Claude vs Gemini — the honest comparison for AWS builders.
A neutral, per-task reference comparing Anthropic's Claude and Google's Gemini in 2026 — reasoning, coding, multimodal, long context, writing, tool use, cost, and latency — with the one structural fact most "Claude vs Gemini" articles skip: Gemini is a Google Cloud model and is not on Amazon Bedrock. If you build on AWS, Claude is the in-platform frontier model (IAM, VPC, one bill, and AWS credits apply); Gemini means standing up Google Cloud and Vertex AI alongside AWS. Honest verdicts per task, a decision table, and where each model genuinely wins.
- Claude and Gemini are both frontier model families and the quality gap on most everyday tasks is narrow — relative strengths flip with each generation, so benchmark on your own prompts rather than trusting leaderboards. As rough current shape: Claude is widely preferred for coding, agentic/tool-use reliability, and faithful long-document work; Gemini is strong on natively-multimodal tasks (image, audio, and video understanding), very long context, and tight integration with Google Search and the Google/Workspace ecosystem; the two trade blows on general reasoning and writing.
- The decisive fact for AWS teams is structural, not about quality: Gemini is a Google Cloud model and is not available on Amazon Bedrock. Claude runs natively on Bedrock (under your IAM, VPC, KMS, CloudTrail and on one AWS bill); Gemini is reached via Google's Vertex AI or the Gemini API — i.e. on Google Cloud (GCP), a different cloud. So for an AWS-native team the in-platform frontier model is Claude, and reaching Gemini means leaving AWS for GCP.
- That point has a money consequence. Claude on Bedrock is AWS spend, so AWS Activate credits can pay for it; Gemini on Vertex AI is Google Cloud spend, so AWS credits do not apply. For funding, your matched AWS partner can file POC or incremental workload (IW) funding for the workload: $50K to $150K for startups. Depends on the workload; AWS approves. You apply for Activate yourself.
IThe right way to compare Claude and Gemini in 2026
"Claude vs Gemini" is one of the most-searched questions in applied AI, and most answers age badly — they pin a winner to a specific benchmark on a specific day. Both are moving targets: Anthropic and Google each ship new generations regularly, and the lead on any given task changes hands. This page is built to stay useful by separating the parts that move from the parts that do not.
Two things are true at once in 2026. First, on the large majority of everyday tasks the quality difference between current Claude and current Gemini is small — both are highly capable frontier models, and for most production work either will clear your bar. Second, where there are real differences, they are task-specific and they shift with each release. A model that leads on a coding or multimodal benchmark this quarter may trail next quarter; relative strengths are not stable enough to hard-code into a decision that outlives a single generation.
So the durable advice is the same one good engineering teams already follow: benchmark the current candidates on your own task, your own prompts, and your own data before committing. A public leaderboard tells you very little about how a model behaves on your specific RAG corpus, your coding style, your document formats, your tool schemas, or your latency budget. Run a small head-to-head on representative requests and measure quality, cost, and latency together.
What is durable — and what this page leans on — is the part that does not change with a model release: where each model runs, how it is governed and billed, and whether AWS credits apply. For a team building on AWS, that structural layer often matters more than a few points on a benchmark, because it determines your security posture, your bill, and whether the build is funded. The rest of this page covers both layers: an honest, per-task read on quality (the part that moves), and a clear account of the platform reality (the part that does not).
One caveat, stated once and meant throughout: specific model version names, context-window sizes, per-token prices, benchmark results, and even which models are offered on which platform all change frequently. Figures and characterizations here are representative as of 2026 to convey relative shape, not audited current numbers. Confirm model availability in the Bedrock model catalog, current Claude rates on the AWS Bedrock pricing page, and current Gemini rates on Google's Vertex AI / Gemini API pricing pages before you build or budget.
On quality: close on most tasks; benchmark on your own prompts because the lead shifts each generation. On platform: Gemini is a Google Cloud model and is not on Amazon Bedrock — Claude is the in-platform frontier model for AWS teams, and AWS credits apply to it (they do not apply to Gemini on Vertex AI / Google).
IIThe structural difference: Gemini is a Google Cloud model, not on Amazon Bedrock
Almost every "Claude vs Gemini" article compares the two models as if you reach them the same way. On AWS you do not. This is the single most decision-relevant difference for an AWS builder, and it has nothing to do with which model is "smarter."
Amazon Bedrock is AWS's managed service for calling foundation models through one API, with providers including Anthropic (Claude), Amazon (Nova, Titan), Meta (Llama), Mistral, Cohere, AI21, Stability AI, and DeepSeek. Google's Gemini models are not part of that catalog. Gemini is Google's own flagship family and Google distributes it through its own surfaces — the Gemini API (Google AI Studio) for developers and Vertex AI for enterprise — both of which run on Google Cloud (GCP), a different cloud. So on AWS, the practical question is not "Claude or Gemini, picked neutrally" but "the in-platform frontier model (Claude, native on Bedrock) versus going out of AWS to Google Cloud to reach Gemini."
That difference cascades into everything an AWS team cares about operationally. Reaching Claude on Bedrock means the call is authenticated with your existing IAM roles and policies, can stay on your private network via VPC endpoints (PrivateLink), encrypts with your KMS keys, is audited in CloudTrail, and lands on your existing AWS invoice in the same Cost Explorer and budgets as the rest of your stack — no new key to provision and secure, no new vendor, no second cloud.
Reaching Gemini, by contrast, means one of two things. Via the Gemini API / Google AI Studio you add a separate vendor surface: a Google API key to manage and rotate, a separate bill and payment relationship, and data leaving your AWS account for Google's platform. Via Vertex AI you get the enterprise controls (Google Cloud IAM, VPC Service Controls, CMEK, Cloud Audit Logs, a GCP bill) — but those are Google Cloud's controls, which means standing up and operating a second cloud provider alongside AWS, with the cross-cloud networking, egress, identity-federation, and dual-platform operational overhead that implies.
None of this says Gemini is worse. It says that for a team whose stack, identity, networking, and billing already live on AWS, Claude is the model that fits inside what you already run, and Gemini is the model that requires you to step outside it onto Google Cloud. For some teams that step is worth it for a specific Gemini strength; for many it is friction with no offsetting model-quality reason. Either way it is a real architectural decision, not a footnote — and it is exactly the part generic comparisons leave out.
- Claude → on AWS, in-platform: Native on Amazon Bedrock. One API, IAM auth, VPC/PrivateLink, KMS, CloudTrail, one consolidated AWS bill. Your data stays in your AWS account and chosen region and is not used to train base models.
- Gemini → on Google Cloud, two surfaces: Not on Bedrock. Reached via the Gemini API / Google AI Studio (a separate vendor surface, key, and bill; data leaves AWS) or Vertex AI (a second cloud — Google Cloud identity, networking, and billing, with cross-cloud operational overhead).
- The consequence for AWS teams: For an AWS-native stack, Claude is the frontier model you can adopt without leaving AWS or adding a provider; Gemini means standing up and operating Google Cloud alongside AWS. That is an architecture decision before it is a model decision.
If you are already on AWS, choosing Gemini is not just choosing a model — it is choosing to run a second cloud (Google Cloud / Vertex AI) or a second vendor surface (the Gemini API). Choosing Claude keeps everything under one account, one identity model, one bill. Unless a specific Gemini capability is decisive for your workload, the in-platform option is usually the lower-friction frontier pick.
IIIQuality, part 1: reasoning and coding
Now the part that moves — model quality, task by task, with honest verdicts. We start with reasoning and coding because they are where teams report the clearest preferences and where the spend often concentrates. Remember the framing: close overall, benchmark on your own prompts, leads shift per generation.
Reasoning and analysis
Both families field strong reasoning models, and both now ship explicit deeper-reasoning modes — Claude's extended thinking and Gemini's reasoning-oriented "thinking" variants — that spend extra internal steps on hard problems (complex math, multi-step logic, careful analysis) at some cost in latency and output tokens. On hard, structured reasoning the two trade the lead generation to generation; neither has a durable, across-the-board edge. Gemini's reasoning often benefits from its tight coupling to Google Search grounding for fact-heavy questions; Claude is frequently cited for staying faithful and well-structured across long, document-grounded reasoning.
Honest verdict: roughly even, task-dependent. For reasoning that must stay tightly grounded in a large body of supplied text, teams often lean Claude (see long context below). For reasoning that benefits from live web grounding and Google-scale knowledge, Gemini's search integration is a genuine convenience. The right answer for your reasoning workload is a small bake-off, not a leaderboard.
Coding
Coding is the task where Claude has the strongest reputation in 2026 — it is widely preferred by developers for code generation, multi-file refactoring, debugging, and especially agentic coding (working through a task across many tool calls without losing the thread). Anthropic has leaned hard into coding and agentic reliability, and a great deal of the developer-tooling ecosystem is built around Claude. Gemini is also a strong and fast-improving coding model — competitive on snippet-level generation, helped by very long context for whole-repo reasoning, and well-integrated with Google's developer tooling — but for sustained, multi-step agentic dev work Claude remains the more common preference.
Honest verdict: Claude is the common preference for serious coding and agentic dev work, particularly on larger, multi-step tasks; Gemini is fully competitive and its very long context is a real asset for reasoning over large codebases in one shot. If coding is your primary workload this is a real reason to favour Claude — and conveniently it is also the in-platform Bedrock option for AWS teams. Still, benchmark both on your actual repository and coding patterns.
If your dominant workload is coding or agentic dev, the model many developers prefer (Claude) is also the one that runs natively on AWS Bedrock and is AWS-credit-eligible. That alignment — best-fit model and in-platform model being the same — is why coding-heavy AWS teams rarely need to leave AWS for Gemini.
IVQuality, part 2: multimodal and long context (Gemini's strongholds)
The next cluster of tasks — native multimodality across image, audio, and video, plus very long context — is where Gemini has built its strongest reputation, and where an AWS team should be most honest about what Claude on Bedrock does and does not cover. Same rule applies: close where it overlaps, but the asymmetries here are real.
Multimodal: image, audio, and video
Gemini was designed as a natively multimodal family and is frequently cited as a leader for understanding not just text and images but also audio and video in a single model — transcribing and reasoning over audio, and analyzing long video clips frame-by-frame, are areas where Gemini's native handling is a genuine strength. Claude on Bedrock is strongly multimodal for text and image understanding (reading charts, screenshots, documents, diagrams), but native audio and video understanding are not Claude's focus — on AWS those modalities are typically handled by purpose-built services (Amazon Transcribe for speech-to-text, Rekognition for video analysis) feeding text into Claude, rather than by one model end-to-end.
Honest verdict: for image-and-text understanding, the two are broadly comparable — pick on your other criteria. For native audio and video understanding in a single model, Gemini has a real edge; if your product depends on one model ingesting video or audio directly, that is a genuine point for Gemini. On AWS you would instead compose Claude with Transcribe / Rekognition, which is more moving parts but keeps everything in-platform and credit-eligible.
Long context
Both families offer very large context windows measured in the hundreds of thousands to millions of tokens, with specific ceilings that change by model and generation. Gemini is well known for pushing extremely large context windows (often cited among the largest available), which is attractive for ingesting huge documents, long videos, or large codebases in a single call. Claude also offers large context and is frequently cited for strong long-context faithfulness — staying coherent and actually using the supplied material, not just accepting it. The usable quality of long context (does the model truly use the middle of a million-token input?) is something to test rather than assume on either side, and remember a big context costs more because input is billed per token — which is where prompt caching earns its keep.
Honest verdict: Gemini often wins on raw context-window size; Claude is strong on long-context faithfulness and pairs with managed RAG on AWS. For "stuff an enormous corpus into one prompt," Gemini's ceiling can be the draw; for "retrieve the right chunks and reason over them reliably," Claude with Bedrock Knowledge Bases is a clean in-platform pattern. Test both on your actual documents.
VQuality, part 3: writing, ecosystem, and tool use
The remaining cluster — writing quality, ecosystem gravity, and tool use / function calling — is where preferences get more subjective and where Gemini's Google integrations and Claude's agentic reliability enter the picture. Same rule applies: close where it overlaps, test it yourself.
Writing and tone
Both write fluently across formats. The differences people report are stylistic rather than capability-level: Claude is frequently described as producing more natural, measured, and steerable long-form prose and as following nuanced tone-and-format instructions closely; Gemini is often described as crisp, well-structured, and strong at synthesizing across many sources (helped by its grounding and Google-ecosystem context). These are preferences, not rankings — and they vary by prompt and by reader.
Honest verdict: a wash that comes down to taste and your specific style guide. If brand voice matters, run the same brief through both and let the people who own the voice pick. Neither is a wrong answer for general writing.
Ecosystem and grounding
Ecosystem is where the two diverge by design. Gemini is woven into Google's world — Google Search grounding, Workspace (Docs, Gmail, Sheets), Android, and the broader Google Cloud data stack (BigQuery and friends) — so if your organization already lives in Google's ecosystem, Gemini reaches your data and your users with the least friction. Claude's gravity is the opposite: it is woven into the AWS world via Bedrock (Agents, Knowledge Bases, the Converse API) and into the developer-tooling ecosystem, so for an AWS-and-developer-centric organization Claude reaches your data and your stack with the least friction.
Honest verdict: ecosystem fit is not about which model is better — it is about which cloud and which productivity suite you already run. Google shop → Gemini is the path of least resistance. AWS shop → Claude is. This mirrors the platform argument and usually points the same way as the rest of your stack.
Tool use / function calling
Both support structured tool use — you describe functions or APIs and the model decides when to call them and with what arguments, then folds the results into its answer. This is the foundation of agents. Claude has a strong reputation for reliable, well-formed tool calls and multi-step agentic loops, closely tied to its coding strength; Gemini also has mature function-calling and benefits from native tool integrations within Google's ecosystem (Search, code execution, and Google APIs). For complex agents that chain many tool calls, small differences in reliability compound, so this is worth measuring on your actual tool schemas.
Honest verdict: edge to Claude for complex, multi-step agentic chains; Gemini is strong and especially convenient when the tools you want to call are Google's own. On AWS, Claude's tool use is first-class through the Converse API and Bedrock Agents — the in-platform way to build agents without leaving your account.
VICost and latency: comparing across platforms
Cost and latency are decisive in production, and here the cross-platform nature of the comparison bites: Claude and Gemini are priced by different vendors, on different clouds, across different tiers of capability. The honest way to compare is by tier and by your real token mix, not by a single sticker number.
Both families price per token, with a rate per million input tokens and a higher rate per million output tokens, and both offer a tiered lineup — a cheap/fast small model, a balanced mid model, and an expensive frontier/deep-reasoning model. Claude's tiers on Bedrock are Haiku (cheapest, fast), Sonnet (the mid workhorse), and Opus-class (priciest, deepest); Gemini's lineup similarly spans a small/fast tier (the "Flash"-class efficient models) up to its frontier "Pro"-class models, with deeper-reasoning variants often priced higher. Within each comparable tier the two vendors are usually in the same broad ballpark, and which is cheaper for you depends on your input/output ratio, your tier mix, your context size, and discounts. Gemini's cheap/fast tier is often very aggressively priced for high-volume work; Claude's Haiku is similarly positioned.
The bigger cost levers are usually not the sticker rate but the optimizations — and these differ by platform. On Bedrock, Claude benefits from Batch (roughly half price for async work) and prompt caching (stop re-paying for a repeated prefix), and from tiered routing across Haiku/Sonnet/Opus with a one-line model-ID change. Google offers its own analogues on Vertex AI / the Gemini API (batch prediction, context caching, smaller models for routing). So the right comparison is not "Claude's rate vs Gemini's rate" but "your optimized cost on each platform for your workload" — and note that very long Gemini contexts, while convenient, are billed per input token and can get expensive fast without caching.
Latency is similarly tier- and deployment-dependent: the small models on both sides are fast and the frontier/reasoning models are slower, and deeper-reasoning modes add latency on both. Where you deploy matters too — Bedrock runs Claude in the AWS regions you choose, close to the rest of an AWS-based application (and cross-region inference helps availability and throughput); reaching Gemini from an AWS app adds a hop out to Google Cloud. For latency-sensitive, AWS-resident applications, keeping inference in-platform on Bedrock can be a real advantage independent of the model.
And then a lever that exists on only one side of this comparison: AWS credits. Claude on Bedrock is AWS spend, so AWS Activate credits can pay for it. Gemini on Vertex AI is Google Cloud spend (it would draw on Google Cloud credits, a separate program), so AWS credits never apply. The funding section below covers who files what.
| Tier role | Claude on Bedrock | Gemini (Vertex / Google) | Rough cost band (input/1M) | Typical use |
|---|---|---|---|---|
| Small / fast | Haiku | Gemini Flash-class | cents — low single $ | High-volume, routing/triage, extraction, latency-sensitive |
| Balanced mid | Sonnet | Gemini Pro-class (mid) | low single-digit $ | The production default: RAG, agents, support, coding, content |
| Frontier / reasoning | Opus-class | Gemini Pro/“thinking” frontier | high single — low double-digit $ | Hardest reasoning, complex agents, high-stakes analysis |
| Async / bulk | Batch (~50% off) | Batch prediction | ~half on-demand | Non-interactive bulk jobs |
| Repeated / huge context | Prompt caching | Context caching | discounts fixed prefix | Chatbots/RAG with a large fixed prompt; very long contexts |
VIIWhen Gemini is the right call (even for an AWS team)
This page argues that Claude is the natural frontier pick for AWS-native teams — but not unconditionally. Here, honestly, are the situations where reaching out of AWS to Google Cloud for Gemini is the right decision, and where it is not.
The structural advantage of Claude on AWS is real, but it is an operational advantage. If a specific Gemini capability is genuinely decisive for your product, the operational friction of Vertex AI or the Gemini API can be worth paying. The honest cases:
- You are already a Google Cloud / Workspace shop: If your stack, identity, data warehouse (BigQuery), and billing already live in Google Cloud — or your org runs on Google Workspace — the calculus flips: Gemini via Vertex AI is the in-platform pick for you, and Claude would be the out-of-platform one (though Claude is also available on Vertex AI's model garden and elsewhere). The "stay in-platform" logic is symmetric — it just favours whichever cloud you already run.
- You need native audio or video understanding in one model: Gemini's native handling of audio and video in a single model is a genuine strength. If your product depends on one model ingesting video or audio directly (long-video analysis, audio reasoning), that is a real point for Gemini — on AWS you would instead compose Claude with Amazon Transcribe and Rekognition, which works but is more moving parts than one model end-to-end.
- You need the very largest context window or deep Google Search grounding: If your use case truly needs to put an enormous corpus into a single prompt, or depends on live Google Search grounding as a first-class feature, Gemini's very large context and native search integration are real differentiators. On AWS the equivalent is retrieval (Bedrock Knowledge Bases) plus a web-search tool, which is a different architecture.
- You benchmarked both on your task and Gemini clearly won: The whole framing of this page is "test on your own prompts." If you did, and Gemini measurably wins on your specific workload by a margin that matters, that evidence beats any structural argument. Use the model that wins your bake-off.
- When it is NOT worth leaving AWS: For the common case — a coding-heavy, agentic, RAG-driven, or text-and-document workload on an AWS-native stack where quality is close — adding a second cloud (Google Cloud / Vertex AI) to reach Gemini is usually friction with no offsetting quality reason. Here the in-platform, credit-eligible option (Claude on Bedrock) is the pragmatic choice.
VIIIClaude vs Gemini — the decision table for AWS builders
The whole comparison in one scannable place: per-dimension honest verdict, who tends to lead, and what it means specifically for a team building on AWS. Verdicts are representative as of 2026 and shift by generation — confirm with your own benchmark.
| Dimension | Claude | Gemini | Honest verdict | For an AWS team |
|---|---|---|---|---|
| On Amazon Bedrock? | Yes — native | No (Google Cloud) | Decisive structural difference | Claude is in-platform; Gemini means Vertex AI/GCP (off AWS) |
| Coding / agentic dev | Often preferred | Strong, big context | Edge: Claude (esp. multi-step) | Preferred model is also the in-platform one — favour Claude |
| Reasoning / analysis | Strong, faithful | Strong + search grounding | Even — task-dependent | Either works; Claude keeps it on AWS |
| Image + text understanding | Strong | Strong | Even | Either; Claude in-platform on Bedrock |
| Audio / video understanding | Not Claude's focus | Native, leading | Edge: Gemini | On AWS, compose Claude + Transcribe / Rekognition |
| Long context | Large + faithful | Among the largest | Edge: Gemini on size; Claude on faithfulness | Pairs with Bedrock Knowledge Bases + caching |
| Writing / tone | Natural, steerable | Crisp, synthesizing | A wash — taste-driven | Pick on voice; not a platform issue |
| Tool use / agents | Reliable, well-formed | Mature + Google tools | Edge: Claude for complex chains | Wired into Bedrock Agents + Converse |
| Ecosystem gravity | AWS + dev tooling | Google / Workspace / GCP | Whichever you already run | AWS shop → Claude is the natural fit |
| Cost (per tier) | Haiku/Sonnet/Opus | Flash / Pro tiers | Same broad band per tier | Plus AWS credits apply to Claude only |
| AWS credits apply? | Yes (it is AWS spend) | No (Google Cloud spend) | Matters for funded startups | Activate credits can pay for Claude on Bedrock, not for Gemini |
IXHow to fund Claude usage, and who files it
Everything above prices Claude as if you pay AWS directly. Two kinds of AWS money can pay that bill instead, and different people file them.
AWS Activate credits. Claude on Bedrock is AWS spend, so Activate credits can pay for it: AWS made them redeemable for third-party models on Bedrock, Anthropic’s included, in April 2024 (AWS announcement). They never apply to Gemini on Vertex AI or the Gemini API, which is Google Cloud spend. You apply for Activate credits yourself. Activate Founders is $1,000 to start, up to $5,000, with no investor needed. Activate Portfolio is up to $200,000, using the Org ID from your VC, accelerator or angel group if it is an AWS Activate Provider. A consulting partner cannot file Activate for you.
Partner-filed funding. Your matched AWS partner can file POC (proof of concept) funding to test the workload, or incremental workload (IW) funding if you already run on AWS and this is new work. For startups that is $50K to $150K. Depends on the workload; AWS approves. A partner can also run the head-to-head on your task and build the Claude workload on Bedrock.
Startups pay CloudRoute nothing. Partners pay CloudRoute a monthly membership plus 10% of AWS-approved funding. For the details, see how POC funding works.
| Funding | Who files it | Amount | When it fits |
|---|---|---|---|
| Migration Acceleration Program (MAP) | Your matched AWS partner | $50K to $150K for startups. Depends on the workload; AWS approves. | You are not on AWS yet and want to migrate. |
| Proof of concept (POC) funding | Your matched AWS partner | $50K to $150K for startups. Depends on the workload; AWS approves. | You want to test something, whether or not you are on AWS today. |
| Incremental workload (IW) funding | Your matched AWS partner | $50K to $150K for startups. Depends on the workload; AWS approves. | You are already on AWS and are launching new work: a new AI feature, a new customer or a new contract. |
| AWS Activate Founders | Founder applies | $1,000 to start, up to $5,000 | Self-funded startups with no investor or accelerator behind them. |
| AWS Activate Portfolio | Founder applies | up to $200,000 | Startups backed by a VC firm, accelerator or angel investor that is an AWS Activate Provider. |
| AWS Credits for AI Startups | Invite only (AWS account manager) | $200,000 or more, by invitation | AI startups ready to scale after Activate Portfolio, invited by AWS. |
Claude vs Gemini for AWS teams — quality, platform, and funding
The comparison distilled to the three things that actually drive the decision: model quality (close, task-dependent), platform fit (Claude is on AWS; Gemini is on Google Cloud), and funding (AWS credits apply to Claude only). Representative 2026 read, not quotes — benchmark quality on your own prompts.
| Decision driver | Claude (on Bedrock) | Gemini (Vertex / Google) | What it means for an AWS team |
|---|---|---|---|
| Overall quality | Frontier; strong on coding, agents, faithful long context | Frontier; strong native multimodal, huge context, Google grounding | Close on most tasks — test on your own workload |
| Runs on AWS Bedrock? | Yes — native, one API | No — it is a Google Cloud model | Claude fits your stack; Gemini needs Vertex AI / the Gemini API |
| Security & billing | IAM, VPC, KMS, CloudTrail, one AWS bill | Google Cloud controls + GCP bill, or a separate Gemini API surface | Claude reuses what you have; Gemini adds a cloud or vendor surface |
| Best-fit workloads | Coding, agents, RAG, document/text work | Native audio/video, very long context, Google-ecosystem data | Coding/agentic/AWS-resident apps lean Claude |
| AWS credits apply? | Yes, it is AWS spend | No, it is Google Cloud spend | Activate credits can pay for Claude; Gemini needs Google Cloud budget |
| Pick the other one when… | — | You are Google-native, need native video/audio or the largest context, or Gemini wins your benchmark | Otherwise the in-platform, credit-eligible pick is Claude |
How a match works (example)
- You tell us about the work
You fill in a short form: what you run today, what you want to move, test or build, your stage and your region.
- We pick one partner
We read your answers and pick one vetted AWS consulting partner that fits the work, the stack and the region. We reply within 24 hours; your matched partner reaches out within 5 business days.
- The partner checks POC funding
Your matched partner scopes the test with you. Then it checks whether the test qualifies for POC (proof of concept) funding. A POC can fit whether or not you run on AWS today.
- The partner files, AWS decides
If the work qualifies, the partner files the funding request with AWS. AWS reviews it and decides. The amount depends on the workload.
- You pay nothing
CloudRoute is free for startups. Partners pay CloudRoute a monthly membership plus 10% of AWS-approved funding.
AWS Activate credits are separate, and you apply for them yourself. Use Activate Founders if no investor backs you. Use Activate Portfolio with the Org ID or invite from your VC, accelerator, incubator or angel group if it is an Activate Provider. A consulting partner cannot apply for Activate on your behalf.
Common questions
Is Claude or Gemini better in 2026?
Is Gemini available on Amazon Bedrock?
How do I use Gemini on AWS?
Which is better for coding, Claude or Gemini?
Claude vs Gemini for multimodal, audio, and video?
Do AWS credits work with Gemini?
Claude vs Gemini for context window and long documents?
When should an AWS team still choose Gemini over Claude?
Where quality is close, build in-platform and fund the workload
Gemini means adding Google Cloud next to AWS, and AWS credits never apply to it. Claude runs natively on Bedrock under your existing IAM, VPC and billing, and AWS Activate credits can pay for it. CloudRoute matches you with a vetted AWS partner who benchmarks Claude vs Gemini on your task, builds it on Bedrock, and can file POC or IW funding when the workload qualifies and AWS approves. Startups pay CloudRoute nothing. Partners pay CloudRoute a monthly membership plus 10% of AWS-approved funding.