deploybase.ai

Deploybase organizes its AI compute market data into four main sections: GPUs, LLMs, MLOps, and Research. Use the GPUs section for GPU cloud instance pricing by model, configuration, and provider; the LLMs section for inference pricing and performance benchmarks; the MLOps section for tools covering training, inference, observability, and deployment; and the Research section for provider comparison articles. The site's data is dated as updated October 21, 2026, so check that date before relying on any figure.

GPUs: GPU Cloud Prices by Provider

The GPUs section is where you compare GPU instance pricing across providers. Its main view, "GPU cloud prices by provider," plots comparable provider prices in USD per GPU-hour by GPU model, with a price axis running from $0 to $12 in the displayed chart. The page also notes counts of providers, GPU listings, and LLM endpoints, and offers an "Explore" control for paging through results.

Use this section when you need to:

• Compare the same GPU model across multiple providers on a per-GPU-hour basis.
• Filter pricing by model and configuration rather than by provider marketing pages.
• Sanity-check a quoted rate against the market range shown in the chart.

The chart's $0–$12 axis reflects the plotted comparison view, not a cap on all market prices. Deploybase's own research articles cite figures well above that range, such as 8×B200 instances at $80–100/hour on AWS and $68.80/hour on CoreWeave. Treat the chart as a comparison tool and the articles as the source for specific configurations.

LLMs: Inference Pricing and Benchmarks

The LLMs section covers LLM inference pricing and benchmarks by provider. Its main view, "LLM performance vs cost," plots output price against throughput, with output price measured in USD per 1M output tokens on a logarithmic axis (roughly $0.10 to $100 in the displayed chart) and a "Fastest measured" marker for top-throughput models.

Use this section when you need to:

• See which models deliver the most output throughput per dollar of output tokens.
• Compare hosted LLM pricing across providers on a consistent unit.
• Identify the fastest measured models before shortlisting candidates.

The chart compares output price only. If your workload is input-heavy (long prompts, retrieval contexts), confirm input pricing separately before deciding.

MLOps: Tools for Training, Inference, Observability, and Deployment

The MLOps section catalogs tools across four categories: training, inference, observability, and deployment. It is a discovery index rather than a pricing table, so use it to build a shortlist of tools for each stage of the model lifecycle, then evaluate each tool on its own documentation.

Research: Provider Comparisons and Market Articles

The Research section publishes dated comparison articles. Examples listed on the site include:

These articles are useful for configuration-specific numbers that the comparison charts don't break out, such as multi-GPU node pricing and launch timelines.

A Practical Navigation Path

1. Start with GPUs if you're choosing infrastructure: compare per-GPU-hour prices for your target model across providers.
2. Move to LLMs if you're choosing a hosted API: compare output price against throughput and note the fastest measured models.
3. Check Research for the specific provider or GPU you're considering, since articles carry exact configurations and dates.
4. Use MLOps to fill gaps in training, serving, observability, or deployment tooling.
5. Confirm the update date (October 21, 2026) and each article's date before committing to a price.

One caveat: the site presents market intelligence and benchmarks, not quotes. Prices and availability change, and the B200 articles in particular describe launch timelines (Q2–Q3 2026 for AWS), so verify current terms with the provider before budgeting.

OpenAPI-generated API documentation is reference documentation that is produced automatically from an OpenAPI description file rather than written by hand. You write (or generate) a machine-readable specification of your API — endpoints, parameters, request bodies, responses, schemas, and auth — and a documentation tool reads that file and renders a browsable, often interactive reference site. The spec becomes the single source of truth; the docs become a build artifact.

This differs from manually written docs in one fundamental way: with hand-written docs, the prose is the source of truth and the API is described separately. With spec-driven docs, the API description is the source, and every page, table, and code sample is derived from it.

How the workflow actually runs

A typical spec-driven documentation pipeline has five stages:

1. Author or generate the spec. You either write an OpenAPI document by hand (YAML or JSON), or generate it from code annotations, framework metadata, or a design-first editor. Design-first means the spec is written before implementation; code-first means it is extracted from existing code.
2. Validate and lint. The spec is checked against the OpenAPI schema and against style rules — consistent naming, required descriptions, no undocumented 4xx responses, no orphaned schemas.
3. Bundle and transform. Multi-file specs are combined, $ref pointers are resolved, and the document is optionally split into per-tag or per-version outputs.
4. Render. A documentation tool converts the spec into HTML: an endpoint list, a sidebar of operations, parameter tables, response schemas, and a "try it" console.
5. Publish and version. The rendered site is deployed, and each API version gets its own snapshot so consumers can read docs matching the version they call.

Steps 2 through 5 are usually automated in CI. If the spec fails validation, the docs build fails — which is the point.

Spec-driven vs. hand-written documentation

The practical conclusion most teams reach: generate the reference, write the guides. Reference material is repetitive and mechanical, which is exactly what generation is good at. Conceptual explanations, migration notes, and tutorials carry judgment that a spec cannot express.

What you get out of the box

Generated reference pages commonly include:

• An operation list grouped by tag or path, with HTTP method and path.
• Parameter tables showing name, location (path, query, header, cookie), type, required flag, and description.
• Request and response schemas rendered as expandable trees, including nested objects and arrays.
• Authentication details pulled from the securitySchemes section.
• Interactive request consoles that let a reader send a real call from the browser.
• Generated code samples in several languages, derived from the same schemas.
• Multiple output formats, such as a static site, a single HTML file, or a mock server.

Because all of these come from one document, changing a field name in the spec updates the parameter table, the schema tree, and every code sample at once.

Where spec-driven documentation breaks down

Generation is not free. The trade-offs are real:

Spec quality becomes documentation quality. A field with no description produces a table row with an empty cell. A vague summary produces a vague heading. Tools can enforce presence of descriptions via linting, but they cannot enforce that the description is useful.

Customization has limits. If you need a page that does not map to an OpenAPI concept — a conceptual overview, a pricing explanation, a comparison of two endpoints — you write it outside the generator and link to it.

Not everything is expressible. Webhooks, streaming responses, long-polling behavior, and complex multi-step flows are awkward or impossible to describe fully in OpenAPI. Those need prose.

The spec can go stale. If the spec is maintained separately from the implementation, it drifts just like hand-written docs. The mitigation is to generate the spec from code, or to test the implementation against the spec in CI.

Interactive consoles need care. A "try it" button that hits a production API with real credentials is a security and rate-limit problem. Point it at a sandbox, or disable it.

Deciding whether to adopt it

Adopt spec-driven reference documentation if most of these are true:

• Your API has more than a handful of endpoints, or changes frequently.
• You ship client SDKs or code samples in more than one language.
• Multiple teams consume the API and need a consistent, always-current reference.
• You already have, or are willing to maintain, an OpenAPI description.

Stay with hand-written docs, or a hybrid, if:

• Your API is small and stable, and the reference fits on one page.
• Your documentation is mostly conceptual and contains little endpoint-level detail.
• You cannot commit to keeping the spec in sync with the implementation.

A reasonable middle path: generate the reference from the spec, and hand-write the getting-started guide, authentication walkthrough, and error-handling page. Link the two directions so readers can move from concept to endpoint and back.

A minimal starting checklist

1. Produce one valid OpenAPI document for a single API version.
2. Add a linter with rules for descriptions, operation IDs, and error responses.
3. Wire the docs build into CI so a failing spec fails the build.
4. Render the reference and review it as a reader, not as the author.
5. Write the two or three conceptual pages the generator cannot produce.
6. Version the published docs alongside the API version.

The core idea is simple: describe the API once, in a format both machines and humans can read, and let the reference documentation fall out of that description. Everything else — tooling, hosting, interactivity — is a detail on top of that decision.

Deploybase is a market intelligence site for AI compute. It aggregates live GPU cloud and LLM API pricing, performance benchmarks, and market data across leading AI infrastructure providers, and organizes that data into four areas: GPUs, LLMs, MLOps tools, and research articles. It is useful when you need to compare providers on price and speed, or track how the AI compute market is moving, before committing to a cloud or API vendor.

What Deploybase covers

The site's own description frames it as "Live GPU cloud and LLM API pricing, performance benchmarks, and market data across leading AI infrastructure providers." In practice, that breaks down into these sections:

The homepage also shows counts for providers, GPU listings, and LLM endpoints, and the data is dated ("Updated October 21, 2026" on the captured page).

How you would actually use it

The site is built around comparison views rather than a single vendor's catalog. A typical workflow:

1. Pick your unit of comparison. For raw compute, compare USD per GPU-hour across providers for the same GPU model and configuration. For hosted inference, compare USD per 1M output tokens against measured throughput.
2. Read the chart, then the article. The GPU and LLM views give you the market spread; the research articles add context such as availability timelines and platform integration.
3. Check the date. Pricing and availability change quickly. The research pieces are dated (for example, March 2026 comparisons), and the B200 coverage notes availability windows like Q2–Q3 2026, so treat older figures as historical rather than current.

Expected result: a shortlist of providers whose price and performance fit your workload, backed by a stated date and source article you can revisit.

What the research articles add

The article library is where Deploybase goes beyond raw numbers. Examples from the page:

• Azure OpenAI vs Google Vertex — API pricing, speed, and latency for hosted LLM services as of March 2026.
• Azure vs AWS GPU Cloud — ND and P5 instances compared for LLM hosting, covering pricing, performance, and integration.
• Azure vs Google Cloud — A100/H100 costs, Vertex AI vs Azure ML, TPU advantages, and reserved instance pricing.
• B200 pricing across providers — AWS B200 at $80–100/hr for 8xB200 (launching Q2–Q3 2026), CoreWeave 8xB200 at $68.80/hour ($8.60 per GPU), and Lambda B200 SXM at $6.08/hour with 192GB HBM3e memory and managed support.

These are useful as worked examples of how the same hardware or API can land at very different price points depending on provider, commitment type (on-demand vs reserved), and included support.

Limits to keep in mind

• It is an aggregator, not a provider. Deploybase does not sell compute; you still sign up and pay with the individual cloud or API vendor. The site's pricing signals mention "pricing" and "premium," but the captured page does not state subscription terms or whether any content sits behind a paywall — the page shows a "Subscribe" option, so check access terms directly if you plan to rely on it regularly.
• Figures are point-in-time. Every comparison carries a date. Re-verify before budgeting, especially for GPUs with limited availability.
• Coverage is a selection, not the whole market. The charts show a set of leading providers and models; niche or regional providers may not appear.

Who it is for

Deploybase fits teams and individuals doing infrastructure due diligence: choosing between GPU clouds for training or inference, picking an LLM API on cost-per-throughput, or tracking where B200-class capacity and pricing are heading. If you already know your vendor and just need to deploy, the provider's own docs will be more direct; Deploybase is most valuable at the comparison and planning stage.

Deploybase publishes dated market-intelligence research on AI compute: side-by-side comparisons of GPU cloud pricing and LLM API cost/performance across providers, plus hardware-specific pricing and setup articles. It is a research and comparison resource, not a cloud provider itself — you use it to shortlist infrastructure, then verify current prices with the provider before committing, since the published figures carry specific dates.

What the site covers

Deploybase organizes its data around three areas, per its own navigation:

• GPUs — GPU instance pricing by model, configuration, and provider, presented as comparable per-GPU-hour prices across providers.
• LLMs — LLM inference pricing and benchmarks by provider, including a performance-vs-cost view plotting output price (USD per 1M output tokens) against throughput.
• MLOps — tools for training, inference, observability, and deployment.

The site describes itself as tracking "live GPU cloud and LLM API pricing, performance benchmarks, and market data across leading AI infrastructure providers," with a stated update date of October 21, 2026.

The kinds of comparisons it publishes

The research articles follow a consistent pattern: two or more named providers, a specific dimension (price, speed, latency, availability), and a dated snapshot. Examples from the published list:

Two things stand out about this coverage. First, it spans both cloud-platform comparisons (hyperscaler vs hyperscaler) and single-hardware deep dives (the same B200 GPU priced across AWS, CoreWeave, and Lambda). Second, the B200 articles show how much the same hardware can vary by provider and configuration — from roughly $6 per GPU-hour at Lambda to $8.60 per GPU-hour on CoreWeave's reserved clusters, with AWS's 8xB200 listing quoted at $80–100/hour for the whole instance. Those are the article's figures as published, not a live quote.

How to use it

1. Pick your dimension first. If cost is the constraint, start with the GPU price-by-provider view or the LLM output-price-vs-throughput chart. If latency matters, go to the LLM speed comparisons.
2. Read the dated article for your shortlist. Each one names the providers, the hardware or model, and the specific metrics compared.
3. Check the date before relying on a number. Every article carries a publication date (the examples above are March 2026; the site's overall data is stamped October 21, 2026). GPU and API pricing moves quickly, so treat any figure as a snapshot and confirm it on the provider's own pricing page before you budget or deploy.
4. Use the setup sections for planning, not as a runbook. The B200 articles include availability timelines and deployment notes, which help you judge readiness — but the actual provisioning steps belong to the provider's documentation.

Limits worth knowing

The published material is comparison and market data. It does not appear to offer its own compute, accounts, or billing — the site's footer lists only GPUs, LLMs, MLOps, Research, Privacy, and Terms, and the page promotes a subscription for updates. Pricing signals on the site reference "pricing" and "premium" without listing payment platforms, so whether any content sits behind a subscription is not something the available material confirms; check the site directly if access matters to you. And because every comparison is dated, the most reliable habit is to use Deploybase to narrow the field, then verify the final numbers at the source.

Deploybase lets you compare GPU cloud prices by opening its GPUs section, where instance pricing is organized by GPU model, configuration, and provider, with comparable provider prices shown in USD per GPU-hour. Use it when you want a side-by-side cost view before choosing a cloud; the site presents market data and charts rather than a single quote for your exact workload.

What the GPU section shows

The GPU page is built around one core comparison: GPU cloud prices by provider. For each GPU model, you can see listings from multiple providers and a price expressed in USD per GPU-hour. The page also includes a chart with a price axis (roughly $0 to $12 per GPU-hour in the displayed scale) and an "Explore" control for paging through additional listings.

This structure means you compare like with like: the same GPU model across providers, rather than mixing different hardware into one number.

Step-by-step comparison

1. Open the GPUs section. From the Deploybase homepage, select GPUs ("View GPU instance pricing by model, configuration, and provider").
2. Pick a GPU model. Choose the model you're evaluating so every price you see refers to the same hardware.
3. Filter by configuration. Narrow listings by configuration so you're comparing equivalent setups, not a single card against an eight-GPU node.
4. Read the per-GPU-hour price. Compare the USD per GPU-hour figures across providers in the listing and the chart.
5. Page through results. Use Explore to move beyond the first set of listings and see more providers.
6. Record your shortlist. Note provider, model, configuration, and price per GPU-hour for each candidate before moving to other checks.

Expected result: a shortlist of providers offering the same GPU model at different per-GPU-hour prices, which you can convert into an estimated cost for your planned usage.

Turning per-hour prices into a budget estimate

The listed unit is USD per GPU-hour, so estimate cost as:

Estimated cost = price per GPU-hour × number of GPUs × hours of use

For example, if you need four GPUs of one model for 100 hours, multiply the per-GPU-hour price by 4 and then by 100. Doing this for each shortlisted provider gives a comparable total rather than a per-hour figure that's hard to judge.

Reading the chart and the listings together

The chart plots provider prices against the per-GPU-hour axis, which makes outliers easy to spot: a provider sitting well above the cluster of comparable offers is visibly more expensive for the same model. The listings underneath give the specific provider and configuration behind each point. Use the chart to spot the range and the listings to confirm exactly what each price covers.

What Deploybase does not settle for you

Deploybase is described as market intelligence for AI compute — live pricing, benchmarks, and market data across providers. It is a comparison and research resource, not a quoting or purchasing tool. The page evidence does not state that prices are guaranteed, that listings are exhaustive, or that any provider is recommended. Treat the figures as a starting point and confirm current pricing, availability, and terms with the provider before committing.

Related comparisons on the site

The research section extends the same per-hour logic to specific providers and hardware, including:

• Azure OpenAI vs Google Vertex — API pricing, speed, and latency (as of March 2026)
• Azure vs AWS GPU cloud — ND vs P5 instances for LLM hosting
• Azure vs Google Cloud — A100/H100 costs and reserved instance pricing
• B200 pricing — AWS 8xB200 at $80–100/hr, CoreWeave 8xB200 at $68.80/hr ($8.60 per GPU), and Lambda B200 SXM at $6.08/hr

These articles are useful once you've narrowed the field on the GPU page and want provider-specific detail.

Common sticking points

• Comparing different configurations. A per-GPU-hour price for one card isn't comparable to a multi-GPU node price unless you normalize to the same unit.
• Ignoring the model. Prices only compare meaningfully within the same GPU model.
• Treating the chart as a quote. The displayed axis and points are market data; your actual cost depends on configuration, duration, and provider terms.
• Skipping the research articles. They add context — such as availability timelines and reserved-capacity pricing — that a single price point doesn't show.