<?php
/**
 * Meta API: WP_Meta_Query class
 *
 * @package WordPress
 * @subpackage Meta
 * @since 4.4.0
 */

/**
 * Core class used to implement meta queries for the Meta API.
 *
 * Used for generating SQL clauses that filter a primary query according to metadata keys and values.
 *
 * WP_Meta_Query is a helper that allows primary query classes, such as WP_Query and WP_User_Query,
 *
 * to filter their results by object metadata, by generating `JOIN` and `WHERE` subclauses to be attached
 * to the primary SQL query string.
 *
 * @since 3.2.0
 */
#[AllowDynamicProperties]
class WP_Meta_Query {
	/**
	 * Array of metadata queries.
	 *
	 * See WP_Meta_Query::__construct() for information on meta query arguments.
	 *
	 * @since 3.2.0
	 * @var array
	 */
	public $queries = array();

	/**
	 * The relation between the queries. Can be one of 'AND' or 'OR'.
	 *
	 * @since 3.2.0
	 * @var string
	 */
	public $relation;

	/**
	 * Database table to query for the metadata.
	 *
	 * @since 4.1.0
	 * @var string
	 */
	public $meta_table;

	/**
	 * Column in meta_table that represents the ID of the object the metadata belongs to.
	 *
	 * @since 4.1.0
	 * @var string
	 */
	public $meta_id_column;

	/**
	 * Database table that where the metadata's objects are stored (eg $wpdb->users).
	 *
	 * @since 4.1.0
	 * @var string
	 */
	public $primary_table;

	/**
	 * Column in primary_table that represents the ID of the object.
	 *
	 * @since 4.1.0
	 * @var string
	 */
	public $primary_id_column;

	/**
	 * A flat list of table aliases used in JOIN clauses.
	 *
	 * @since 4.1.0
	 * @var array
	 */
	protected $table_aliases = array();

	/**
	 * A flat list of clauses, keyed by clause 'name'.
	 *
	 * @since 4.2.0
	 * @var array
	 */
	protected $clauses = array();

	/**
	 * Whether the query contains any OR relations.
	 *
	 * @since 4.3.0
	 * @var bool
	 */
	protected $has_or_relation = false;

	/**
	 * Constructor.
	 *
	 * @since 3.2.0
	 * @since 4.2.0 Introduced support for naming query clauses by associative array keys.
	 * @since 5.1.0 Introduced `$compare_key` clause parameter, which enables LIKE key matches.
	 * @since 5.3.0 Increased the number of operators available to `$compare_key`. Introduced `$type_key`,
	 *              which enables the `$key` to be cast to a new data type for comparisons.
	 *
	 * @param array $meta_query {
	 *     Array of meta query clauses. When first-order clauses or sub-clauses use strings as
	 *     their array keys, they may be referenced in the 'orderby' parameter of the parent query.
	 *
	 *     @type string $relation Optional. The MySQL keyword used to join the clauses of the query.
	 *                            Accepts 'AND' or 'OR'. Default 'AND'.
	 *     @type array  ...$0 {
	 *         Optional. An array of first-order clause parameters, or another fully-formed meta query.
	 *
	 *         @type string|string[] $key         Meta key or keys to filter by.
	 *         @type string          $compare_key MySQL operator used for comparing the $key. Accepts:
	 *                                            - '='
	 *                                            - '!='
	 *                                            - 'LIKE'
	 *                                            - 'NOT LIKE'
	 *                                            - 'IN'
	 *                                            - 'NOT IN'
	 *                                            - 'REGEXP'
	 *                                            - 'NOT REGEXP'
	 *                                            - 'RLIKE'
	 *                                            - 'EXISTS' (alias of '=')
	 *                                            - 'NOT EXISTS' (alias of '!=')
	 *                                            Default is 'IN' when `$key` is an array, '=' otherwise.
	 *         @type string          $type_key    MySQL data type that the meta_key column will be CAST to for
	 *                                            comparisons. Accepts 'BINARY' for case-sensitive regular expression
	 *                                            comparisons. Default is ''.
	 *         @type string|string[] $value       Meta value or values to filter by.
	 *         @type string          $compare     MySQL operator used for comparing the $value. Accepts:
	 *                                            - '='
	 *                                            - '!='
	 *                                            - '>'
	 *                                            - '>='
	 *                                            - '<'
	 *                                            - '<='
	 *                                            - 'LIKE'
	 *                                            - 'NOT LIKE'
	 *                                            - 'IN'
	 *                                            - 'NOT IN'
	 *                                            - 'BETWEEN'
	 *                                            - 'NOT BETWEEN'
	 *                                            - 'REGEXP'
	 *                                            - 'NOT REGEXP'
	 *                                            - 'RLIKE'
	 *                                            - 'EXISTS'
	 *                                            - 'NOT EXISTS'
	 *                                            Default is 'IN' when `$value` is an array, '=' otherwise.
	 *         @type string          $type        MySQL data type that the meta_value column will be CAST to for
	 *                                            comparisons. Accepts:
	 *                                            - 'NUMERIC'
	 *                                            - 'BINARY'
	 *                                            - 'CHAR'
	 *                                            - 'DATE'
	 *                                            - 'DATETIME'
	 *                                            - 'DECIMAL'
	 *                                            - 'SIGNED'
	 *                                            - 'TIME'
	 *                                            - 'UNSIGNED'
	 *                                            Default is 'CHAR'.
	 *     }
	 * }
	 */
	public function __construct( $meta_query = false ) {
		if ( ! $meta_query ) {
			return;
		}

		if ( isset( $meta_query['relation'] ) && 'OR' === strtoupper( $meta_query['relation'] ) ) {
			$this->relation = 'OR';
		} else {
			$this->relation = 'AND';
		}

		$this->queries = $this->sanitize_query( $meta_query );
	}

	/**
	 * Ensures the 'meta_query' argument passed to the class constructor is well-formed.
	 *
	 * Eliminates empty items and ensures that a 'relation' is set.
	 *
	 * @since 4.1.0
	 *
	 * @param array $queries Array of query clauses.
	 * @return array Sanitized array of query clauses.
	 */
	public function sanitize_query( $queries ) {
		$clean_queries = array();

		if ( ! is_array( $queries ) ) {
			return $clean_queries;
		}

		foreach ( $queries as $key => $query ) {
			if ( 'relation' === $key ) {
				$relation = $query;

			} elseif ( ! is_array( $query ) ) {
				continue;

				// First-order clause.
			} elseif ( $this->is_first_order_clause( $query ) ) {
				if ( isset( $query['value'] ) && array() === $query['value'] ) {
					unset( $query['value'] );
				}

				$clean_queries[ $key ] = $query;

				// Otherwise, it's a nested query, so we recurse.
			} else {
				$cleaned_query = $this->sanitize_query( $query );

				if ( ! empty( $cleaned_query ) ) {
					$clean_queries[ $key ] = $cleaned_query;
				}
			}
		}

		if ( empty( $clean_queries ) ) {
			return $clean_queries;
		}

		// Sanitize the 'relation' key provided in the query.
		if ( isset( $relation ) && 'OR' === strtoupper( $relation ) ) {
			$clean_queries['relation'] = 'OR';
			$this->has_or_relation     = true;

			/*
			* If there is only a single clause, call the relation 'OR'.
			* This value will not actually be used to join clauses, but it
			* simplifies the logic around combining key-only queries.
			*/
		} elseif ( 1 === count( $clean_queries ) ) {
			$clean_queries['relation'] = 'OR';

			// Default to AND.
		} else {
			$clean_queries['relation'] = 'AND';
		}

		return $clean_queries;
	}

	/**
	 * Determines whether a query clause is first-order.
	 *
	 * A first-order meta query clause is one that has either a 'key' or
	 * a 'value' array key.
	 *
	 * @since 4.1.0
	 *
	 * @param array $query Meta query arguments.
	 * @return bool Whether the query clause is a first-order clause.
	 */
	protected function is_first_order_clause( $query ) {
		return isset( $query['key'] ) || isset( $query['value'] );
	}

	/**
	 * Constructs a meta query based on 'meta_*' query vars
	 *
	 * @since 3.2.0
	 *
	 * @param array $qv The query variables.
	 */
	public function parse_query_vars( $qv ) {
		$meta_query = array();

		/*
		 * For orderby=meta_value to work correctly, simple query needs to be
		 * first (so that its table join is against an unaliased meta table) and
		 * needs to be its own clause (so it doesn't interfere with the logic of
		 * the rest of the meta_query).
		 */
		$primary_meta_query = array();
		foreach ( array( 'key', 'compare', 'type', 'compare_key', 'type_key' ) as $key ) {
			if ( ! empty( $qv[ "meta_$key" ] ) ) {
				$primary_meta_query[ $key ] = $qv[ "meta_$key" ];
			}
		}

		// WP_Query sets 'meta_value' = '' by default.
		if ( isset( $qv['meta_value'] ) && '' !== $qv['meta_value'] && ( ! is_array( $qv['meta_value'] ) || $qv['meta_value'] ) ) {
			$primary_meta_query['value'] = $qv['meta_value'];
		}

		$existing_meta_query = isset( $qv['meta_query'] ) && is_array( $qv['meta_query'] ) ? $qv['meta_query'] : array();

		if ( ! empty( $primary_meta_query ) && ! empty( $existing_meta_query ) ) {
			$meta_query = array(
				'relation' => 'AND',
				$primary_meta_query,
				$existing_meta_query,
			);
		} elseif ( ! empty( $primary_meta_query ) ) {
			$meta_query = array(
				$primary_meta_query,
			);
		} elseif ( ! empty( $existing_meta_query ) ) {
			$meta_query = $existing_meta_query;
		}

		$this->__construct( $meta_query );
	}

	/**
	 * Returns the appropriate alias for the given meta type if applicable.
	 *
	 * @since 3.7.0
	 *
	 * @param string $type MySQL type to cast meta_value.
	 * @return string MySQL type.
	 */
	public function get_cast_for_type( $type = '' ) {
		if ( empty( $type ) ) {
			return 'CHAR';
		}

		$meta_type = strtoupper( $type );

		if ( ! preg_match( '/^(?:BINARY|CHAR|DATE|DATETIME|SIGNED|UNSIGNED|TIME|NUMERIC(?:\(\d+(?:,\s?\d+)?\))?|DECIMAL(?:\(\d+(?:,\s?\d+)?\))?)$/', $meta_type ) ) {
			return 'CHAR';
		}

		if ( 'NUMERIC' === $meta_type ) {
			$meta_type = 'SIGNED';
		}

		return $meta_type;
	}

	/**
	 * Generates SQL clauses to be appended to a main query.
	 *
	 * @since 3.2.0
	 *
	 * @param string $type              Type of meta. Possible values include but are not limited
	 *                                  to 'post', 'comment', 'blog', 'term', and 'user'.
	 * @param string $primary_table     Database table where the object being filtered is stored (eg wp_users).
	 * @param string $primary_id_column ID column for the filtered object in $primary_table.
	 * @param object $context           Optional. The main query object that corresponds to the type, for
	 *                                  example a `WP_Query`, `WP_User_Query`, or `WP_Site_Query`.
	 *                                  Default null.
	 * @return string[]|false {
	 *     Array containing JOIN and WHERE SQL clauses to append to the main query,
	 *     or false if no table exists for the requested meta type.
	 *
	 *     @type string $join  SQL fragment to append to the main JOIN clause.
	 *     @type string $where SQL fragment to append to the main WHERE clause.
	 * }
	 */
	public function get_sql( $type, $primary_table, $primary_id_column, $context = null ) {
		$meta_table = _get_meta_table( $type );
		if ( ! $meta_table ) {
			return false;
		}

		$this->table_aliases = array();

		$this->meta_table     = $meta_table;
		$this->meta_id_column = sanitize_key( $type . '_id' );

		$this->primary_table     = $primary_table;
		$this->primary_id_column = $primary_id_column;

		$sql = $this->get_sql_clauses();

		/*
		 * If any JOINs are LEFT JOINs (as in the case of NOT EXISTS), then all JOINs should
		 * be LEFT. Otherwise posts with no metadata will be excluded from results.
		 */
		if ( str_contains( $sql['join'], 'LEFT JOIN' ) ) {
			$sql['join'] = str_replace( 'INNER JOIN', 'LEFT JOIN', $sql['join'] );
		}

		/**
		 * Filters the meta query's generated SQL.
		 *
		 * @since 3.1.0
		 *
		 * @param string[] $sql               Array containing the query's JOIN and WHERE clauses.
		 * @param array    $queries           Array of meta queries.
		 * @param string   $type              Type of meta. Possible values include but are not limited
		 *                                    to 'post', 'comment', 'blog', 'term', and 'user'.
		 * @param string   $primary_table     Primary table.
		 * @param string   $primary_id_column Primary column ID.
		 * @param object   $context           The main query object that corresponds to the type, for
		 *                                    example a `WP_Query`, `WP_User_Query`, or `WP_Site_Query`.
		 */
		return apply_filters_ref_array( 'get_meta_sql', array( $sql, $this->queries, $type, $primary_table, $primary_id_column, $context ) );
	}

	/**
	 * Generates SQL clauses to be appended to a main query.
	 *
	 * Called by the public WP_Meta_Query::get_sql(), this method is abstracted
	 * out to maintain parity with the other Query classes.
	 *
	 * @since 4.1.0
	 *
	 * @return string[] {
	 *     Array containing JOIN and WHERE SQL clauses to append to the main query.
	 *
	 *     @type string $join  SQL fragment to append to the main JOIN clause.
	 *     @type string $where SQL fragment to append to the main WHERE clause.
	 * }
	 */
	protected function get_sql_clauses() {
		/*
		 * $queries are passed by reference to get_sql_for_query() for recursion.
		 * To keep $this->queries unaltered, pass a copy.
		 */
		$queries = $this->queries;
		$sql     = $this->get_sql_for_query( $queries );

		if ( ! empty( $sql['where'] ) ) {
			$sql['where'] = ' AND ' . $sql['where'];
		}

		return $sql;
	}

	/**
	 * Generates SQL clauses for a single query array.
	 *
	 * If nested subqueries are found, this method recurses the tree to
	 * produce the properly nested SQL.
	 *
	 * @since 4.1.0
	 *
	 * @param array $query Query to parse (passed by reference).
	 * @param int   $depth Optional. Number of tree levels deep we currently are.
	 *                     Used to calculate indentation. Default 0.
	 * @return string[] {
	 *     Array containing JOIN and WHERE SQL clauses to append to a single query array.
	 *
	 *     @type string $join  SQL fragment to append to the main JOIN clause.
	 *     @type string $where SQL fragment to append to the main WHERE clause.
	 * }
	 */
	protected function get_sql_for_query( &$query, $depth = 0 ) {
		$sql_chunks = array(
			'join'  => array(),
			'where' => array(),
		);

		$sql = array(
			'join'  => '',
			'where' => '',
		);

		$indent = '';
		for ( $i = 0; $i < $depth; $i++ ) {
			$indent .= '  ';
		}

		foreach ( $query as $key => &$clause ) {
			if ( 'relation' === $key ) {
				$relation = $query['relation'];
			} elseif ( is_array( $clause ) ) {

				// This is a first-order clause.
				if ( $this->is_first_order_clause( $clause ) ) {
					$clause_sql = $this->get_sql_for_clause( $clause, $query, $key );

					$where_count = count( $clause_sql['where'] );
					if ( ! $where_count ) {
						$sql_chunks['where'][] = '';
					} elseif ( 1 === $where_count ) {
						$sql_chunks['where'][] = $clause_sql['where'][0];
					} else {
						$sql_chunks['where'][] = '( ' . implode( ' AND ', $clause_sql['where'] ) . ' )';
					}

					$sql_chunks['join'] = array_merge( $sql_chunks['join'], $clause_sql['join'] );
					// This is a subquery, so we recurse.
				} else {
					$clause_sql = $this->get_sql_for_query( $clause, $depth + 1 );

					$sql_chunks['where'][] = $clause_sql['where'];
					$sql_chunks['join'][]  = $clause_sql['join'];
				}
			}
		}

		// Filter to remove empties.
		$sql_chunks['join']  = array_filter( $sql_chunks['join'] );
		$sql_chunks['where'] = array_filter( $sql_chunks['where'] );

		if ( empty( $relation ) ) {
			$relation = 'AND';
		}

		// Filter duplicate JOIN clauses and combine into a single string.
		if ( ! empty( $sql_chunks['join'] ) ) {
			$sql['join'] = implode( ' ', array_unique( $sql_chunks['join'] ) );
		}

		// Generate a single WHERE clause with proper brackets and indentation.
		if ( ! empty( $sql_chunks['where'] ) ) {
			$sql['where'] = '( ' . "\n  " . $indent . implode( ' ' . "\n  " . $indent . $relation . ' ' . "\n  " . $indent, $sql_chunks['where'] ) . "\n" . $indent . ')';
		}

		return $sql;
	}

	/**
	 * Generates SQL JOIN and WHERE clauses for a first-order query clause.
	 *
	 * "First-order" means that it's an array with a 'key' or 'value'.
	 *
	 * @since 4.1.0
	 *
	 * @global wpdb $wpdb WordPress database abstraction object.
	 *
	 * @param array  $clause       Query clause (passed by reference).
	 * @param array  $parent_query Parent query array.
	 * @param string $clause_key   Optional. The array key used to name the clause in the original `$meta_query`
	 *                             parameters. If not provided, a key will be generated automatically.
	 *                             Default empty string.
	 * @return array {
	 *     Array containing JOIN and WHERE SQL clauses to append to a first-order query.
	 *
	 *     @type string[] $join  Array of SQL fragments to append to the main JOIN clause.
	 *     @type string[] $where Array of SQL fragments to append to the main WHERE clause.
	 * }
	 */
	public function get_sql_for_clause( &$clause, $parent_query, $clause_key = '' ) {
		global $wpdb;

		$sql_chunks = array(
			'where' => array(),
			'join'  => array(),
		);

		if ( isset( $clause['compare'] ) ) {
			$clause['compare'] = strtoupper( $clause['compare'] );
		} else {
			$clause['compare'] = isset( $clause['value'] ) && is_array( $clause['value'] ) ? 'IN' : '=';
		}

		$non_numeric_operators = array(
			'=',
			'!=',
			'LIKE',
			'NOT LIKE',
			'IN',
			'NOT IN',
			'EXISTS',
			'NOT EXISTS',
			'RLIKE',
			'REGEXP',
			'NOT REGEXP',
		);

		$numeric_operators = array(
			'>',
			'>=',
			'<',
			'<=',
			'BETWEEN',
			'NOT BETWEEN',
		);

		if ( ! in_array( $clause['compare'], $non_numeric_operators, true ) && ! in_array( $clause['compare'], $numeric_operators, true ) ) {
			$clause['compare'] = '=';
		}

		if ( isset( $clause['compare_key'] ) ) {
			$clause['compare_key'] = strtoupper( $clause['compare_key'] );
		} else {
			$clause['compare_key'] = isset( $clause['key'] ) && is_array( $clause['key'] ) ? 'IN' : '=';
		}

		if ( ! in_array( $clause['compare_key'], $non_numeric_operators, true ) ) {
			$clause['compare_key'] = '=';
		}

		$meta_compare     = $clause['compare'];
		$meta_compare_key = $clause['compare_key'];

		// First build the JOIN clause, if one is required.
		$join = '';

		// We prefer to avoid joins if possible. Look for an existing join compatible with this clause.
		$alias = $this->find_compatible_table_alias( $clause, $parent_query );
		if ( false === $alias ) {
			$i     = count( $this->table_aliases );
			$alias = $i ? 'mt' . $i : $this->meta_table;

			// JOIN clauses for NOT EXISTS have their own syntax.
			if ( 'NOT EXISTS' === $meta_compare ) {
				$join .= " LEFT JOIN $this->meta_table";
				$join .= $i ? " AS $alias" : '';

				if ( 'LIKE' === $meta_compare_key ) {
					$join .= $wpdb->prepare( " ON ( $this->primary_table.$this->primary_id_column = $alias.$this->meta_id_column AND $alias.meta_key LIKE %s )", '%' . $wpdb->esc_like( $clause['key'] ) . '%' );
				} else {
					$join .= $wpdb->prepare( " ON ( $this->primary_table.$this->primary_id_column = $alias.$this->meta_id_column AND $alias.meta_key = %s )", $clause['key'] );
				}

				// All other JOIN clauses.
			} else {
				$join .= " INNER JOIN $this->meta_table";
				$join .= $i ? " AS $alias" : '';
				$join .= " ON ( $this->primary_table.$this->primary_id_column = $alias.$this->meta_id_column )";
			}

			$this->table_aliases[] = $alias;
			$sql_chunks['join'][]  = $join;
		}

		// Save the alias to this clause, for future siblings to find.
		$clause['alias'] = $alias;

		// Determine the data type.
		$_meta_type     = isset( $clause['type'] ) ? $clause['type'] : '';
		$meta_type      = $this->get_cast_for_type( $_meta_type );
		$clause['cast'] = $meta_type;

		// Fallback for clause keys is the table alias. Key must be a string.
		if ( is_int( $clause_key ) || ! $clause_key ) {
			$clause_key = $clause['alias'];
		}

		// Ensure unique clause keys, so none are overwritten.
		$iterator        = 1;
		$clause_key_base = $clause_key;
		while ( isset( $this->clauses[ $clause_key ] ) ) {
			$clause_key = $clause_key_base . '-' . $iterator;
			++$iterator;
		}

		// Store the clause in our flat array.
		$this->clauses[ $clause_key ] =& $clause;

		// Next, build the WHERE clause.

		// meta_key.
		if ( array_key_exists( 'key', $clause ) ) {
			if ( 'NOT EXISTS' === $meta_compare ) {
				$sql_chunks['where'][] = $alias . '.' . $this->meta_id_column . ' IS NULL';
			} else {
				/**
				 * In joined clauses negative operators have to be nested into a
				 * NOT EXISTS clause and flipped, to avoid returning records with
				 * matching post IDs but different meta keys. Here we prepare the
				 * nested clause.
				 */
				if ( in_array( $meta_compare_key, array( '!=', 'NOT IN', 'NOT LIKE', 'NOT EXISTS', 'NOT REGEXP' ), true ) ) {
					// Negative clauses may be reused.
					$i                     = count( $this->table_aliases );
					$subquery_alias        = $i ? 'mt' . $i : $this->meta_table;
					$this->table_aliases[] = $subquery_alias;

					$meta_compare_string_start  = 'NOT EXISTS (';
					$meta_compare_string_start .= "SELECT 1 FROM $wpdb->postmeta $subquery_alias ";
					$meta_compare_string_start .= "WHERE $subquery_alias.post_ID = $alias.post_ID ";
					$meta_compare_string_end    = 'LIMIT 1';
					$meta_compare_string_end   .= ')';
				}

				switch ( $meta_compare_key ) {
					case '=':
					case 'EXISTS':
						$where = $wpdb->prepare( "$alias.meta_key = %s", trim( $clause['key'] ) ); // phpcs:ignore WordPress.DB.PreparedSQL.InterpolatedNotPrepared
						break;
					case 'LIKE':
						$meta_compare_value = '%' . $wpdb->esc_like( trim( $clause['key'] ) ) . '%';
						$where              = $wpdb->prepare( "$alias.meta_key LIKE %s", $meta_compare_value ); // phpcs:ignore WordPress.DB.PreparedSQL.InterpolatedNotPrepared
						break;
					case 'IN':
						$meta_compare_string = "$alias.meta_key IN (" . substr( str_repeat( ',%s', count( $clause['key'] ) ), 1 ) . ')';
						$where               = $wpdb->prepare( $meta_compare_string, $clause['key'] ); // phpcs:ignore WordPress.DB.PreparedSQL.NotPrepared
						break;
					case 'RLIKE':
					case 'REGEXP':
						$operator = $meta_compare_key;
						if ( isset( $clause['type_key'] ) && 'BINARY' === strtoupper( $clause['type_key'] ) ) {
							$cast     = 'BINARY';
							$meta_key = "CAST($alias.meta_key AS BINARY)";
						} else {
							$cast     = '';
							$meta_key = "$alias.meta_key";
						}
						$where = $wpdb->prepare( "$meta_key $operator $cast %s", trim( $clause['key'] ) ); // phpcs:ignore WordPress.DB.PreparedSQL.InterpolatedNotPrepared
						break;

					case '!=':
					case 'NOT EXISTS':
						$meta_compare_string = $meta_compare_string_start . "AND $subquery_alias.meta_key = %s " . $meta_compare_string_end;
						$where               = $wpdb->prepare( $meta_compare_string, $clause['key'] ); // phpcs:ignore WordPress.DB.PreparedSQL.NotPrepared
						break;
					case 'NOT LIKE':
						$meta_compare_string = $meta_compare_string_start . "AND $subquery_alias.meta_key LIKE %s " . $meta_compare_string_end;

						$meta_compare_value = '%' . $wpdb->esc_like( trim( $clause['key'] ) ) . '%';
						$where              = $wpdb->prepare( $meta_compare_string, $meta_compare_value ); // phpcs:ignore WordPress.DB.PreparedSQL.NotPrepared
						break;
					case 'NOT IN':
						$array_subclause     = '(' . substr( str_repeat( ',%s', count( $clause['key'] ) ), 1 ) . ') ';
						$meta_compare_string = $meta_compare_string_start . "AND $subquery_alias.meta_key IN " . $array_subclause . $meta_compare_string_end;
						$where               = $wpdb->prepare( $meta_compare_string, $clause['key'] ); // phpcs:ignore WordPress.DB.PreparedSQL.NotPrepared
						break;
					case 'NOT REGEXP':
						$operator = $meta_compare_key;
						if ( isset( $clause['type_key'] ) && 'BINARY' === strtoupper( $clause['type_key'] ) ) {
							$cast     = 'BINARY';
							$meta_key = "CAST($subquery_alias.meta_key AS BINARY)";
						} else {
							$cast     = '';
							$meta_key = "$subquery_alias.meta_key";
						}

						$meta_compare_string = $meta_compare_string_start . "AND $meta_key REGEXP $cast %s " . $meta_compare_string_end;
						$where               = $wpdb->prepare( $meta_compare_string, $clause['key'] ); // phpcs:ignore WordPress.DB.PreparedSQL.NotPrepared
						break;
				}

				$sql_chunks['where'][] = $where;
			}
		}

		// meta_value.
		if ( array_key_exists( 'value', $clause ) ) {
			$meta_value = $clause['value'];

			if ( in_array( $meta_compare, array( 'IN', 'NOT IN', 'BETWEEN', 'NOT BETWEEN' ), true ) ) {
				if ( ! is_array( $meta_value ) ) {
					$meta_value = preg_split( '/[,\s]+/', $meta_value );
				}
			} elseif ( is_string( $meta_value ) ) {
				$meta_value = trim( $meta_value );
			}

			switch ( $meta_compare ) {
				case 'IN':
				case 'NOT IN':
					$meta_compare_string = '(' . substr( str_repeat( ',%s', count( $meta_value ) ), 1 ) . ')';
					$where               = $wpdb->prepare( $meta_compare_string, $meta_value );
					break;

				case 'BETWEEN':
				case 'NOT BETWEEN':
					$where = $wpdb->prepare( '%s AND %s', $meta_value[0], $meta_value[1] );
					break;

				case 'LIKE':
				case 'NOT LIKE':
					$meta_value = '%' . $wpdb->esc_like( $meta_value ) . '%';
					$where      = $wpdb->prepare( '%s', $meta_value );
					break;

				// EXISTS with a value is interpreted as '='.
				case 'EXISTS':
					$meta_compare = '=';
					$where        = $wpdb->prepare( '%s', $meta_value );
					break;

				// 'value' is ignored for NOT EXISTS.
				case 'NOT EXISTS':
					$where = '';
					break;

				default:
					$where = $wpdb->prepare( '%s', $meta_value );
					break;

			}

			if ( $where ) {
				if ( 'CHAR' === $meta_type ) {
					$sql_chunks['where'][] = "$alias.meta_value {$meta_compare} {$where}";
				} else {
					$sql_chunks['where'][] = "CAST($alias.meta_value AS {$meta_type}) {$meta_compare} {$where}";
				}
			}
		}

		/*
		 * Multiple WHERE clauses (for meta_key and meta_value) should
		 * be joined in parentheses.
		 */
		if ( 1 < count( $sql_chunks['where'] ) ) {
			$sql_chunks['where'] = array( '( ' . implode( ' AND ', $sql_chunks['where'] ) . ' )' );
		}

		return $sql_chunks;
	}

	/**
	 * Gets a flattened list of sanitized meta clauses.
	 *
	 * This array should be used for clause lookup, as when the table alias and CAST type must be determined for
	 * a value of 'orderby' corresponding to a meta clause.
	 *
	 * @since 4.2.0
	 *
	 * @return array Meta clauses.
	 */
	public function get_clauses() {
		return $this->clauses;
	}

	/**
	 * Identifies an existing table alias that is compatible with the current
	 * query clause.
	 *
	 * We avoid unnecessary table joins by allowing each clause to look for
	 * an existing table alias that is compatible with the query that it
	 * needs to perform.
	 *
	 * An existing alias is compatible if (a) it is a sibling of `$clause`
	 * (ie, it's under the scope of the same relation), and (b) the combination
	 * of operator and relation between the clauses allows for a shared table join.
	 * In the case of WP_Meta_Query, this only applies to 'IN' clauses that are
	 * connected by the relation 'OR'.
	 *
	 * @since 4.1.0
	 *
	 * @param array $clause       Query clause.
	 * @param array $parent_query Parent query of $clause.
	 * @return string|false Table alias if found, otherwise false.
	 */
	protected function find_compatible_table_alias( $clause, $parent_query ) {
		$alias = false;

		foreach ( $parent_query as $sibling ) {
			// If the sibling has no alias yet, there's nothing to check.
			if ( empty( $sibling['alias'] ) ) {
				continue;
			}

			// We're only interested in siblings that are first-order clauses.
			if ( ! is_array( $sibling ) || ! $this->is_first_order_clause( $sibling ) ) {
				continue;
			}

			$compatible_compares = array();

			// Clauses connected by OR can share joins as long as they have "positive" operators.
			if ( 'OR' === $parent_query['relation'] ) {
				$compatible_compares = array( '=', 'IN', 'BETWEEN', 'LIKE', 'REGEXP', 'RLIKE', '>', '>=', '<', '<=' );

				// Clauses joined by AND with "negative" operators share a join only if they also share a key.
			} elseif ( isset( $sibling['key'] ) && isset( $clause['key'] ) && $sibling['key'] === $clause['key'] ) {
				$compatible_compares = array( '!=', 'NOT IN', 'NOT LIKE' );
			}

			$clause_compare  = strtoupper( $clause['compare'] );
			$sibling_compare = strtoupper( $sibling['compare'] );
			if ( in_array( $clause_compare, $compatible_compares, true ) && in_array( $sibling_compare, $compatible_compares, true ) ) {
				$alias = preg_replace( '/\W/', '_', $sibling['alias'] );
				break;
			}
		}

		/**
		 * Filters the table alias identified as compatible with the current clause.
		 *
		 * @since 4.1.0
		 *
		 * @param string|false  $alias        Table alias, or false if none was found.
		 * @param array         $clause       First-order query clause.
		 * @param array         $parent_query Parent of $clause.
		 * @param WP_Meta_Query $query        WP_Meta_Query object.
		 */
		return apply_filters( 'meta_query_find_compatible_table_alias', $alias, $clause, $parent_query, $this );
	}

	/**
	 * Checks whether the current query has any OR relations.
	 *
	 * In some cases, the presence of an OR relation somewhere in the query will require
	 * the use of a `DISTINCT` or `GROUP BY` keyword in the `SELECT` clause. The current
	 * method can be used in these cases to determine whether such a clause is necessary.
	 *
	 * @since 4.3.0
	 *
	 * @return bool True if the query contains any `OR` relations, otherwise false.
	 */
	public function has_or_relation() {
		return $this->has_or_relation;
	}
}

[et_pb_section fb_built=”1″ custom_padding_last_edited=”on|phone” admin_label=”Introduction” _builder_version=”4.16″ width_tablet=”” width_phone=”84%” width_last_edited=”on|phone” min_height=”1973.1px” custom_margin=”|||” custom_margin_tablet=”” custom_margin_phone=”|0px||0px|false|false” custom_margin_last_edited=”on|phone” custom_padding=”29px|0px|4px|0px||” custom_padding_tablet=”” custom_padding_phone=”” global_colors_info=”{}”][et_pb_row column_structure=”3_4,1_4″ use_custom_gutter=”on” gutter_width=”4″ custom_padding_last_edited=”on|phone” admin_label=”Intro & Content” _builder_version=”4.18.0″ min_height=”1883.1px” min_height_tablet=”” min_height_phone=”auto” min_height_last_edited=”on|phone” height_tablet=”” height_phone=”auto” height_last_edited=”on|phone” custom_margin_tablet=”” custom_margin_phone=”0px||-57px||false|false” custom_margin_last_edited=”on|phone” custom_padding=”1px|0px|0px|||” custom_padding_tablet=”” custom_padding_phone=”0px||0px||false|false” animation_style=”fade” global_colors_info=”{}”][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” header_2_font=”||||||||” header_2_text_color=”#4c4c4c” header_2_font_size=”22px” width=”123.8%” min_height=”123.5px” custom_margin=”6px|-70px|45px||false|false” custom_margin_tablet=”|0px|||false|false” custom_margin_phone=”|0px|||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”5px|0px|0px|||” custom_padding_tablet=”” custom_padding_phone=”” custom_padding_last_edited=”on|desktop” global_colors_info=”{}”]<\/p>\n

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As businesses increasingly rely on Software as a Service (SaaS) solutions for their operations, they are confronted with the inevitable reality of price increases. While these hikes can strain budgets and disrupt workflows, the emergence of AI-powered tools offers a glimmer of hope. In this article, we explore several innovative ways AI is helping organizations counter SaaS price increases.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/blockquote>\n

[\/et_pb_text][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” header_2_font=”||||||||” header_2_text_color=”#4c4c4c” header_2_font_size=”22px” width=”123.8%” custom_margin=”26px|-70px|||false|false” custom_margin_tablet=”|0px|||false|false” custom_margin_phone=”|0px|||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”5px|0px|9px|||” custom_padding_tablet=”” custom_padding_phone=”” custom_padding_last_edited=”on|desktop” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

How We Can Help You Save<\/h2>\n

[\/et_pb_text][et_pb_divider divider_weight=”2px” _builder_version=”4.18.0″ max_width=”60px” module_alignment=”left” height=”2px” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.18.0″ text_font=”Poppins|300|||||||” text_text_color=”#0a0a0a” text_letter_spacing=”1px” text_line_height=”2em” max_width_tablet=”” max_width_phone=”” max_width_last_edited=”on|phone” min_height=”124px” custom_margin=”|-150px|6px||false|false” custom_margin_tablet=”|0px|||false|false” custom_margin_phone=”|-52px||0px|false|false” custom_margin_last_edited=”on|phone” custom_padding=”|0px|0px||false|false” custom_padding_tablet=”” custom_padding_phone=”” custom_padding_last_edited=”on|phone” hover_enabled=”0″ inline_fonts=”Poppins,Alata,Aclonica” global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

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Several notable SaaS (Software as a Service) products have experienced significant price hikes over the years: Salesforce, GitHub, Zoom, and Zendesk for example.<\/span><\/p>\n

<\/span><\/p>\n

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• \n
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  • \n
    \n
    • We’ll build you your own and cut your software renewal fees.<\/li>\n
    • We analyze the software you use and how you use it<\/li>\n
    • We’ll write a proposal to cut out your vendors and replace it with software that you own.<\/li>\n
    • We can manage the hosting of your new software on public clouds such as Amazon AWS or Microsoft Azure.<\/li>\n
    • Based on typical clients, your replacement software will cost about 1-2 years of what you currently pay, but then cost only 10-20% to maintain than what you were paying before.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

      [\/et_pb_text][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” header_2_font=”||||||||” header_2_text_color=”#4c4c4c” header_2_font_size=”22px” width=”123.8%” custom_margin=”26px|-70px|14px||false|false” custom_margin_tablet=”|0px|||false|false” custom_margin_phone=”|0px|||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”5px|0px|9px|||” custom_padding_tablet=”” custom_padding_phone=”” custom_padding_last_edited=”on|desktop” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

      Benefits and Advantages<\/h2>\n

      [\/et_pb_text][et_pb_divider divider_weight=”2px” _builder_version=”4.18.0″ max_width=”60px” module_alignment=”left” height=”2px” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.18.0″ text_font=”Poppins|300|||||||” text_text_color=”#0a0a0a” text_letter_spacing=”1px” text_line_height=”2em” max_width_tablet=”” max_width_phone=”” max_width_last_edited=”on|phone” min_height=”124px” custom_margin=”|-150px|6px||false|false” custom_margin_tablet=”|0px|||false|false” custom_margin_phone=”|-52px||0px|false|false” custom_margin_last_edited=”on|phone” custom_padding=”|0px|0px||false|false” custom_padding_tablet=”” custom_padding_phone=”” custom_padding_last_edited=”on|phone” hover_enabled=”0″ inline_fonts=”Poppins,Alata,Aclonica” global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

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      Using AI to replace costly SaaS (Software as a Service) solutions can offer several benefits and advantages for organizations. Here are some compelling reasons to consider this approach:<\/p>\n

      Cost Savings:<\/strong> One of the primary reasons to replace costly SaaS solutions with AI-driven alternatives is cost savings. AI can automate tasks and processes that might require expensive software subscriptions. By building or integrating AI solutions, organizations can reduce their software expenses significantly.<\/p>\n

      Customization:<\/strong> AI solutions can be tailored to the specific needs of an organization. Unlike off-the-shelf SaaS products, which offer predefined features and functionalities, AI can be trained and customized to perform tasks and provide insights that align precisely with the organization’s requirements.<\/p>\n

      Scalability:<\/strong> AI solutions can often scale more efficiently than SaaS subscriptions. As your organization grows, you can expand your AI infrastructure without incurring linear increases in costs, unlike SaaS, where additional users or features can lead to higher subscription fees.<\/p>\n

      Data Security:<\/strong> AI solutions can be developed with a strong focus on data security and privacy. Organizations can have more control over their data, reducing the risk of data breaches or unauthorized access associated with third-party SaaS providers.<\/p>\n

      Integration:<\/strong> AI can seamlessly integrate with existing systems and workflows, allowing organizations to enhance their processes without disrupting their current operations. This integration can lead to increased efficiency and productivity.<\/p>\n

      Automation and Efficiency:<\/strong> AI can automate repetitive and time-consuming tasks, freeing up human resources for more strategic and value-added activities. This can result in increased efficiency and reduced labor costs.<\/p>\n

      Predictive Analytics:<\/strong> AI can provide valuable predictive insights that help organizations make data-driven decisions. This can be particularly beneficial in various industries, such as finance, healthcare, and marketing, where predictive analytics can lead to cost savings and revenue generation.<\/p>\n

      Long-term Cost Control:<\/strong> AI solutions typically involve upfront development costs but can provide long-term cost control as they do not rely on recurring subscription fees. This can be especially advantageous for organizations looking to manage their budgets over time.<\/p>\n

      Competitive Advantage:<\/strong> Organizations that successfully leverage AI to replace costly SaaS solutions may gain a competitive advantage. They can allocate resources strategically and invest in innovative solutions that differentiate them from competitors.<\/p>\n

      It’s important to note that while AI offers numerous advantages, its implementation can also come with challenges, including the need for skilled AI talent, data quality, and ethical considerations. The decision to replace SaaS with AI should be based on a thorough assessment of the organization’s specific needs, goals, and available resources.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

      [\/et_pb_text][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” header_2_font=”||||||||” header_2_text_color=”#4c4c4c” header_2_font_size=”22px” width=”123.8%” custom_margin=”26px|-70px|||false|false” custom_margin_tablet=”|0px|||false|false” custom_margin_phone=”|0px|||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”5px|0px|9px|||” custom_padding_tablet=”” custom_padding_phone=”” custom_padding_last_edited=”on|desktop” global_colors_info=”{}”]<\/p>\n

      Smart SaaS Usage Optimization<\/h2>\n

      [\/et_pb_text][et_pb_divider divider_weight=”2px” _builder_version=”4.18.0″ max_width=”60px” module_alignment=”left” height=”2px” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.18.0″ text_font=”Poppins|300|||||||” text_text_color=”#0a0a0a” text_letter_spacing=”1px” text_line_height=”2em” max_width_tablet=”” max_width_phone=”” max_width_last_edited=”on|phone” min_height=”141px” custom_margin=”|-150px|1px||false|false” custom_margin_tablet=”|0px|||false|false” custom_margin_phone=”|-52px||0px|false|false” custom_margin_last_edited=”on|phone” custom_padding=”|0px|17px||false|false” custom_padding_tablet=”” custom_padding_phone=”” custom_padding_last_edited=”on|phone” hover_enabled=”0″ inline_fonts=”Poppins,Alata,Aclonica” global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

      AI-driven tools for monitoring SaaS application usage and recommending cost-efficient alternatives or unused features are transforming how organizations manage their software expenses:<\/p>\n

      Usage Tracking: These tools continuously monitor how employees use SaaS applications, collecting data on feature utilization, frequency, and user engagement.<\/p>\n

      Recommendation Engine: AI algorithms analyze usage data and compare it to available features and pricing plans. They identify cost-efficient alternatives within the same SaaS ecosystem or suggest unused features that can replace or supplement existing subscriptions.<\/p>\n

      Examples of Companies Saving Money:<\/strong><\/p>\n

      Company A<\/span>: After implementing an AI-driven usage monitoring tool, Company A identified that a significant portion of their team rarely used advanced features in their CRM software. By downgrading to a more cost-effective plan tailored to their actual needs, they reduced annual expenses by 30%.<\/p>\n

      Company B<\/span>: This tech startup found that their cloud storage expenses were steadily increasing. AI analysis revealed that many users were storing duplicate files unnecessarily. The AI tool recommended deduplication and a smarter file management strategy, resulting in a 25% reduction in storage costs.<\/p>\n

      Company C<\/span>: Company C was using multiple project management tools across different teams, leading to fragmented workflows and increased expenses. AI-driven analysis suggested consolidating to a single, more feature-rich solution that reduced subscription costs by 40%.<\/p>\n

      These examples highlight how AI-powered tools can help organizations make data-driven decisions, optimize their SaaS subscriptions, and achieve significant cost savings while maintaining or even improving productivity.<\/p>\n

      [\/et_pb_text][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” header_2_font=”||||||||” header_2_text_color=”#4c4c4c” header_2_font_size=”22px” width=”123.8%” custom_margin=”26px|-70px|3px||false|false” custom_margin_tablet=”|0px|||false|false” custom_margin_phone=”|0px|||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”5px|0px|9px|||” custom_padding_tablet=”” custom_padding_phone=”” custom_padding_last_edited=”on|desktop” global_colors_info=”{}”]<\/p>\n

      SaaS Vendor Comparison and Alternatives<\/span><\/h2>\n

      [\/et_pb_text][et_pb_divider divider_weight=”2px” _builder_version=”4.18.0″ max_width=”60px” module_alignment=”left” height=”2px” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.18.0″ text_font=”Poppins|300|||||||” text_text_color=”#0a0a0a” text_letter_spacing=”1px” text_line_height=”2em” max_width_tablet=”” max_width_phone=”” max_width_last_edited=”on|phone” min_height=”143px” custom_margin=”|-150px|35px||false|false” custom_margin_tablet=”|0px|||false|false” custom_margin_phone=”|-52px||0px|false|false” custom_margin_last_edited=”on|phone” custom_padding=”|0px|0px||false|false” custom_padding_tablet=”” custom_padding_phone=”” custom_padding_last_edited=”on|phone” inline_fonts=”Poppins,Alata,Aclonica” global_colors_info=”{}”]<\/p>\n

      AI-driven platforms that compare SaaS vendors play a crucial role in simplifying the software selection process for businesses. These platforms utilize artificial intelligence and data analytics to:<\/p>\n

      Pricing Comparison<\/span>: They collect pricing data from various SaaS providers and present it in a unified, easy-to-compare format, allowing businesses to identify cost-effective options.<\/p>\n

      Feature Analysis<\/span>: AI algorithms analyze the features offered by different vendors and generate side-by-side comparisons, helping organizations find solutions that align with their specific needs.<\/p>\n

      User Reviews<\/span>: These platforms aggregate user reviews and sentiment analysis to provide insights into the user experience, reliability, and support quality of SaaS products.<\/p>\n

      Recommendations<\/span>: AI-driven platforms often offer tailored recommendations based on a company’s requirements and budget constraints, facilitating informed decision-making.<\/p>\n

      How Businesses Can Use These Platforms:<\/strong><\/p>\n

      Comprehensive Research<\/span>: Companies can use these platforms to gain a comprehensive understanding of the SaaS landscape, ensuring they make informed choices.<\/p>\n

      Cost-Efficiency<\/span>: By comparing pricing structures and available features, organizations can identify SaaS vendors that offer the best value for their investment.<\/p>\n

      User Satisfaction<\/span>: Analyzing user reviews and sentiment can help businesses gauge user satisfaction and potential issues with a particular vendor’s software.<\/p>\n

      Customization<\/span>: Businesses can tailor their search criteria to find SaaS solutions that precisely match their unique requirements, reducing the risk of overpaying for unnecessary features.<\/p>\n

      Time Savings<\/span>: These platforms streamline the research process, saving businesses valuable time that can be allocated to other critical tasks.<\/p>\n

      In a crowded SaaS market, AI-driven comparison platforms empower businesses to make well-informed decisions that align with their budget and needs, ultimately leading to more cost-effective and efficient software adoption.<\/p>\n

      [\/et_pb_text][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” header_2_font=”||||||||” header_2_text_color=”#4c4c4c” header_2_font_size=”22px” custom_margin=”26px|-122px|||false|false” custom_margin_tablet=”|0px|||false|false” custom_margin_phone=”|0px|||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”5px|0px|9px|||” custom_padding_tablet=”” custom_padding_phone=”” custom_padding_last_edited=”on|desktop” global_colors_info=”{}”]<\/p>\n

      Case Studies<\/h2>\n

      [\/et_pb_text][et_pb_divider divider_weight=”2px” _builder_version=”4.18.0″ max_width=”60px” module_alignment=”left” height=”2px” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.18.0″ text_font=”Poppins|300|||||||” text_text_color=”#0a0a0a” text_letter_spacing=”1px” text_line_height=”2em” max_width_tablet=”” max_width_phone=”” max_width_last_edited=”on|phone” min_height=”50px” custom_margin=”|-150px|44px||false|false” custom_margin_tablet=”|0px|||false|false” custom_margin_phone=”|-52px||0px|false|false” custom_margin_last_edited=”on|phone” custom_padding=”|0px|0px||false|false” custom_padding_tablet=”” custom_padding_phone=”” custom_padding_last_edited=”on|phone” inline_fonts=”Poppins,Alata,Aclonica” global_colors_info=”{}”]<\/p>\n

      Case Study 1: Tech Innovators Inc.<\/strong><\/p>\n

      Background:<\/em> Tech Innovators Inc. is a medium-sized technology company that heavily relies on SaaS applications for various aspects of their operations. They’ve noticed recurring price hikes across multiple software tools, which posed a significant challenge to their budget management.<\/p>\n

      AI Solution Implementation:<\/em> Tech Innovators Inc. decided to implement an AI-driven cost prediction and optimization platform. The AI algorithms analyzed historical pricing data, detected patterns in price increases, and predicted potential future hikes for each SaaS product they used.<\/p>\n

      Results:<\/em><\/p>\n

      Proactive Budget Adjustments: With AI-generated price increase predictions, the company proactively adjusted their budgets to accommodate expected hikes.<\/p>\n

      Negotiation Success: Armed with data-backed insights, Tech Innovators Inc. engaged in more informed negotiations with their SaaS providers. They managed to secure better pricing terms and, in some cases, lock in current rates for an extended period.<\/p>\n

      Optimized Subscriptions: The AI platform identified several underutilized features in existing subscriptions and recommended downgrades to lower-tier plans, saving the company 15% on their SaaS expenses.<\/p>\n

      Case Study 2: Retail Plus Ltd.<\/strong><\/p>\n

      Background:<\/em> Retail Plus Ltd. is a national retail chain with numerous locations. They use various SaaS applications for inventory management, sales tracking, and customer engagement. Rising SaaS costs were impacting their profitability.<\/p>\n

      AI Solution Implementation:<\/em> Retail Plus Ltd. adopted an AI-powered SaaS optimization tool that continuously monitored usage patterns across their stores. It recommended cost-effective alternatives and identified unused features within their existing subscriptions.<\/p>\n

      Results:<\/em><\/p>\n

      Cost Reduction: The AI tool helped Retail Plus Ltd. identify duplicate SaaS subscriptions across different store locations. By consolidating these subscriptions and renegotiating with providers, they reduced SaaS expenses by 20%.<\/p>\n

      Improved Efficiency: By pinpointing underutilized features, the company optimized their workflows, enhancing operational efficiency and customer service.<\/p>\n

      Vendor Negotiations: With data-driven insights into their SaaS usage, Retail Plus Ltd. entered into more productive negotiations with their vendors, securing discounts and improved support packages.<\/p>\n

      These case studies showcase how AI-powered solutions can empower companies to proactively manage SaaS costs, negotiate effectively, and optimize their software subscriptions, ultimately resulting in significant cost savings and improved operational efficiency.<\/p>\n

      [\/et_pb_text][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” header_2_font=”||||||||” header_2_text_color=”#4c4c4c” header_2_font_size=”22px” min_height=”37px” custom_margin=”26px|-122px|21px||false|false” custom_margin_tablet=”|0px|||false|false” custom_margin_phone=”|0px|||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”5px|0px|9px|||” custom_padding_tablet=”” custom_padding_phone=”” custom_padding_last_edited=”on|desktop” global_colors_info=”{}”]<\/p>\n

      Final Thoughts<\/h2>\n

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      In the ever-evolving landscape of Software as a Service (SaaS), where innovation and convenience often come with a price, AI-powered solutions are emerging as a formidable shield against the challenges of SaaS price hikes. As we’ve explored in this article, AI’s ability to analyze historical pricing data, optimize SaaS usage, and forecast future costs is transforming the way businesses approach software subscriptions.<\/p>\n

      No longer do organizations need to navigate the murky waters of SaaS pricing changes blindly. With AI-driven predictive models, companies can anticipate and plan for potential price increases, ensuring that their budgets remain on track and their financial stability intact. This proactive approach is not just a means of cost management; it’s a strategic advantage in a world where agility and adaptability are paramount.<\/p>\n

      AI also plays a pivotal role in optimizing SaaS usage. By monitoring application usage and recommending cost-efficient alternatives or unused features, businesses can trim unnecessary expenses while maintaining productivity. These AI-driven insights are more than just cost savings; they’re a pathway to efficiency and competitiveness.<\/p>\n

      Moreover, AI isn’t just a financial tool; it’s a negotiation partner. Armed with AI-driven insights, businesses can engage in informed discussions with SaaS providers, leveraging data-backed arguments to secure more favorable contracts. Negotiating from a position of strength is the cornerstone of smart cost management.<\/p>\n

      As we’ve seen from real-world examples, companies are reaping the rewards of AI-powered SaaS cost optimization. They are reducing expenses, reallocating resources, and making strategic decisions that position them for long-term success. In the face of unpredictable pricing landscapes, AI is the ally that empowers organizations to take control of their software costs.<\/p>\n

      In conclusion, the era of SaaS price hikes need not be a source of anxiety or frustration. Instead, it’s an opportunity for businesses to embrace AI-powered solutions that provide clarity, control, and cost-effectiveness. As AI continues to evolve, its role in shielding businesses against SaaS price hikes is destined to become even more indispensable, enabling organizations to thrive in a digital world where innovation meets fiscal responsibility.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

      [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.18.0″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

      As businesses increasingly rely on Software as a Service (SaaS) solutions for their operations, they are confronted with the inevitable reality of price increases. While these hikes can strain budgets and disrupt workflows, the emergence of AI-powered tools offers a glimmer of hope.<\/p>\n","protected":false},"author":3,"featured_media":7783,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[2316,567,59,122],"tags":[51,2421,126,123],"class_list":["post-7779","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-agents","category-artificial-intelligence","category-custom-software-development","category-saas-replacement","tag-ai","tag-ai-solutions","tag-saas","tag-saas-replacement"],"yoast_head":"