AWS Bedrock Pricing: Cost Breakdown, How to Optimize In 2026

Quick Answer: AWS Bedrock pricing differs by usage. For example, Nova Pro costs about $0.80 per million input tokens and $3.20 per million output tokens. Rates vary by model and region. 

Your Bedrock proof of concept worked. The model was good, the API was clean, you shipped a feature, and users like it.

Then the bill arrived.

It was higher than the back-of-napkin number you used to greenlight the project, and the lines you can see on the invoice do not quite explain why. You are not alone, and the answer is not “Bedrock is expensive.”

The answer is that Bedrock pricing has five or six moving parts that are not obvious until you have lived through one or two billing cycles.

A useful forecast starts with the workload: the model calls it makes, the context it sends, and the services supporting it. From there, you can test which changes lower costs without weakening the result.

Need to price your own Bedrock workload? Book a Bedrock cost review with Avahi.

Key Takeaways

• Forecast input and output tokens separately, using representative traffic and the rates for your chosen model and Region.
• Include supporting costs such as retrieval, vector storage, orchestration, and Guardrails in the budget.
• Test model routing, prompt caching, and batch inference against your quality and latency requirements before projecting savings.
• Consider Provisioned Throughput for steady demand. Reserved capacity still costs money when it sits idle.
• Book a Bedrock cost review with Avahi to assess your workload and scope a proof of value.

How AWS Bedrock Pricing Actually Works (Also Known as Amazon Bedrock Pricing)

Bedrock is usage-based. There is no minimum, no subscription, and no AWS Bedrock free tier you can rely on for production (more on the free question further down). You pay for what you use, but “what you use” has five categories worth understanding before you forecast a bill.

Four cost levers shape an Amazon Bedrock bill:

• On-Demand: Pay per 1,000 input tokens, per 1,000 output tokens, per image, or per second of generated video. No commitment, no minimum, prices vary by model and AWS Region. This is where most teams start.
• Provisioned Throughput: Reserve dedicated capacity for a specific model in model units, billed hourly whether you use the capacity or not. This is what you switch to when usage gets predictable and large.
• Batch Inference: Run asynchronous jobs at 50% off the On-Demand rate. For Amazon Nova specifically, AWS also offers a Flex tier at the same 50% discount, plus a Priority tier at approximately 1.75x Standard for mission-critical workloads that need preferential compute allocation. Suitable for jobs where latency does not matter (overnight summarization, document classification, periodic enrichment).
• Prompt Caching: Cache repeated input context (system prompts, large knowledge snippets, few-shot examples) and pay a fraction of the input-token rate for the cached portion. Savings of up to 90% on the input side for context-heavy applications.

These cost levers are not exclusive. A production system may combine several of them:

• On-Demand for live traffic
• Batch for overnight work
• Prompt Caching on the static parts of every request, and sometimes,
• Provisioned Throughput on the model that handles the bulk of the load.

Then there are the components that sit on top of the model and have their own pricing: Bedrock Knowledge Bases for RAG, Bedrock Agents for multi-step workflows, Bedrock Flows for visual orchestration, and Bedrock Guardrails for content moderation.

Budget for their applicable charges and the AWS services they invoke.

Quick Reference: The Four AWS Bedrock Pricing Modes at a Glance

On-Demand Pricing: The Default Starting Point

When you first call a Bedrock model through the API, you are on On-Demand. The model is shared multi-tenant, you pay per token, and there is no commitment.

The structure is the same across every text model: you pay separately for input tokens (your prompt, including any retrieved context and the system message) and output tokens (the model’s response). Output tokens generally cost 3x to 5x more than input tokens. This ratio is the single most important fact for cost forecasting, because most teams underweight output cost when they estimate spend.

A quick example using Amazon Nova Pro. As of June 2026 on the official AWS Nova pricing page, Nova Pro On-Demand is $0.80 per 1 million input tokens and $3.20 per 1 million output tokens, which works out to $0.0008 per 1,000 input tokens and $0.0032 per 1,000 output tokens.

So a typical chat exchange of 1,000 input tokens and 500 output tokens costs $0.0008 + $0.0016 = $0.0024 per call. At 100,000 calls per day, that is about $240 per day, or roughly $7,200 per month.

Selected Amazon Nova pricing in US East (N. Virginia), USD per million tokens. Standard on-demand rates unless labeled otherwise; AWS price list dated September 30, 2026.

Note: Cache read input tokens are billed at 75% less than the on-demand input token price. Flex tier and Batch tier prices are roughly 50% off the Standard tier rates shown. Nova 2 Lite is generally available. Availability and rates vary across the Nova 2 models. Verify the latest rates here.

Selected Anthropic Claude pricing on Amazon Bedrock (global cross–Region inference). USD per million tokens, Standard tier, from AWS’s September 30, 2026 price list for us-east-1. Claude 3.5 Haiku is the regional exception noted below.

A few things to check before using these prices:

• Region: Claude 3.5 Haiku prices apply to in-Region and geographic inference. Global inference uses different rates.
• Batch processing: AWS lists Claude 3.5 Haiku batch support in us-west-2 for single-Region requests and us-east-1 for cross-Region requests. Batch support isn’t listed for Opus 4.7 or 4.8.
• Caching: Cache-read prices don’t include the separate cost of writing to the cache.
• Context size: The Opus 4.5, Sonnet 4/4.5 and Haiku 4.5 prices shown apply to contexts of up to 200,000 tokens.
• Older models: Sonnet 4 is a legacy model with an end-of-life date of October 14, 2026. Check extended-access pricing before choosing it.
• Regional pricing: Rates can vary by inference option. For example, Sonnet 4.6 geo/in-Region pricing is $3.30 for input and $16.50 for output per million tokens.

Check AWS’s current pricing and model availability before budgeting.

How the input prices compare:

At $3 per million input tokens, Sonnet 4.6 costs:

• 3.75 times as much as Nova Pro, at $0.80
• 50 times as much as Nova Lite, at $0.06

These comparisons cover input tokens only. Your total also includes output tokens and any routing charges.

Anthropic’s Claude family on Bedrock follows the same pattern at a higher absolute cost.

• AWS Bedrock Anthropic pricing tracks Anthropic’s published API rates closely, with regional variation.
• Claude Opus 4.8 is one option for complex reasoning ($5.00 per 1M input tokens, $25.00 per 1M output at the global Standard rate).
• Claude Sonnet 4.6 is the mainstream high-quality choice ($3.00 per 1M input, $15.00 per 1M output, with a 1M-token context window).
• Claude Haiku 4.5 is the faster, cheaper option ($1.00 per 1M input, $5.00 per 1M output).
• Anthropic also has Mythos Preview available in gated research preview (non-GA, no published prices yet).
• Meta Llama and Mistral models offer alternative price-performance points. Always verify the current rate on the official AWS Bedrock pricing page before you commit to a budget, because rates move and Region matters.

For embeddings (vector models like Amazon Titan Text Embeddings or Cohere Embed), you pay only on input tokens because the output is a vector, not generated text. Embedding costs are usually small per call, but RAG applications generate a lot of them, so they add up.

For image and video generation, the billing unit can change to images or seconds of video. Check the selected model’s rate and lifecycle before budgeting: AWS lists September 30, 2026 EOL dates for Nova Canvas and Nova Reel.

The pattern across all of these: you are paying for output. The way to cut On-Demand cost is almost always to produce less output, or to produce the same output with a cheaper model.

Provisioned Throughput: When It Pays Off

Provisioned Throughput is the answer for steady, high-volume workloads. You reserve model units (each guaranteeing a known token-per-minute throughput) and you pay an hourly rate whether the capacity is in use or idle.

On-Demand avoids paying for idle reserved capacity. Compare its cost and service limits with the provisioned option against your actual demand.

Provisioned Throughput is more expensive per hour but offers predictable performance and predictable cost. There are three commitment tiers: no-commit (pay hourly, stop any time), 1-month commitment (lower hourly rate), and 6-month commitment (lowest hourly rate).

The honest math on whether to switch:

• If your daily token volume is modest and bursty, stay on On-Demand.
• For sustained traffic, compare the provisioned charge with on-demand spend for the same model, Region, and input/output mix. There is no universal tokens–per-minute break-even point.
• Inference options depend on the model and customization method. Imported custom models support on-demand inference. Fine-tuned models have model-specific deployment requirements, so check the applicable documentation before budgeting for capacity.

The mistake to avoid is over-reserving. Buying 6-month commitment capacity for traffic that is not yet stable is the single fastest way to overspend on Bedrock. Most teams should run on On-Demand long enough to see the real traffic shape before they commit.

When Does Provisioned Throughput Make Sense?

Measure stable hourly demand and service-level needs first. Provisioned Throughput reserves model capacity, so unused capacity still has a cost.

Compare the monthly provisioned charge with observed on-demand spend for the same model, Region, and traffic profile.

Keep bursty or early-stage traffic on demand unless the capacity or latency requirement justifies a reservation.

Batch Inference and Prompt Caching: The Two Quiet Levers

Two pricing modes are easy to miss and often move the bill more than the model-choice decision.

Batch Inference runs prompts asynchronously at half the On-Demand rate for supported models (AWS also has a Flex tier at the same 50% discount for supported models, with similar trade-offs).

The use cases are non-interactive:

• Overnight document summarization
• Periodic data enrichment
• Large-scale embedding generation
• End-of-day report generation
• Anything where the request and response do not need to happen in real time. If you can hold a request for hours, you can pay half.
• The pattern that works: take the high-volume, latency-tolerant parts of your workload and move them to Batch. The first place to look is anything that runs on a cron schedule. The second is anything where a user kicks off a job and is happy to get an email when it is done.

Prompt Caching is the bigger lever for context-heavy applications. If your prompts include a large static prefix (a system prompt, a large set of few-shot examples, a long policy document, a knowledge base context) and the prefix repeats across requests, Bedrock can cache the prefix and bill the cached tokens at a fraction of the standard input-token rate.

Cache-read savings can reach 90% on supported models. Net savings depend on reuse, cache–write charges, minimum prefix length, and expiry.

Check whether your application repeatedly sends a cache-eligible prefix.

If your application sends the same 5,000-token system prompt with every request, you are probably paying for 5,000 input tokens every call when you could be paying for a tiny fraction of that.

Worth an audit if you are seeing a higher-than-expected input-token bill.

The Hidden Costs: Knowledge Bases, Agents, Flows, Guardrails

Most teams budget for the model and forget the components that wrap around it. Each has its own line on the bill.

Bedrock Knowledge Bases is the managed RAG capability. The model invocation during retrieval is charged at standard inference rates. 

The hidden cost is the vector store: if you use Amazon OpenSearch Serverless as the backing index (the default managed option), expect a baseline of a few hundred dollars per month minimum for the OCU capacity, even at low query volume. 

For smaller workloads, alternatives like Amazon Aurora PostgreSQL with pgvector or self-managed OpenSearch can be cheaper but require more setup. There is also the embedding model cost during ingestion, which is paid once per document but recurs whenever you reindex.

Bedrock Agents orchestrate multi-step workflows where the model calls tools, retrieves context, and produces a final answer. 

The agent invocation itself does not have a fixed per-call fee; what you pay is the sum of every model call the agent makes along the way. 

Agents that make many tool calls and many model invocations per user request can be deceptively expensive if you are only counting the visible user interactions.

Bedrock Flows is the visual orchestration product for chaining prompts, conditions, and tools. The Flows-specific cost is roughly $0.035 per 1,000 visual node transitions, on top of the underlying model invocation costs. 

For low-volume use it is trivial; for high-volume production workloads it is worth measuring.

Bedrock Guardrails pricing depends on the filters enabled, the API used, and the volume evaluated. Check the AWS pricing page for the configuration you plan to deploy.

Include both input and output evaluations when you use Guardrails on both. Each enabled filter can add to the charge, so model the complete policy set.

None of these are unreasonable on their own. They become a problem when teams stack them all together without measuring each one’s contribution to the final number.

Hidden costs at a glance.

Model Routing: Match Cost to Request Complexity

If you read one section of this guide, read this one.

Model routing can lower costs when some requests meet the quality target on a cheaper model. Compare its impact with caching, batch processing, and capacity changes using your own traffic.

The pattern: send simple requests to a cheap, fast model. Send complex requests to a large, expensive model. Decide which is which programmatically.

An illustrative routing example: Suppose your application handles two kinds of queries: short factual lookups (about 70% of traffic) and complex multi-step reasoning (about 30% of traffic). 

If you route everything to Claude Sonnet 4.6 ($3/M input and $15/M output), you pay its rates on every call.

If 70% of requests move to Claude Haiku 4.5 ($1/M input and $5/M output), while 30% stay on Sonnet 4.6, the same input and output token volume costs about 46.7% less before routing overhead.

This illustration assumes the cheaper model meets the quality target. Test that assumption on representative traffic.

The implementation is a classifier or router function that runs before the model call. AWS Intelligent Prompt Routing supports selected model pairs within a family. Confirm model and Region support before choosing it; other combinations need application-level routing.

AWS publishes that it can reduce costs by up to 30% without compromising accuracy. If you want the routing pattern but do not want to build the classifier yourself, this is the no-build option.

The trade-off is real. Routing adds a small amount of latency, a tiny upfront cost for the classifier, and architectural complexity. For low-volume applications it is overkill. At higher volume, measure whether the savings justify that overhead.

This is the pattern Avahi has used with Groopview, our anchor case study below.

Optimization levers to test against your workload:

AWS Bedrock Pricing Calculator: Forecasting Your Bill

The official AWS Pricing Calculator supports Amazon Bedrock and is the right tool for a first forecast. The fields that matter most:

• Model and Region (rates differ across Regions)
• Daily input tokens and daily output tokens (your most-used model and pricing mode)
• Knowledge Bases vector store size and query rate
• Guardrails evaluations per day
• Image or video generation volume, if applicable
  

The honest caveat: the calculator only shows you what AWS will charge for components you input. It does not catch the patterns that drive overspend (oversized models, uncached prompts, over-reserved throughput, Guardrails running on every input and every output). 

For a real forecast on a real workload, instrument the application first, measure for a week or two on On-Demand, and forecast from observed usage. The calculator is a starting point, not the answer.

Build a Bedrock Cost Estimate From Your Workload

Start with the model, Region, and inference tier you will use. Estimate monthly requests and average input and output tokens from a representative traffic sample.

Monthly model cost = (input tokens ÷ 1,000,000 × input rate) + (output tokens ÷ 1,000,000 × output rate). Use rates quoted per million tokens for both parts of the calculation.

Use the official Amazon Bedrock pricing page for the model and Region you selected. Confirm supported tiers and availability before committing to the estimate.

Calculate expected traffic, a peak month, and a month with longer prompts or answers. Include retries and agent steps: one user action may trigger several model calls.

Add Charges Beyond Model Tokens

Add Guardrails, Knowledge Bases ingestion, storage, and retrieval infrastructure where applicable. Agents and Flows may trigger additional model calls and other AWS services.

Check each relevant line item on the AWS pricing page, then validate the complete estimate in AWS Pricing Calculator.

Test savings against your traffic. Caching requires reuse of a supported prompt prefix. Batch and Flex have model, latency, and availability conditions. Compare total cost, including supporting services, before switching.

When NOT to Use Bedrock

Compare Bedrock with alternatives against your workload and operating requirements.

Bedrock IS the right answer when:

• You want managed access to multiple frontier models behind one interface (Anthropic, Amazon Nova, Meta, Mistral, Cohere, and others without managing separate provider relationships).
• You need AWS-native integration with the rest of your stack (S3, Lambda, ECS, IAM, VPC, CloudWatch).
• You care about IAM controls, VPC isolation, and AWS-grade audit logging for compliance.
• The unit economics make sense at your scale once you apply routing, caching, and the right pricing mode.

Bedrock is NOT the right answer when:

• Your entire workload is one specific model at very high sustained volume and you can run it more cheaply elsewhere.
• You run Llama-family models at heavy sustained load (direct hosting on Amazon EC2 with Inferentia or a dedicated inference platform often beats Bedrock economics).
• Your use case fits a small open-source model and self-hosting on your own infrastructure is meaningfully cheaper.
• Your application only ever uses one provider’s models and going direct to that provider’s API wins on price (you give up the AWS-native security and integration story in exchange).

Bedrock optimizes for breadth (many models, one interface), governance (AWS-grade controls), and ease of integration. If those matter to you, include Bedrock in your shortlist. If you do not need them, run the math on alternatives before committing.

Real Result: How Groopview Cut AI Avatar Response Time by 80% with Dual-Nova Model Routing

Groopview builds an AI co-host avatar that processes text and images during live streams in real time. The product is latency-sensitive in a way that most enterprise AI is not: a slow response breaks the social-media experience the avatar is meant to enable.

The previous architecture routed every interaction through a single capable model. Response time was around 12 seconds. For a live-streaming product, that was too slow to feel real-time, and the model cost per interaction was higher than the unit economics could carry at scale.

We at Avahi rebuilt the architecture on Amazon Bedrock using a Dual-Nova orchestration framework, routing every request through a classifier that decides whether the query is simple or complex:

• Simple queries (most of the traffic) route to Amazon Nova Lite, the smaller and cheaper model.
• Complex queries route to Amazon Nova Pro, the larger, higher-quality model.
  

The stack: Amazon Bedrock (Nova Pro and Nova Lite), Amazon API Gateway, AWS Lambda, Amazon EC2 g6e GPU instances for the avatar rendering, Amazon S3, Amazon RDS, and Amazon CloudWatch for observability.

The result:

• AI avatar response time dropped from 12 seconds to about 2.5 seconds for simple queries (an 80% latency reduction).
• Complex queries return in about 7 seconds.
• Higher session stickiness and new revenue streams from the improved real-time experience.
• The cost-per-interaction dropped because the majority of traffic now hits the cheaper model.
  

This is the model-routing pattern made concrete. Same product, same models available, different architectural choice on which request hits which model.

Read the full case study →

Where AWS Funding Fits

The part most pricing guides cannot offer.

Avahi is an AWS Premier Tier Services Partner, and through our partnership with AWS, the proof of concept that proves your Bedrock cost case can be funded. Eligible companies may receive a funded PoC depending on the project, so you can model the actual cost on your actual workload before committing to a full build.

The structure that works: pick the workload you are evaluating, define the cost target, build a scoped PoC against your real traffic shape on Bedrock with the right routing and caching architecture, measure the bill against the forecast, and decide.

Bring a representative traffic sample and your current cost baseline. We can review model choice, routing, caching, and a scoped proof of value. 

Book a Bedrock cost review with Avahi.

Make the Call With Avahi

Bedrock combines managed model access with AWS-native controls. Oversized models, uncached repeated context, idle reserved capacity, and unnecessary calls can increase costs. Measure each contribution before changing the architecture.

Use the calculator for an initial forecast, then validate the model, routing, caching, and capacity assumptions with a scoped PoC against representative traffic.

Book a Bedrock cost review for your highest-volume workload. Eligible companies may receive a funded PoC depending on your project.

FAQs: AWS Bedrock Pricing

How Much Does It Cost to Get Bedrock?

There is no fixed cost to get started with AWS Bedrock (officially Amazon Bedrock). You enable the service in your AWS account, check the access requirements for the foundation models you want to use, and pay only for what you process: per 1,000 input and output tokens for text models, per image for image generation, per second for video, plus hourly rates for any reserved Provisioned Throughput. A small prototype touching a model like Amazon Nova Lite or Claude 3.5 Haiku can run for a few dollars a day. A production application can range from hundreds to many thousands of dollars per month depending on volume and model choice.

Is AWS Bedrock Free?

Bedrock is not free in the way Amazon S3 or AWS Lambda have free tiers you can rely on for production workloads. AWS does periodically offer credits and trial allowances for specific models, and individual model providers occasionally promote evaluation periods. For production deployments, assume Bedrock is paid usage from the first call. The right way to evaluate before committing is a scoped proof of concept, which can be partially or fully AWS-funded for eligible companies.

Is Bedrock Costly?

Bedrock is not categorically expensive. It becomes expensive in three predictable ways: defaulting to the largest model for every request when most do not need it, leaving large static prompts uncached, and over-reserving Provisioned Throughput before traffic is stable. Teams that route simple requests to small models, cache repeated context, and stay on On-Demand until traffic is predictable find that Bedrock costs are reasonable for what they deliver. (Threads on AWS Bedrock pricing Reddit consistently surface the same three failure modes: oversized models, uncached prompts, and over-reserved throughput.) The architecture choices matter more than the rate card.

What Is AWS Bedrock Used For?

AWS Bedrock is a fully managed service that gives you access to foundation models from multiple providers (Anthropic Claude, Amazon Nova, Meta Llama, Mistral, Stability AI, Cohere, and others) behind one API. It is used to build generative AI applications including chatbots, RAG systems, document processing, image and video generation, agents, and content moderation, without the work of managing the underlying model infrastructure. Bedrock is the AWS-native answer to the question of how to use frontier models inside an environment that meets enterprise IAM, VPC, and audit requirements.

How Much Is Claude on Bedrock?

AWS Bedrock pricing Claude follows the standard Bedrock pattern: priced per 1,000 input and output tokens, with output typically costing 3x to 5x more than input. Claude Sonnet 4.6 is the higher-quality, higher-cost option in the current lineup. Claude 3.5 Haiku is the faster, cheaper option for most tasks. Exact rates vary by Region and change as Anthropic releases new versions, so always verify the current rate on the official AWS Bedrock pricing page before forecasting.

What Is AWS Bedrock Provisioned Throughput Pricing?

Provisioned Throughput on Bedrock reserves dedicated capacity in model units, billed hourly per unit regardless of utilization. There are three commitment tiers: no-commit (highest hourly rate, stop any time), 1-month commitment (lower rate), and 6-month commitment (lowest rate). Each model has its own model-unit rate and its own commitment-tier savings. The break-even versus On-Demand depends on sustained utilization; for predictable high-volume workloads it usually wins, for bursty or unpredictable traffic it usually does not.

AWS Bedrock Guardrails Pricing: How Much Does It Cost?

Guardrails pricing depends on the filter and API. Applicable content filters cost $0.15 per 1,000 text units for text and $0.00075 per image; InvokeGuardrailChecks uses a separate price schedule.

Include each enabled filter and both input and output evaluations in your forecast. A text unit is up to 1,000 characters, so longer content can consume multiple units.

How Does AWS Bedrock LLM Pricing Work?

Bedrock LLM pricing has four modes: On-Demand (per 1,000 input and output tokens, no commitment), Provisioned Throughput (reserved capacity by the hour), Batch Inference (asynchronous processing at roughly half the On-Demand rate), and Prompt Caching (cached input context billed at a fraction of standard rates, up to 90% off). Most production deployments combine three of these: On-Demand for live traffic, Batch for overnight work, and Prompt Caching on static prompt prefixes. Provisioned Throughput comes in when usage is large, steady, and predictable.

Can Avahi Help Optimize My AWS Bedrock Bill?

Yes. Avahi is an AWS Premier Tier Services Partner that builds and optimizes generative AI architectures on AWS, including model routing, prompt caching, batch inference, and capacity planning. A scoped proof of value can test the cost case against your workload before a larger build. Eligible companies may receive a funded PoC through our partnership with AWS, depending on the project.

Book a Bedrock cost review with Avahi.