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No public source establishes one provider as universally cheapest or fastest. Build a workload-specific scorecard, verify volatile figures on official pages immediately before a decision, and treat lock-in as a migration-cost estimate for the entire application stack rather than as an API-label question.
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Start with a workload you can reproduce
Write a one-page workload specification before opening a pricing calculator. Without fixed assumptions, every provider can appear to win by using a different model, region or traffic pattern.
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- Tasks and acceptance criteria: list representative prompts, expected tool calls, safety rules and what counts as an acceptable answer. Include difficult cases, not only short demonstrations.
- Model tier: identify the capability level required, the context window, modalities and structured-output features. Compare equivalent quality tiers; a cheaper, weaker model is not an apples-to-apples saving.
- Request shape: document average and maximum input tokens, expected output tokens, system-prompt size, retrieved context, tool definitions and attachment sizes.
- Traffic: specify requests per minute, bursts, concurrent users, daily schedule, retries and streaming behavior. Separate interactive traffic from offline or batch work.
- Geography: state where users, data and downstream systems are located and whether prompts or outputs must remain in a particular country, region or processing zone.
- Reliability target: define latency percentiles, throughput, error budget and recovery expectations instead of saying only “fast” or “highly available.”
Freeze the comparison variables
Choose a model, version, region, deployment type and API mode for each candidate. If a provider does not offer an equivalent option, mark the difference explicitly rather than silently substituting a different quality level. Keep prompt templates, context assembly, stop conditions and token limits identical. Record the test date because model catalogs, prices and quotas change.
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Calculate effective cost, not headline token price
Prices are attached to a particular model, region and inference option. Amazon Bedrock’s official page, for example, organizes rates by model and region and documents on-demand, batch and other inference choices: https://aws.amazon.com/bedrock/pricing/. A comparison that copies one input-token number into a spreadsheet while ignoring output tokens, caching or capacity is not a workload estimate.
Cost components to include
| Cost axis | What to capture | Why it changes the result |
|---|---|---|
| Input tokens | Uncached prompt and context tokens per request | Long system prompts and retrieval context can dominate interactive usage. |
| Output tokens | Generated tokens, including tool-related or hidden response content when billed | Two models with the same input rate can produce very different bills when answer length differs. |
| Cached tokens | Eligible prefix or prompt-cache volume, cache-hit rate and cache lifetime | Caching can lower repeated-context cost, but only if the provider’s rules and hit rate match your traffic. |
| Batch or priority processing | Discounts, surcharges, queue behavior and eligibility for offline jobs | Interactive and asynchronous workloads use different inference products and service-level expectations. |
| Provisioned capacity | Committed units, term, utilization and scaling policy | A reservation can stabilize throughput but becomes wasteful when demand is intermittent. |
| Region and data transfer | Serving region, cross-region calls, egress and network charges | The nearest model endpoint may not be the cheapest once transfer and compliance routing are included. |
| Supporting services | Retrieval indexes, storage, queues, observability, gateways, moderation and orchestration | The model line item is only part of a production request’s total cost. |
Use a transparent monthly formula
Put every assumption next to the result. A useful worksheet can express monthly spend as:
monthly cost = requests × (input tokens × input rate + output tokens × output rate + cached tokens × cache rate) + batch or priority charges + capacity commitment + supporting services + transfer
Use separate rows for interactive, batch and priority traffic. Add a low, expected and peak scenario rather than one average. Show token counts before rounding, the currency and billing period, and whether a figure is recurring or a one-time allowance. If a price is not stated for a selected model or region, write “not stated” and obtain the live value from the provider.
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Check official pages at decision time
Google Cloud publishes generative-AI pricing documentation at https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing. Use the page for the exact model, usage mode and service you selected; it does not establish a normalized Google-versus-AWS-versus-Microsoft workload price. Likewise, do not describe a generic “Bedrock price” or “Foundry price” without naming the model, region and deployment.
Separate provider charges from your own efficiency
Measure token reduction, cache hit rate, retrieval payload size and retry rate in your application. A provider with a higher list rate can still produce a lower bill if it meets the quality target with fewer output tokens or fewer retries. Conversely, a low rate can lose its advantage when a weaker model needs larger prompts, multiple attempts or extra post-processing.
Measure performance under your traffic shape
Performance is multidimensional. Microsoft’s Foundry benchmark documentation identifies time to first token and generated tokens per second as useful measures, and reports latency percentiles under defined test conditions: https://learn.microsoft.com/en-us/azure/foundry/concepts/model-benchmarks. Treat those published results as snapshots, not promises for your application.
Metrics that map to user experience
| Metric | Definition | Use it for |
|---|---|---|
| Time to first token (TTFT) | Elapsed time from request submission until the first streamed token | Perceived responsiveness in chat and interactive generation. |
| Output tokens per second | Generation rate after streaming begins | How quickly a visible answer completes. |
| End-to-end latency | Total time from request start to completion, including queueing, network and tool calls | Completion targets and timeout design. |
| Latency percentiles | p50, p95 and p99 (or the percentiles in your service objective) | Tail behavior that averages hide. |
| Sustained throughput | Successful requests or tokens per unit of time at planned concurrency | Capacity planning and cost per useful request. |
| Error and throttling rate | Timeouts, 4xx/5xx responses, rate-limit events and retries | Operational reliability and hidden latency. |
| Task quality | Human or automated score against the acceptance criteria | Whether speed is achieved without unacceptable answer degradation. |
Run a controlled benchmark
- Build a test set from production-like prompts, including short and long contexts, tool calls, multilingual cases and safety-sensitive requests where relevant.
- Pin the model version, deployment type, region, API parameters and client runtime for every provider.
- Warm the path before measurement, then run enough repetitions to capture normal and tail behavior. Keep cold-start observations separate from warmed results.
- Test sequential requests and the concurrency levels your workload actually reaches. Add a burst test to expose queueing and throttling.
- Capture request timestamps, first-token timestamps, completion timestamps, token counts, status codes, retries and quality scores in one schema.
- Repeat tests at the same times or under comparable load where practical, and report the conditions with every result.
- Re-run after material model, region, deployment or client changes. Published benchmark pages are periodically refreshed, so an old number should not be treated as current capacity.
Do not overread provider benchmarks
A benchmark using synthetic prompts, a particular prompt length, sequential requests and one region answers a narrow question. It does not predict p99 latency for a retrieval-heavy, highly concurrent application in another geography. Microsoft’s published methodology is useful because it states its conditions; reproduce your own prompt lengths, concurrency, context assembly and acceptance criteria before choosing.
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Compare geography, deployment and guarantees separately
A cloud brand does not by itself identify where prompts are processed or what throughput is guaranteed. Microsoft’s deployment documentation distinguishes global and data-zone processing scopes, notes that model support varies by deployment type, and explains that provisioned deployments provide guaranteed throughput with lower latency variance while standard deployments are best-effort: https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/deployment-types.
Questions to answer for each candidate
- Which named region or processing boundary handles prompts, outputs, logs and abuse-monitoring data?
- Can traffic fail over to another region, and would that violate your data-residency requirement?
- Is the selected model available in the required geography and deployment type?
- Is throughput best-effort, quota-limited or backed by a provisioned commitment?
- What service-level objective or support commitment applies to the exact API and tier?
- What network path connects your users, application, retrieval system and model endpoint?
Keep a separate geography-and-guarantee column in the scorecard. Do not award a performance win to an endpoint that cannot legally or operationally serve the target users.
Treat lock-in as the cost of replacing the whole stack
Lock-in is not answered by whether two services expose a superficially similar chat-completions endpoint. Estimate the people, testing, data movement and downtime needed to replace provider-specific behavior.
| Layer | Inventory | Migration question |
|---|---|---|
| API and response contract | Authentication, streaming events, structured output, tool schemas, error codes and retry headers | Can an adapter preserve your application contract, or must every client change? |
| Model behavior | Tokenization, context limits, safety filters, system-message handling, tool-calling quirks and quality profile | How many prompts, validators and evaluations must be retuned? |
| Version lifecycle | Aliases, deprecation dates, automatic upgrades and pinning options | Can you test a replacement before the current model disappears? |
| Retrieval and data | Vector indexes, embedding model, chunking metadata, object storage and export formats | Can data and embeddings be exported without a full re-index? |
| Orchestration | Managed agents, prompt registries, routing, guardrails and evaluation pipelines | Which workflows are expressed in proprietary configuration? |
| Identity and security | Roles, private networking, key management, secrets and policy bindings | How much infrastructure must be recreated in the destination cloud? |
| Observability | Trace IDs, token accounting, logs, dashboards, alerts and retention rules | Can historical and real-time telemetry move with the service? |
| Commercial and legal terms | Model licenses, usage restrictions, data-processing terms and committed spend | Are you allowed to move outputs, weights, prompts and fine-tuning data? |
Understand who controls model terms
For documented serverless model deployments, Microsoft hosts models in Azure-managed infrastructure and exposes the API, while the model provider sets license terms and prices. Microsoft states: “Model providers define the license terms and set the price for use of their models.” Read the full overview at https://learn.microsoft.com/en-us/azure/foundry/concepts/foundry-models-overview. This separation matters when a price, usage right or model version is controlled by a party other than the hosting cloud.
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Reduce avoidable switching cost
- Put a small internal adapter in front of provider APIs and normalize streaming, usage and error events.
- Store prompts, tool schemas, model identifiers and generation settings in version control.
- Keep retrieval documents, metadata, embeddings and evaluation sets in exportable formats.
- Maintain a provider-neutral test suite that checks quality, safety, latency and cost before traffic moves.
- Pin versions where possible and monitor deprecation notices instead of relying on aliases.
- Document every managed feature used in production and assign an owner for its replacement.
These practices do not make providers identical. They make the remaining differences visible and let you price migration work instead of discovering it during an outage or forced upgrade.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use provider documentation without pretending it is a cross-provider benchmark
Amazon Bedrock
Use the Bedrock pricing page to retrieve the current rate for the exact model, region and inference option, then add batch, on-demand, caching, provisioned capacity and related-service assumptions. The page is evidence of available pricing structures, not proof that Bedrock is cheapest for your workload: https://aws.amazon.com/bedrock/pricing/.
Microsoft Foundry
Use the benchmark documentation for metric definitions and methodology, then reproduce the test with your own prompts and concurrency. Use the deployment-types documentation to distinguish global, data-zone and other scopes, and standard versus provisioned guarantees. A benchmark result without its region, model, deployment and request pattern is incomplete.
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Google Cloud generative AI services
Consult the live pricing documentation for the selected model and usage mode: https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing. The available evidence does not provide an apples-to-apples normalized bill against AWS or Microsoft, so calculate one from the same workload worksheet rather than ranking providers from isolated list prices.
Build a decision scorecard
Turn measurements into a decision that reflects business priorities. Keep raw observations alongside the score so a weighted total cannot hide a compliance failure.
| Category | Suggested evidence | Decision rule |
|---|---|---|
| Effective cost | Low, expected and peak monthly scenarios with all token and service assumptions | Reject options that exceed the approved budget at expected or peak volume. |
| Performance | TTFT, output rate, p50/p95/p99 latency, throughput, errors and quality on the same test set | Require every service objective, not merely the best average. |
| Geography and guarantees | Processing boundary, model availability, failover behavior and throughput commitment | Treat a residency or reliability failure as disqualifying. |
| Portability | Inventory of proprietary APIs, managed data and orchestration, plus estimated replacement hours | Include migration cost in total cost of ownership and require an exit plan. |
| Operational fit | Existing identity, networking, monitoring, procurement and staff capability | Record organizational advantages as constraints, not as undocumented performance claims. |
Weight only after applying hard gates
First remove candidates that fail legal, geography, quality or reliability requirements. Then assign weights—for example, cost, latency, quality, operational fit and portability—based on the application’s priorities. Preserve the unweighted measurements and explain every weight. Recalculate when traffic, model version or regional requirements change; a single score is a decision aid, not a permanent ranking.
A repeatable comparison procedure
- Define the workload: freeze prompts, context sizes, token mix, traffic shape, quality tests, region and reliability objectives.
- Select equivalent candidates: name the model or quality tier, version, deployment type and API mode for each provider.
- Collect live commercial inputs: retrieve model- and region-specific rates and document cache, batch, priority, capacity, transfer and supporting-service assumptions.
- Build the cost scenarios: calculate monthly low, expected and peak usage, separating interactive and offline traffic.
- Implement one harness: send identical requests, capture TTFT, output rate, end-to-end latency percentiles, throughput, errors, retries, token counts and quality scores.
- Run controlled tests: include warm, sequential, realistic-concurrency and burst conditions; publish the exact test conditions with results.
- Audit the complete stack: inventory API behavior, model lifecycle, retrieval, orchestration, identity, observability, data boundaries and license terms.
- Apply hard gates and weights: reject noncompliant options, then score the survivors with migration cost and operational fit included.
- Validate the exit plan: export representative data, run the provider-neutral evaluation suite against a second option and estimate the people and calendar time required to switch.
- Set a review trigger: repeat pricing and performance checks after material model, deployment, traffic or contract changes.
Common comparison mistakes and their fixes
- Comparing different models: match capability and quality acceptance criteria before comparing rates.
- Using one average latency: report percentiles and separate queue, network, first-token and generation time.
- Testing only sequential requests: add the concurrency and bursts your application creates.
- Ignoring output tokens: measure generated length and retries; output often changes the bill materially.
- Assuming an API label means portability: test streaming, tools, structured output, errors and safety behavior through an adapter.
- Assuming a cloud region guarantees residency: verify the exact deployment scope and data handling for prompts, outputs and logs.
- Counting only model spend: include retrieval, storage, networking, monitoring, gateways and human operations.
- Treating provider benchmarks as guarantees: reproduce the workload and publish conditions, date, model, region and deployment.
- Leaving migration until procurement: maintain exports, evaluations and an inventory of provider-specific features from the first production release.
What a defensible conclusion looks like
The winning provider is the one that satisfies the defined quality, geography and reliability gates at an acceptable total cost while delivering measured performance for the actual traffic shape and a tolerable exit cost. It may not have the lowest published token rate or the best benchmark number. State the model, version, region, deployment, test date, prompt and concurrency conditions, cost assumptions and known uncertainties so another team can reproduce the decision.
Quick Recap
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