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AI on AWS · AI · Los Angeles

Amazon Bedrock and SageMaker consulting, with your data kept in AWS

Pick the layer that matches the job: a managed model call, a hosted model you control, or raw GPUs.

AI (now also called SI, super intelligence) on AWS comes in three layers. Amazon Bedrock serves ready models such as Claude, Llama, Mistral and Amazon Nova through one API. Amazon SageMaker AI hosts and tunes models you pick yourself. Under both sit GPU and accelerator instances on EC2. Our Glendale-led team chooses the right layer for each AI / SI job, builds private assistants over your own documents, sets the IAM rules and guardrails, and pipes the answers into Zoho CRM, Desk and Cliq.

  • 3 layersmanaged, hosted, bare GPU
  • Per tokenor per instance-hour
  • Your accountdocuments, vectors and logs
Book an AWS call Bedrock or SageMaker?
AWS AWS PartnerAlso a Zoho Advanced Partner
You’ll talk to our senior team, not a call center.300+ projects · serving US clients since 2025

Path: ready

SampleOne question, two possible routes down the stack.

01Should your AI live on AWS?

When does it make sense to run AI / SI on AWS instead of a vendor’s app?

If your files, customers and databases already sit in an AWS account, keeping the model calls there too is usually the cleanest choice for privacy reviews and billing. If you just want a chat window for a few people, a vendor’s own app is quicker. This is the split we use.

Strong reasons to build on AWS

  1. A legal or security team must sign off. IAM roles, KMS keys, VPC endpoints and CloudTrail give reviewers controls they already know.
  2. The answers depend on your own documents. Policies, manuals and contracts in S3 can feed a Bedrock knowledge base without copying them to another vendor.
  3. You want to swap models later. Bedrock puts several model families behind one API, so changing from one to another is a setting plus a round of testing.
  4. You need a model nobody serves for you. An open-weight or fine-tuned model can run on a SageMaker AI endpoint you size yourself.
  5. One bill, one set of logins. Usage lands on the AWS invoice you already pay, under budgets and alerts you already have.

When we’d point you elsewhere

  1. Everything happens in a single Zoho app. Zia may already do it in your edition. See our Zoho Zia page first.
  2. You have no AWS account and no plans for one. A direct model API, or an agent on Zoho Catalyst, gets you there with less setup.
  3. A feature shipped by a model maker last week. New tools sometimes reach the vendor’s own API before Bedrock. If you need it today, we wire that API instead.

02AWS AI consulting services

What our AWS AI / SI consultants build for US companies

Most first projects are a private assistant, a summarizer or a classifier. They look small on a slide and still need access rules, test questions, cost alarms and someone watching them after launch. That’s the work below.

  1. 01

    Bedrock knowledge assistants

    Your PDFs and wiki pages indexed into a Bedrock knowledge base, answers with links back to the source page, and metadata filters so each department only searches what it should.

    Ships with a set of test questions you approve

  2. 02

    SageMaker AI model hosting

    Open-weight models from SageMaker JumpStart or Hugging Face deployed to real-time, serverless or asynchronous endpoints, with autoscaling and a plan to shut idle ones down.

    Instance type picked from load tests, never guessed

  3. 03

    GPU and accelerator sizing

    G6 or G5 for most inference, P5 for heavy training, Inferentia2 and Trainium when the model supports them. We check quotas, Savings Plans and Spot before you commit.

    A monthly cost range written down per option

  4. 04

    Guardrails, IAM and privacy

    Bedrock Guardrails for PII masking and denied topics, least-privilege roles per function, PrivateLink endpoints so traffic skips the public internet, and logging switched on.

    A one-page data flow your security lead can file

  5. 05

    Agents and Zoho actions

    Bedrock Agents or AgentCore calling Lambda tools that read and update Zoho records, with a person approving anything that touches money or customers.

    Every tool call kept in CloudWatch Logs

  6. 06

    Cost watch after launch

    Token and instance spend tagged per app, AWS Budgets alerts, batch jobs moved to half-price batch inference, and prompt caching where the model offers it.

    A monthly note on spend, quality flags and changes

03A private assistant, mapped

Where does a question go inside a private Bedrock assistant?

Picture a 60-person parts distributor in the Inland Empire. Counter staff keep asking the same questions about return windows and special orders, and the answers sit in thirty PDFs. We build them a knowledge assistant on Amazon Bedrock. Step through it below: the documents, the vectors and the logs all live inside their own AWS account.

Outside AWS

Zoho Cliq botwhere staff ask
Zoho WorkDrivesource folder of PDFs

Their AWS account · US East (N. Virginia)

S3 bucketencrypted with their KMS key
Knowledge basechunks, Titan embeddings
Vector storeS3 Vectors or Aurora pgvector
API Gatewaysigned requests only
Lambdaretrieve, then generate
Bedrock modelplus Guardrails
CloudWatch + CloudTraillogs and audit trail
IAM rolesone per function
VPC endpointPrivateLink to Bedrock

Load the documents

A scheduled sync copies the returns policy, warranty sheets and special-order rules from a WorkDrive folder into an S3 bucket. The bucket only accepts traffic from their account and encrypts every file with a key they own.

Leaves their account? No

Sample scenario, not a client. Some newer models on Bedrock run through cross-Region inference profiles, which can process a request in another US Region; we tell you which ones before you choose.

04Bedrock vs SageMaker vs API

Bedrock, SageMaker AI or a direct model API: which corner are you in?

Tap every statement that sounds like your team. The dot slides toward the option those needs pull hardest on. Underneath, a volume slider compares sample monthly costs at the rates AWS publishes for its Virginia Region.

Sample lean: no answers yet

Tap a statement or two to begin.

  • Bedrock · Amazon Nova Liteper token, nothing while idle$0
  • Bedrock · Claude Haiku 4.5per token, regional rate$0
  • SageMaker AI · ml.g5.xlargeone real-time endpoint, on all month$0

Working it out…

Sample model-side charges only, before tax. Vector store, Lambda, logs and data transfer are extra. One ml.g5.xlarge instance doesn’t promise any particular throughput; we load-test before sizing. No quality claim is made about any model here.

05Bedrock + Zoho

How do Bedrock and SageMaker answers reach Zoho?

Zoho apps send webhooks out and accept REST calls in, so a small Lambda function sits between them and the model. Three wiring patterns cover most requests we get.

Desk replies

  1. New ticket in Zoho Desk
  2. Lambda asks the knowledge base
  3. Bedrock drafts a reply with sources
  4. Draft saved as a private comment for the agent

CRM call notes

  1. Call logged on a Zoho CRM deal
  2. Webhook queued in SQS
  3. Model writes a summary and next step
  4. Summary field and a follow-up task updated

Custom scoring

  1. Nightly export of won and lost deals
  2. SageMaker AI trains a small scoring model
  3. Batch transform scores open leads
  4. Scores land in CRM and a Zoho Analytics view

Want an assistant like Claude to act inside Zoho directly? That’s what Zoho MCP is for, and it pairs well with a Bedrock knowledge base for the document side. Lighter glue between Zoho apps can stay in Zoho Flow.

06Bedrock and SageMaker pricing

What do Amazon Bedrock and SageMaker AI cost?

Bedrock bills per token: input and output are priced separately, and every model has its own rate. SageMaker AI bills per instance-hour while an endpoint runs, whether or not anyone is asking. Below are a few US East (N. Virginia) on-demand list rates so the gap is easy to see.

Service and modelInputOutput
Bedrock · Amazon Nova Microtext only$0.035 per 1M tokens$0.14 per 1M tokens
Bedrock · Amazon Nova Lite$0.06 per 1M tokens$0.24 per 1M tokens
Bedrock · Amazon Nova Pro$0.80 per 1M tokens$3.20 per 1M tokens
Bedrock · Claude Haiku 4.5regional rate; global routing lists $1.00 / $5.00$1.10 per 1M tokens$5.50 per 1M tokens
Bedrock · Titan Text Embeddings V2used to index documents$0.02 per 1M tokensn/a
SageMaker AI real-time endpointper instance-hour while runningml.m5.large $0.115 · ml.g6.xlarge $1.1267 · ml.g5.xlarge $1.408

Meters people forget. A basic knowledge base is billed through what it uses: the embedding model and the vector store behind it. Guardrails are charged per text unit checked. Provisioned Throughput is an hourly commitment. Batch inference on the models above lists at half the on-demand rate.

Idle endpoints still bill. A SageMaker AI endpoint left on over a long weekend costs the same as a busy one. Serverless and asynchronous endpoints can scale to zero, and we use them when traffic is patchy.

What our part costs. We price the assistant, its guardrails, the IAM roles and every Zoho hook as one fixed sum, which goes into a signed BRD well ahead of any model touching your files. Model and instance charges go straight from AWS to you, unmarked. The first call is free.

Rates shown are US East (N. Virginia) on-demand list prices in US dollars, excluding tax, as of October 2026. Bedrock adds and reprices models often, and other US Regions can differ, so we re-run the numbers for your chosen model and Region during scoping.

07AI on AWS questions

Amazon Bedrock and SageMaker questions from US teams

Privacy, model choice, GPUs and cost come up on almost every call. Asking about one particular model or Region? Bring it, plus a rough count of questions per month.

Talk through your use caseTry the chooser →

What is Amazon Bedrock?

Amazon Bedrock is an AWS service that serves foundation models from several makers, including Anthropic Claude, Meta Llama, Mistral and Amazon Nova, through one API. You pay per token with no servers to run, and it adds tools such as knowledge bases for your own documents, Guardrails and agents.

Amazon Bedrock vs SageMaker AI: which should we use?

Use Bedrock when a model it already serves can do the job, because there is nothing to host and you pay only for tokens. Use SageMaker AI when you need to host an open-weight or fine-tuned model yourself, control the instance type, or train your own model. Many projects start on Bedrock and add SageMaker AI later for one special model.

Does Amazon Bedrock use our data to train models?

AWS states that Bedrock does not store or log your prompts and completions, does not use them to train AWS or third-party models, and does not share them with the model makers. Your documents, vector store and logs remain inside the AWS account you own, encrypted with keys you control.

What is SI?

SI stands for super intelligence. In September 2026 a US executive order asked federal agencies to use “super intelligence” or “SI” in place of “artificial intelligence” in official communications. Most businesses still say AI, so we use both terms.

How much does Amazon Bedrock cost?

Each model has its own per-token rates. In US East (N. Virginia), Amazon Nova Lite lists at $0.06 per million input tokens and $0.24 per million output tokens, and Claude Haiku 4.5 at $1.10 and $5.50 at the regional rate. A knowledge base adds the cost of its vector store and embeddings, and batch jobs list at half price on those models.

How much does a SageMaker AI endpoint cost per month?

A real-time endpoint bills per instance-hour for as long as it runs. In US East, an ml.g5.xlarge lists at $1.408 an hour, which is about $1,028 for a 730-hour month on one instance. Serverless and asynchronous endpoints can scale down to zero when traffic is patchy.

Which GPU instance do we need to run an open-source model on AWS?

It depends on the model size and how many requests arrive at once. Smaller open models often fit on a single G6 or G5 instance, while large ones may need several GPUs or a P5 instance. We load-test on your prompts, then compare on-demand, Savings Plans and Inferentia2 before you commit.

Can we build a private ChatGPT-style assistant over our own documents on AWS?

Yes. The usual design is S3 for the files, a Bedrock knowledge base with a vector store for search, a Lambda function that retrieves passages and calls a model, and Guardrails on the output. Staff can ask through a web page, Zoho Cliq or Zoho Desk, and every answer links back to its source.

Can Amazon Bedrock send answers into Zoho CRM or Zoho Desk?

Yes, through a small layer of code. Zoho sends a webhook when a record changes, a Lambda function calls Bedrock, and the result is written back through the Zoho REST API as a draft reply, a summary field or a task. For assistants that act inside Zoho on request, we also use Zoho MCP.

What does an AWS AI build with you cost?

The first call is free. Each build then carries a fixed price in a signed BRD listing the assistant, the model, the test questions, the guardrails and the Zoho connections. Larger multi-model or GPU work may begin with a paid discovery, quoted in advance. AWS bills model and instance usage to you directly.

08How an AWS AI project starts

Twenty real questions before one line of code

We ask you for a short list of questions your team really asks, with the answers you’d accept. That list decides the model, the layer and whether the project is worth building. One assistant over one document set rarely needs more than that call to size. A multi-model or GPU project sometimes starts with a brief paid discovery, and you see that quote first.

We run AWS AI projects over video for companies across North and Latin America. Near Los Angeles, your security reviewer can also meet us face to face.

  1. Discovery callThe job you want done, where the documents live, and who must approve
  2. Test setTwenty or so real questions with answers your team agrees are right
  3. Layer, model & estimateBedrock, SageMaker AI or GPUs, plus a monthly running range
  4. BRD sign-off Fixed priceAssistant, guardrails, Zoho hooks and a go-live date in writing

Once signed, the build runs on the same delivery track as any fresh project. See how we work →

09Let’s talk

Tell us what you’re building. We’ll map the fix.

Book a free consultation with Elite Tech Global. We review your Zoho, AWS, Shopify or WordPress setup, show where AI / SI agents can save time, and agree the next step. If we work together, every requirement and a fixed price go into a signed BRD before the build starts.

Real people, one team. Led by co-founders Ikramul Karim and Khris Kakosimidi, and a 40+ member team across Zoho, AWS and AI.

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