avesapi.com

avesapi.com is a SERP (search engine results page) API built for rank trackers, SEO platforms, and developers. It scrapes search engines at scale and returns real-time results as either raw HTML or structured JSON — without requiring you to manage proxies or solve captchas. It fits teams that need live search data piped into an existing SEO tool or rank-tracking workflow, rather than a one-off manual lookup.

What the API actually returns

The service gives you two output formats, and which one you pick depends on whether you already have a parser.

• Real-Time HTML Results — You send a query with a device and location, and get back precise HTML from the search engine. If you already run your own parser, you can drop this HTML straight into your existing pipeline.
• Seamless JSON Data — If you don't have a parser, the API returns structured JSON covering videos, images, maps, and answer boxes. This is the faster path to "actionable insights" if you'd rather not build parsing logic yourself.

Both formats are delivered in real time, and the results include ads, organic results, and other page features in one response.

How it handles scale and targeting

The core pitch is scraping search engines at scale without the usual friction:

• No proxies, no captchas — the infrastructure handles that layer, so you don't maintain proxy pools or captcha-solving.
• Geo-targeted results — you can make location-specific requests using UULE, country, and/or city parameters when you need local rankings.
• Concurrent requests — the platform is designed for parallel requests, which matters when you're tracking thousands of keywords.
• High accuracy and success rate — stated as product claims; verify against your own keyword set before committing.

Pricing and plans

Pricing is pay-per-request, and the site states there is no monthly recurring subscription — you pay only on successful requests. Each request returns a real-time result and costs 1 credit. The published tiers are:

All tiers include GEO-targeted search, JSON or HTML export, and always return live results. The site notes it currently does not support priorities. For larger volumes, it points to a separate pricing page.

Who it's for

• Rank trackers — need scheduled, geo-specific SERP snapshots at volume.
• SEO platforms — need to embed live SERP data into a product without building scraping infrastructure.
• Developers — want a clean JSON or HTML feed they can integrate into an existing system.

If you only need occasional manual SERP checks, a full API is likely overkill. If you're running continuous rank tracking or building an SEO tool that depends on fresh search data, the pay-per-request model and dual output formats are the main things to evaluate.

A practical way to test it

Start with the free tier to confirm two things before paying: that the JSON schema matches what your system expects, and that geo-targeted results are accurate for the locations you track. Run a sample of your real keywords through both HTML and JSON endpoints, compare the parsed output against a manual search, and check the success rate on your specific query types. That tells you more than any benchmark page will.

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.

A SERP API such as avesapi.com sits between your rank tracker or SEO platform and a search engine: you send a query with device and location parameters, and it returns the live results page as parsed HTML or structured JSON. That removes the two hardest parts of building a rank tracker yourself — proxy rotation and captcha handling — because the provider absorbs them. It fits teams building or scaling SEO tools; it is not a ready-made dashboard for someone who just wants to check a few keywords by hand.

What the API actually does

The service describes itself as a SERP API for rank trackers and SEO platforms, with the promise of scraping search engines at scale "no proxies, no captchas, just clean data in seconds." In practice, one request does the following:

• Takes a query plus targeting inputs. You specify the query, the device, and the location. Location targeting is done through UULE, country, and/or city parameters.
• Fetches the live search engine result page. Every request returns a real-time result rather than a cached or scheduled one.
• Returns the full result set. The response covers ads, organic results, and other elements such as videos, images, maps, and answer boxes.
• Delivers it in your preferred format. You can take raw HTML if you already have a parser, or structured JSON if you don't.

The "no proxies, no captchas" point is the core value proposition. Those are the components that make DIY SERP scraping expensive to maintain: proxy pools need constant replacement, and anti-bot challenges break pipelines without warning. Delegating them to the API means your engineering effort goes into ranking logic and reporting instead.

Why rank tracking needs this layer

A rank tracker's job is to answer "where does this URL appear for this keyword, for this audience?" That requires repeated, location-specific, device-specific queries at a volume no manual process can sustain.

Two design choices in the API map directly onto that need:

Geo-specific results. A keyword can rank differently in different cities and countries. Because the API accepts UULE, country, and city parameters, you can track a single keyword across multiple markets and store each result separately — which is what agencies reporting on local visibility need.

Real-time results only. The provider states it currently doesn't support priorities and that each request returns a real-time result. For rank tracking this is usually desirable: you want the position as of the moment you checked, not a stale snapshot. The trade-off is that you cannot schedule a cheaper batch job for later.

HTML vs. JSON: which output to choose

The two export formats serve different starting points.

A practical pattern is to use JSON for the ranking pipeline and keep HTML only where you need to re-verify a result after a parser change.

Pricing model and what it means for planning

The pricing page lists a pay-per-request model rather than a recurring subscription. The published tiers are:

All tiers include GEO-targeted search, JSON or HTML export, and always-live results. The provider states there are no monthly recurring charges and that you only pay upon successful requests — so failed requests are not billed, which matters when you are estimating cost per tracked keyword.

To size a plan, multiply keywords by locations by check frequency. For example, 500 keywords tracked daily across 5 cities is 2,500 requests per day, or roughly 75,000 per month — which lands between Premium and Professional. The site notes that larger volumes should be checked against the pricing page.

What you still have to build yourself

The API returns search results; it does not produce rankings. Your application still needs to:

1. Store the keyword/location/device matrix you want to track.
2. Match returned results against your target URLs to derive a position.
3. Handle result-set changes — featured snippets, answer boxes, and map packs shift what "position 1" means.
4. Schedule and retry requests, and log failures.
5. Turn positions into the reports your users or clients actually read.

That division of labor is the point: the API handles acquisition, you handle interpretation.

Getting started

The site offers a free test tier of 1,000 requests and a "Try Free Now" entry point, plus documentation and a login/register flow. A sensible first step is to run a small set of your real keywords across two or three locations in both JSON and HTML, confirm the fields you need are present, and only then commit to a paid tier.

Yes. avesapi.com supports geo-targeted search results, and you can request them by location rather than receiving a single generic result set. According to the site, you can make geo-specific requests using UULE, country, and/or city parameters, and the API returns results for the query, device, and location you specify. Geo-targeted search is also listed as a feature on every plan tier, including the free test tier.

This matters if you run local rank tracking, check localized SERPs, or build SEO tools that need to report what a user in a specific market actually sees.

How Geo-Targeting Works on avesapi.com

The mechanism is parameter-based. Instead of sending only a query, you send the query plus location signals, and the API returns results reflecting that location.

The site also states that results reflect the specified location and device, so device targeting (for example, mobile vs. desktop) is part of the same request context. If you need mobile local results for a specific city, you set both the location and the device rather than assuming the API infers them.

Why This Matters for Rank Tracking and SEO Tools

Search results differ by location. A keyword that ranks in one city may rank differently in another, and local intent queries often surface a local pack, map results, or different organic ordering depending on where the request originates.

Geo-targeted requests let you:

• Track rankings per city or country instead of a single blended position
• Verify whether a page appears in local results for a target market
• Feed location-specific SERP data into dashboards or client reports
• Compare how the same query resolves across markets

For rank trackers, this is usually the difference between a number that looks right and a number that matches what a real user in that market sees.

What You Get Back

The API returns results in two formats:

• HTML — precise HTML results for your query, device, and location. Useful if you already have a parser and want to run your own extraction.
• JSON — structured data including videos, images, maps, and answer boxes, plus ads and organic results. Useful if you don't want to build a parser.

Geo-targeting applies to both output formats, so you can choose the format based on your existing pipeline rather than on location support.

Availability Across Plans

Geo-targeted search is listed as an included feature on every tier shown on the pricing page:

Two details worth noting from the pricing section:

• Each request returns a real-time result and costs 1 credit, regardless of plan.
• The model is pay-per-request, not a recurring subscription. The site states you only pay upon successful requests.

So geo-targeting is not gated behind a higher tier — the difference between plans is request volume, not location capability.

Practical Notes Before You Build

• Location precision depends on the parameter you use. Country-level targeting is coarser than city-level, and UULE is the option to reach for when you need a specific market that a city name alone doesn't capture cleanly.
• Device is part of the request context. If your rank tracking reports mobile positions, set the device explicitly rather than relying on a default.
• There is no priority tier. The site states priorities are not currently supported and every request returns a live result, so plan concurrency around that rather than around request priority.
• Volume planning is straightforward. Since each request costs one credit, estimate credits as (keywords × locations × devices × check frequency) and match that against the plan sizes above.

If your goal is local rank tracking or localized SEO reporting, geo-targeted requests are supported and available from the free test tier upward. The main decision is not whether location targeting exists, but how precisely you need to specify it — country, city, or UULE — and how many location-and-device combinations you plan to check on a recurring basis.

avesapi.com uses a pay-per-request model rather than a recurring subscription. Every request returns a live result and costs 1 credit, so the plan you choose is simply a prepaid block of requests. Based on the published pricing, there are four tiers: a free test tier with 1K requests, Starter at $50 for 25K requests, Premium at $125 for 100K requests, and Professional at $500 for 500K requests. All tiers include GEO-targeted search and JSON or HTML export. If your volume exceeds the Professional tier, the site directs you to its pricing page for larger search limits.

The pricing model in plain terms

The key mechanic is that you are not buying a monthly subscription — you are buying a pool of requests. The site states it uses pay-per-request pricing and that you "only pay upon successful requests," which means failed requests are not billed. There is no monthly recurrence, so credits do not expire on a subscription clock in the way a SaaS plan would.

Two consequences follow from this:

• Cost scales linearly with volume. At the published tiers, the effective cost per request drops as you buy larger blocks (see the table below).
• You are not locked into a renewal. If you run a burst of rank-tracking jobs and then go quiet, you are not paying for idle months.

The site also notes it "currently doesn't support priorities" — every request is treated the same and returns a real-time result. There is no premium queue to buy into.

Plan comparison

All four published tiers list the same feature set: GEO-targeted search, JSON or HTML export, and always-live results. The only variable is request volume, so the decision is purely about how many requests you will consume.

Estimating your request volume

Because pricing is per request, the practical question is how many requests your workflow generates. A few common patterns:

• Rank tracking: one request per keyword per location per device per check. Tracking 500 keywords daily across one country and one device is roughly 500 requests/day, or about 15K/month — comfortably inside Starter.
• Multi-location tracking: the same 500 keywords across 10 cities is 5,000 requests/day, or ~150K/month — that pushes you into Premium.
• SEO tool with end users: if each user triggers searches on demand, volume can spike unpredictably. The pay-per-request model handles this without a plan upgrade, but you should size your credit purchase to your peak month, not your average.

The site supports geo-specific requests via UULE, country, and/or city parameters, so each location variant counts as its own request. Factor that multiplier in before picking a tier.

Choosing between tiers

• Pick Free Test first if you have not yet validated that the JSON or HTML output fits your pipeline. It costs nothing and answers the format question directly.
• Pick Starter if your monthly volume is under ~25K requests — typical for a single-site rank tracker or an early-stage tool.
• Pick Premium if you track multiple locations or run a tool with a modest active user base, landing in the 25K–100K range.
• Pick Professional if you are running a production SEO platform with large keyword sets or frequent refresh intervals, up to 500K requests.
• Ask for custom pricing if you expect to exceed 500K requests, since the published tiers stop there.

What the pricing does not tell you

The published page does not specify credit expiry, refund terms, or whether unused requests roll over. It also does not list payment platforms. Before buying a large block, confirm these details directly — particularly expiry, since a 500K-request purchase is only economical if you can consume it within whatever window applies.

The site does state that you only pay for successful requests, which is the most useful cost-control guarantee on the page: if a scrape fails, it should not consume a credit.