java 8 → 17 upgrade across thousands of files). Each Pro seat includes a monthly allowance of transformation throughput (measured in lines of code processed); exceed that allowance and the overage bills per unit. For most teams writing day-to-day code, the seat fee is the whole bill. For a team doing a one-off mass migration, model the transformation usage separately — it can briefly dwarf the seat cost during the migration month, then drop back to zero.
| Capability | Free tier ($0) | Pro tier (~$19/user/mo) |
|---|---|---|
| Inline code completion (15+ languages) | Yes | Yes |
| Chat (in IDE / CLI / console) | Yes | Yes |
| Agentic feature dev (/dev) & code transform (/transform) | Limited monthly allowance | Higher limits + production volume |
| Security / code-quality scans | Capped per month | Higher monthly limits |
| Org license management (IAM Identity Center) | No | Yes |
| Policy controls, reference tracking, admin | No | Yes |
| Heavy transformation overage | N/A (capped) | Usage-based above included allowance |
| Best for | Individuals, trials, light use | Teams, governed rollout, scale |
Q Business has two seat tiers — Lite (~$3) and Pro (~$20) per user per month — plus a separate, usage-based charge for the index that stores and serves your data. The index is the single biggest source of "the bill was higher than we modeled," so it gets its own treatment here.
Q Business Lite is about $3 per user per month and covers the core conversational experience: ask natural-language questions, get cited answers grounded in your connected data, basic chat and summarization. It is designed for large populations of light, mostly read-only users — frontline staff, new hires, anyone who occasionally looks something up rather than living in the assistant.
Q Business Pro is about $20 per user per month and is the full feature set: everything in Lite plus plugins and actions (create a Jira ticket, update a Salesforce record, post to Teams — from chat), custom plugins against your own APIs, the Q Apps no-code builder, and access to Amazon Q in QuickSight for natural-language analytics over your BI data. Pro is for power users — analysts, support leads who take actions, sales engineers, app builders.
A genuinely useful cost lever: you can mix tiers inside one application. Put the broad population on Lite and the smaller set of power users on Pro, and the blended per-seat cost drops well below a flat $20. Most well-run Q Business deployments are mostly-Lite with a Pro minority.
Separate from seats, Q Business charges for the index — the managed store that holds your ingested documents (text, embeddings, metadata, and ACLs) and serves retrieval at query time. The index is provisioned in capacity units. As a representative 2026 shape: each index unit costs on the order of $0.14 per hour (roughly $100 per unit per month if run continuously) and holds on the order of 20,000 documents (and a bounded amount of query throughput). You add units as your document corpus grows.
The practical implication: index cost scales with document volume and uptime, not with seat count. A team of 50 users indexing 15,000 documents pays for one index unit (~$100/mo) regardless of how many of those 50 are on Pro vs Lite. An enterprise indexing two million documents needs ~100 index units (~$10,000/mo) and that line can rival or exceed the seat bill. This is exactly why teams that model "seats only" get surprised — for large corpora the index is a first-class cost, not a rounding error.
There is no separate per-query LLM token bill the way there is when you call Amazon Bedrock directly — the Q Business subscription bundles the model inference. What bills on top of seats + index is ordinary AWS plumbing: connector data transfer, the S3 buckets where you keep source documents, and any KMS/networking you add. Size the index to real document volume rather than over-provisioning units on day one; you can scale units up as ingestion grows.
| Line item | Lite | Pro | Index (storage) |
|---|---|---|---|
| Price | ~$3 / user / mo | ~$20 / user / mo | ~$0.14 / unit / hr (~$100 / unit / mo) |
| Conversational Q&A + cited answers | Yes | Yes | — |
| Document summarization | Yes | Yes | — |
| Plugins & actions (Jira, ServiceNow, etc.) | No | Yes | — |
| Custom plugins (your APIs) + Q Apps | No | Yes | — |
| Amazon Q in QuickSight (NL analytics) | No | Yes | — |
| Scales with | Seat count | Seat count | Document volume + uptime (~20K docs/unit) |
Pricing decisions are really feature decisions. Below is the honest "what unlocks at each step up" so you assign the cheapest tier that still does the job — and know the specific moment a user needs an upgrade.
The recurring rollout error is binary: either everyone goes on the top tier (overspend) or everyone goes on the bottom tier (power users hit a wall and adoption stalls). The fix is to map each role to the cheapest tier that covers its job, and to know the exact capability that forces an upgrade.
Assign the cheapest tier that still does the user's job, then scale the index to document volume independently of seats. A mostly-Lite Q Business deployment with a Pro minority, sized index, is almost always the right-cost shape — not a flat top-tier rollout.
Abstract per-seat numbers are hard to budget against. Here are two fully worked monthly examples — one for Q Developer, one for Q Business — with every line item, using the representative 2026 rates from above.
Example A — Q Developer for a 40-engineer team. Forty engineers, all on the Pro tier for central management and production limits. Seat cost: 40 × ~$19 = ~$760/month. Steady-state coding (completions, chat, normal agent use) is covered by the seat — no overage. In a quarter where they run a one-off Java 8 → 17 migration across a large monolith with /transform, that migration month exceeds the included transformation allowance and adds, say, a few hundred dollars of usage on top for that month only. Steady-state run-rate: ~$760/month; migration-month spike: ~$760 + transformation overage, then back to ~$760.
Example B — Q Business for a 500-person company. A mixed population: 420 light users on Lite and 80 power users (support leads, analysts, sales engineers) on Pro. Seat cost: (420 × ~$3) + (80 × ~$20) = $1,260 + $1,600 = ~$2,860/month for seats. Their corpus is ~600,000 documents across SharePoint, Confluence, Salesforce, Slack, and S3, needing ~30 index units: 30 × ~$100 = ~$3,000/month for the index. Blended total: ~$2,860 + ~$3,000 = ~$5,860/month. Note how the index (~$3,000) is comparable to the entire seat bill (~$2,860) — exactly the dynamic teams under-model when they quote "seats only."
Two lessons fall out of Example B. First, tier mix matters enormously: had all 500 users been put on Pro, seats alone would be 500 × ~$20 = $10,000/month instead of ~$2,860 — the mostly-Lite split saves ~$7,000/month. Second, the index is a real budget line at enterprise document scale; size it to actual document count and grow units as ingestion grows, rather than guessing high on day one.
| Scenario | Seats | Seat cost / mo | Index / overage | Total / mo |
|---|---|---|---|---|
| Q Developer — 40 eng, all Pro (steady state) | 40 × ~$19 | ~$760 | none | ~$760 |
| Q Developer — same team, big migration month | 40 × ~$19 | ~$760 | + transform overage | ~$760 + overage |
| Q Business — 500 users all on Pro | 500 × ~$20 | ~$10,000 | + index | ~$10,000 + index |
| Q Business — 420 Lite + 80 Pro (mixed) | (420 × ~$3) + (80 × ~$20) | ~$2,860 | ~30 units ≈ ~$3,000 | ~$5,860 |
Because Q is two products, it competes on price in two different markets. Here is the head-to-head on dollars — with the caveat that price is rarely the only axis, and the cheaper sticker can hide a second meter (like Q Business's index or Copilot's GitHub dependency).
Coding — Q Developer (~$19) vs GitHub Copilot. GitHub Copilot Business is around $19 per user per month and Copilot Enterprise around $39 per user per month; there is also a lower-cost individual Pro plan (~$10/mo). So Q Developer Pro lands essentially level with Copilot Business and well below Copilot Enterprise. On pure seat price they are close at the Business tier — the differentiators are elsewhere (Q's AWS awareness and managed code transformations vs Copilot's GitHub-native ecosystem and broad model choice). Watch the second meter: Q Developer can add transformation usage during big migrations, while Copilot's value is tied to your codebase living in GitHub.
Enterprise knowledge — Q Business (~$3 / ~$20) vs Microsoft 365 Copilot and ChatGPT Enterprise. Microsoft 365 Copilot is about $30 per user per month, on top of an existing Microsoft 365 license — so the true cost is $30 plus the underlying M365 seat. ChatGPT Enterprise is custom-quoted and typically lands in the $40–$60+ per user per month range with seat minimums. Q Business Pro (~$20) undercuts both on the seat, and Q Business Lite (~$3) is dramatically cheaper for light users — but remember Q Business adds the index line, whereas M365 Copilot folds its grounding into the per-seat price (and assumes you already pay for M365).
The honest read on price. For a broad population of light knowledge users, Q Business Lite at ~$3 is the cheapest credible enterprise-assistant seat in this set by a wide margin. For power users, Q Business Pro (~$20) is still below M365 Copilot (~$30) and ChatGPT Enterprise ($40–$60+). On coding, Q Developer is at parity with Copilot Business and cheaper than Copilot Enterprise. But "cheapest sticker" is not "cheapest in production": include Q Business's index, Copilot's GitHub dependency, and M365 Copilot's underlying-license assumption before declaring a winner. Q's consistent structural advantage is that it bills and runs inside your AWS account — which is where AWS credits can absorb the cost entirely.
Once the tiers are clear, most of the savings come from a short list of deliberate choices. These are the levers that actually move the bill — and the traps that inflate it.
Q Business monthly = (Lite seats × ~$3) + (Pro seats × ~$20) + (index units × ~$100). Q Developer monthly = (Pro seats × ~$19) + transformation overage in migration months. Build the model on these two formulas, then validate the per-unit rates on the live AWS pricing pages before committing.
The pricing above is the list price. The reason this matters for an AWS-native buyer is that Amazon Q consumption is ordinary AWS spend — which means AWS credit programs can absorb it, especially during the build-and-pilot phase when you are proving value.
Because Q Developer and Q Business both bill through your AWS account, the seat and index charges show up as AWS consumption — the same line items AWS credits apply against. For a team standing up Q Business (or rolling out Q Developer org-wide), the pilot and early-rollout spend can be largely or fully credit-funded rather than paid out of pocket, which removes the "what if adoption is slow" budget risk during the period you are still measuring value.
AWS funds these credit pools through partner-incentive programs: a Bedrock / GenAI proof-of-concept pool (typically $10K–$50K) is well-matched to a Q Business pilot, with Activate Portfolio credits (up to $100K) behind it for the broader AWS spend, and the GenAI Accelerator (up to $1M) for the largest AI-first build-outs. See AWS PoC / Bedrock POC funding explained, $100K AWS credits, and AWS credits for generative-AI startups for the mechanics of each.
The customer-facing point is simple: you pay $0 for the engagement. AWS funds the credit pool because it wants workloads consolidated on AWS; the vetted partner who configures Q (connectors, IAM Identity Center, permission mapping, guardrails, tier strategy) is paid through AWS's engagement funding; and CloudRoute is paid a routing commission by the partner. You see neither invoice. The list prices on this page tell you the steady-state run-rate after credits are exhausted — and that run-rate is exactly what the tier-mix and index-sizing levers above are for.
A single scannable read on seat price across both markets Amazon Q plays in. Treat the second-meter column as load-bearing: the cheapest sticker is not always the cheapest in production.
| Product | Category | Approx. seat price | Second meter / catch | Data not used to train? |
|---|---|---|---|---|
| Amazon Q Developer Pro | AI coding assistant | ~$19 / user/mo (Free tier exists) | Transformation overage on big migrations | Yes |
| Amazon Q Business Lite | Enterprise RAG assistant | ~$3 / user/mo | Index/storage billed separately | Yes |
| Amazon Q Business Pro | Enterprise RAG assistant | ~$20 / user/mo | Index/storage billed separately | Yes |
| GitHub Copilot | AI coding assistant | ~$10 (Pro) / ~$19 (Business) / ~$39 (Enterprise) | Value tied to code living in GitHub | Yes (Business/Enterprise) |
| Microsoft 365 Copilot | Enterprise assistant | ~$30 / user/mo | On top of an existing M365 license | Yes |
| ChatGPT Enterprise | General assistant | Custom — typically ~$40–$60+ / user/mo | Seat minimums; custom quote | Yes |
Situation: Leadership had priced Amazon Q Business by multiplying headcount by the Pro rate (~$20) and arrived at a scary ~$24K/month number, which stalled the project. They had also entirely missed the index line item, so even that number was wrong in both directions. They wanted the assistant over all four sources with strict per-user permissions, but had no in-house GenAI team to model the real cost or build it — and no appetite to spend during a pilot whose value was still unproven.
What CloudRoute did: Routed within a day to an AWS Advanced-tier partner with Q Business and IAM Identity Center experience. The partner re-modeled the cost as a mostly-Lite deployment — ~1,050 Lite seats for occasional lookups, ~150 Pro seats for dispatch leads and analysts who needed plugins and QuickSight — plus the right number of index units for the ~900K-document corpus, landing the steady-state run-rate far below the original ~$24K guess. CloudRoute helped the partner file a Bedrock/GenAI PoC credit application to cover the pilot, with Activate Portfolio credits behind it for the surrounding AWS spend, so the entire build-and-pilot phase was credit-funded.
Outcome: Pilot live in under three weeks; permission verification confirmed restricted HR/finance docs in SharePoint stayed invisible to unentitled users. The mostly-Lite tier mix cut the projected seat bill by more than half versus the all-Pro estimate, and the first ~$45K of AWS consumption across pilot and rollout was credit-funded. CloudRoute's commission was paid by the partner out of AWS's engagement funding — the customer paid $0 for the routing.
pilot window: <3 weeks · seat mix: ~1,050 Lite + ~150 Pro · credit-funded AWS spend: ~$45K · cost to customer: $0
CloudRoute routes you to a vetted AWS partner who sizes the tiers and index correctly and stands up Amazon Q over your data — funded by AWS credits. AWS funds the build. Customer pays $0.