¿Cuántos datos hacen falta?

Para clasificación tabular y previsión, unos pocos miles de ejemplos etiquetados por clase son un punto de partida razonable, y en series temporales dos ciclos estacionales completos importan más que el volumen bruto. Ajustar un modelo preentrenado para visión o texto puede funcionar con unos cientos de ejemplos bien etiquetados.

¿Cuánto cuesta un proyecto de ML?

Un estudio de viabilidad con modelo offline suele costar entre 10.000 y 35.000 $. Un sistema desplegado con pipelines, monitorización y reentrenamiento se sitúa entre 60.000 y 250.000 $, más en torno a un 20 % anual para mantener la precisión frente a la deriva de datos.

¿Cuánto se tarda en llegar a producción?

Lo habitual son de tres a cinco meses: de cuatro a seis semanas de acceso y preparación de datos, tres o cuatro semanas de modelado y el resto en integración, monitorización y la validación que el negocio necesita antes de fiarse del resultado.

¿Machine learning o un LLM?

Si la salida es un número, una clase o un ranking y tienes etiquetas históricas, el ML clásico es más barato, más rápido y más preciso. Un LLM encaja cuando la entrada es lenguaje o imágenes sin estructura y escribir reglas o etiquetas explícitas resultaría impracticable.

Machine learning 2026 — empresas y tarifas

Machine learning 2026 — empresas y tarifas | Wadline

The short answer

1,000 Machine learning companies are listed. 63% publish a minimum project budget; the median floor is $10k. Most work starts in the $3–10k range. Median published rate is $25/hr across 67 companies.

Best supplied: Rusia (420) · EE.UU. (130) · India (77)

Recalculated 2026-09-11 from live profiles.

What it costs, by country

Each cell counts companies whose published minimum project budget falls in that band. Only countries with at least 5 listed companies appear.

Country	under $3k	$3–10k	$10–20k	$20–30k	$30k+	Median rate
Rusia420Widest choice	3	82	72	54	48	$30/hr
EE.UU.130	4	21	14	13	46	$30/hr
India77	1	12	9	11	10	$18/hr
Bielorrusia61	4	19	7	7	5	$25/hr
Ucrania50	—	12	5	9	3	—
Alemania30	—	2	—	5	1	—
Países Bajos26	—	3	4	4	9	—
Polonia23	—	4	4	5	5	—
Reino Unido21	—	2	4	1	7	—
Kazajstán13	3	4	1	1	—	—
Uzbekistán11	1	4	3	—	—	—
Armenia9	—	3	2	—	—	—
Australia7	1	1	—	3	—	—
Israel5	—	2	—	—	—	—
Canadá5	1	—	—	—	2	—

How this table is built

Figures are minimum project budgets companies publish on their own profiles — the floor below which they decline work, not the price of a finished project.
USD only. Companies quote in six currencies and averaging across them produces a number nobody charges.
Budgets outside $300–$2,000,000 and rates outside $5–$500/hr are dropped as data-entry errors.
A country needs 5 listed companies for a row, and 5 published rates before a median rate is shown.

What will your budget reach?

Move the slider. Every figure below is a count of companies whose published minimum project budget sits at or under that amount.

Your budget$10k

$1k$250k

208 of 626 companies (33%) take projects at this size.

Where the entry thresholds sit

under $3k: 21
$3–10k: 187
$10–20k: 144
$20–30k: 126
$30k+: 148

Goes furthest in Rusia — 85 companies

See companies at this budget Full calculator

This says who will engage, not what your scope should cost. A budget that reaches most of the market still buys very different things depending on what you are building.

Who is focused, and who has proved it

Horizontal: how concentrated a company is on Machine learning. Vertical: how much checkable evidence stands behind it. 59 of 1000 companies are plotted — the rest have fewer than 2 evidence signals, so placing them would be guesswork.

Big Drop IncLeaders
FueledLeaders
IntellectsoftLeaders
moodleyLeaders
The Digital EmbassyLeaders
Vergani&GascoLeaders

How a company is placed

Focus is the inverse of how many categories a company claims, blended with the share of its portfolio tagged with this one. A firm listing three technologies is concentrated; a firm listing forty also appears here.
Proven delivery adds published reviews (to 20), portfolio projects (to 25), Ahrefs Domain Rating (to 60) and years listed (to 10). Each is capped so no single signal carries a company to the top.
Positions are relative to the other companies on this page: the median company sits at the centre of each axis, so a quadrant boundary means "ahead of half the companies listed here". Raw scores are in each bubble's tooltip.
Deliberately excluded: the star rating, which sits in too narrow a band to separate anyone; the editorial rank, which is a hand-assigned sort weight; and paid verification, which does not move anything on this chart.

Leaders:: Concentrated on this work and with the record to show for it.
Specialists:: This is most of what they do, but the public evidence is still thin.
Established generalists:: A substantial track record, spread across many categories.
Emerging:: Building both focus and evidence.

Empresas de desarrollo de machine learning

Before you shortlist anyone

Opiniones de clientes sobre Machine learning

ХакТайка

Asanov Agency

JanBask Digital Design

Dónde sigue siendo más rentable el machine learning

Cómo evaluar a un socio de ML

Proyectos de portfolio

Plataforma de juego y apuestas de Ajedrez - Glashh

Real Estate

TopxGun

Preguntas frecuentes

Por qué confiar en Wadline

Reseñas de clientes verificadas

Rankings basados en datos

Alcance global

Transparencia total

Explorar con IA

Categorías relacionadas

The short answer

What it costs, by country

What will your budget reach?

Where the entry thresholds sit

Who is focused, and who has proved it